CN113962011A - Electric automobile braking system model and establishing method thereof - Google Patents

Abstract

The invention belongs to the technical field of electric automobiles, and discloses an electric automobile brake system model and an establishing method thereof, wherein the establishing method of the electric automobile brake system model comprises the following steps: establishing a simulation model of an electric automobile AEB; establishing interfaces of CarSim and Simulink software; building a braking system model based on Simulink; and verifying the simulation model. Simulation experiment analysis results show that the method can basically realize collision avoidance in an AEB test scene specified in C-NCAP, and the control strategy has great improvement on a classical algorithm on five indexes of speed reduction rate, maximum deceleration, deceleration change rate, minimum distance between two vehicles and early warning time, improves the safety of an automobile and the comfort of a driver, and can be well adapted to a road surface with a low road surface adhesion coefficient.

Description

Electric automobile braking system model and establishing method thereof

Technical Field

The invention belongs to the technical field of electric automobiles, and particularly relates to an electric automobile braking system model and an establishing method thereof.

Background

In order to effectively reduce the occurrence of traffic accidents and improve driving safety, many solutions have been adopted. The schemes can be divided into two categories according to the time before and after the accident occurs: passive security class and active security class. Passive safety schemes are mainly aimed at acting after an accident occurs to reduce the degree of injury of traffic accidents to people in the vehicle, such as safety airbags, safety belts, toughened glass and the like; the active safety scheme is to work before a traffic accident occurs to avoid the traffic accident, such as a reverse radar/image, a forward early warning system (FCW), an automatic emergency braking system (AEB), an adaptive cruise system (ACC), and the like. By taking the essence of the safety scheme, the passive safety scheme can not avoid accidents, but only can reduce the injury degree of people in the vehicle to a certain extent after the accidents occur, and the active safety measures can avoid the traffic accidents to a certain extent.

AEB is an active safety technique for avoiding or mitigating collisions through automatic emergency braking, and is a function in Advanced Driving Assistance Systems (ADAS). The AEB system acquires the motion information of a front target through a radar sensor, a camera sensor and other forward sensing sensors, and predicts the collision danger by combining the motion state of the vehicle. When the danger degree is low, the system can give an early warning to the driver, and the early warning comprises the modes of sound, images and the like; partial braking, namely braking with smaller braking strength, can be adopted when the danger degree is higher, and if the driver still can not react to carry out effective operation, the system can force automatic emergency braking to avoid the occurrence of collision as far as possible. Studies have shown an overall 38% reduction in rear-end collisions for AEB-equipped vehicles. The control strategy of the AEB is one of hot spots of research of the AEB system and is also the core content of automatic emergency braking. The improvement of the safety and reliability of the control strategy is a permanent topic of the AEB control strategy and is a key point for enterprises and students to deeply discuss. The driving and braking parts of the electric automobile are greatly different from those of the traditional diesel locomotive, and the research on the AEB system of the electric automobile still has important research value.

AEB system development status: before the 21 st century, foreign scholars had great development on automobile brake technology, and in the later period of the 21 st century, multinational scientists developed vehicle-mounted millimeter wave radars and developed anti-collision systems, and although the development level of electronic devices is still relatively limited, the development of AEB systems still lays a foundation.

Since the 21 st century, the rapid development of computer and electronic technology has made a real breakthrough in the automobile AEB system. The Pre-safe Pre-collision safety system is arranged on a 2003 Benz S-level vehicle at the earliest, and when the system detects that a driver is performing danger avoiding operation by monitoring data such as a steering wheel angle sensor, a transverse acceleration sensor, a brake pedal torque sensor and the like of the vehicle, the Pre-safe can automatically close a vehicle window in advance, tighten a safety belt and adjust a seat angle to reduce loss caused by collision. Later, Pre-safe adds a brake assist device to the original system that automatically applies a maximum deceleration of 0.4G when the system detects an impending collision. At present, the current running main stream vehicle adopts the latest preventive safety system enhanced version (PRE-SAFE PLUS), a protection mechanism for preventing rear-end collision early warning and secondary rear-end collision is added, braking is adopted when rear-end collision cannot be avoided, secondary rear-end collision accidents can be prevented, and the safety performance is more perfect.

Public companies have developed pre-crash safety systems (Front Assist) and are equipped with under-the-flag multiple models of vehicles. In the popular new CC vehicle user manual, a car with the speed ranging from 30km/h to 150km/h collects traffic information in the range of 150w in front, and when a danger is met, the system responds to the dangerous condition in three stages: the first stage is that the system judges that the vehicle and the front vehicle are likely to collide, and at the moment, sound and light alarm is immediately sent out to remind a driver to prepare for braking and prepare for emergency braking of the car; in the second stage, if the driver does not react to the sent early warning, the system triggers short rapid braking through an active braking intervention function to warn the driver that the car possibly collides with the front car; in a third phase, if the driver has not yet reacted to the emergency warning, the system may automatically brake the vehicle with a gradually increasing braking force to reduce the vehicle speed in the collision.

The wolwo motor company, sweden, is also a research which is constantly dedicated to the technical sector of automobile safety. In 2008, volvo automobile company successfully introduced its first generation City Safety system, which utilized a laser-induced digital camera on the front windshield to detect vehicles and obstacles within 10 meters in front of the vehicle, and then calculated the Safety braking distance and the required braking torque at a speed of up to 50 times per second based on the speed of the vehicle and the relative speeds of the front and rear vehicles. The system can avoid collision when the speed of the vehicle is below 30km/h and the relative speed is lower than 15 km/h; when the relative speed is higher than 30km/h, the collision damage is reduced. The second generation city safety system introduced in 2013 is started when the speed of the vehicle is below 50km/h, and collision can be avoided when the speed difference is below 50 km/h. The City Safety of 2015 enhanced version is not limited by the speed of the vehicle, and the Walkwo XC90T8 honor version introduces a highly automated driving technology (Pilot Assist II), newly adds a cruise function with automatic steering, and can realize automatic driving on a road with clear road line at the speed of no more than 130 km/h.

The research of the U.S. AEB system has started late relative to europe, but currently the automotive AEB technology in the united states is world-leading due to the great scientific investment made by the U.S. government. The most representative product is the unmanned automobile of google, and the condition around the automobile is sensed through a plurality of radars and cameras arranged on the automobile, and the core system of the unmanned automobile comprises an AEB emergency braking system. Its unmanned automobile has accumulated a trip of over 300 ten thousand miles since its ascent in 2012, and is a creditable lead for the unmanned industry.

The Euro-NCAP of the european union issued relevant regulations on AEB system testing in 2014 and updated in 2018. The Euro-NCAP will evaluate the automatic braking function and the front collision warning function in three different driving scenarios: the method comprises three items of vehicles running towards a static vehicle, vehicles running at a slow speed close to the front and vehicles in front for emergency braking. In addition, regulations on the testing of AEB systems have been developed in the united states, australia and japan in succession, which have contributed to the popularity and development of AEB to a large extent.

AEB has developed relatively late domestically, but many enterprises and scholars are in close pursuit of overseas footfalls. The first domestic mass-production vehicle with an automatic tight braking system is a Borui GC9 launched in a bird nest in 2014 and 12 months, and a sensor carried by the Borui GC is a Bosch radar, so that the self-adaptive cruise and automatic emergency stop braking of the vehicle can be realized. Shanghai in 2014 shows an autonomous driving demonstration vehicle env20 developed in SAE of China, and a scanning laser radar carried by the autonomous driving demonstration vehicle env20 can comprehensively detect obstacles around the vehicle and automatically realize a braking and stopping function in emergency. At present, most automobile enterprises in China have recognized the importance of the automatic emergency braking system, so that the research of the AEB technology of automobiles in China will be in the stage of rapid development.

The technology accumulation of scientific research institutions such as Qinghua university and the like and the industrial advantages of Tianjin City are relied on by Tianjin Qingzhi technology Limited, the test on the standard working condition of the AEB system of the independently developed commercial vehicle is carried out in the national passenger car quality supervision and detection center in 2016 and 12 months, and the result shows that the AEB system meets the requirements of various detection indexes of ECE regulations.

In 12 months in 2017, a representative team of Suzhou Anzhi automobile parts and components wins out with full score scores in all AEB tests of 2017 China intelligent automobile race, and the development strength of independent brands in the field of AEB is highlighted. The ADAS system of the intelligent team has already finished function integration and performance adjustment with domestic autonomous brand vehicle models, and finished durable adaptive road test of 10 kilometers of complex traffic road conditions, successfully passed the most rigorous performance evaluation of the whole vehicle level of the Chinese automobile technical research center, and became an autonomous ADAS driving auxiliary product with the domestic unique performance completely reaching the highest five-star evaluation of Euro-NCAP.

In addition, domestic autonomous brand whole car factories such as automobile and Jili car also develop autonomous research and development of ADAS in seconds. The AEB system is an important component of ADAS and is a focus of attention of various enterprises.

In the evaluation scheme of the C-NCAP (Chinese New vehicle evaluation procedure) in 2018, the evaluation of an active safety part is added in due time aiming at the characteristic of high occurrence of the pedestrian accident in China, and the automatic emergency braking system test and the pedestrian protection test are performed on the vehicle provided with the AEB system and are respectively graded.

Through the analysis, the AEB function is relatively mature at present, but the problems of insufficient recognition precision, high collision avoidance rate and the like exist. In addition, although a certain amount of effort has been made in the research of the AEB technology in China, the research on the AEB in China needs to be further advanced compared with that in foreign countries.

The current research situation of the AEB control algorithm at home and abroad is as follows: the AEB system controller can judge the driving safety state of the vehicle in real time through safety state judgment logic according to the state of the vehicle and the information of the target vehicle, and give corresponding early warning and braking instructions. The control algorithm of the AEB is an important part in the AEB control strategy and directly determines whether the system can judge the front collision danger. The control algorithms used in the AEB for judging the longitudinal driving risk at present are mainly classified into four categories: the algorithm comprises a collision time algorithm, a safe distance algorithm, a minimum deceleration algorithm for avoiding collision, a driver subjective feeling algorithm and the like, wherein the collision time algorithm and the safe distance algorithm are widely applied. The collision time algorithm is an algorithm for judging danger by calculating the time of distance collision and is based on the reaction of a driver; the safe distance algorithm judges the danger by the relative distance between two vehicles and is based on the time distance between vehicles.

Current state of the art collision time algorithm: time-to-collision (TTC) is defined as the Time required for two vehicles to travel with the current vehicle speed until a collision occurs, starting from the current Time. The security algorithm in the FCW & AEB function of mobileiye, well known as the TTC algorithm is used. When the TTC is less than a certain threshold and the driver does not react appropriately in time, the vehicle may take automatic emergency braking. The threshold value is typically related to the reaction time of the driver and the time required for the braking to start to a constant braking force. The TTC is calculated as follows:

wherein d represents the actual distance between the front and rear vehicles, v_relIndicating the relative speed of the front and rear vehicles.

The Uzolong uses a first-order TTC algorithm, sets three different TTC thresholds aiming at three different speed intervals of low speed, medium speed and high speed, formulates three different expected decelerations, carries out simulation on CarSim and Simulink software, and verifies that the algorithm can fully pass AEB test in C-NCAP.

MeixinZ et al used the TTC algorithm in studying the effect of the forward collision warning system on the following behavior of the vehicle, and when TTC was less than 2.7s, the system would sound a series of high pitched sounds and display red, flashing car icons to the driver.

Yang is a dynamic algorithm established by the people of the same class with a certain E-class SUV vehicle as a research object, a risk assessment algorithm based on the TTC collision time theory is established in the real pedestrian test scene at home and abroad, and the simulation verification is carried out on the control strategy through the combined simulation of Matlab and CarSim software. The TTC risk assessment algorithm correctly sends out pedestrian collision early warning, and no false alarm or missing alarm occurs.

And in the driving process, the value of the calculated TTC is compared with a threshold value, if the TTC is smaller than the threshold value, the risk of collision is indicated, and the automobile can automatically brake. However, the TTC is not suitable for some special conditions, for example, when the relative vehicle speed of the front and rear vehicles approaches zero, the TTC tends to be infinite, and obviously, the criterion of the TTC fails.

To remedy the deficiencies of classical TTC, xujie et al propose a method that considers the relative acceleration a of the host vehicle and the most dangerous target_relThe second order TTC formula of (a) is proposed to cope with the conditions of TTC failure:

in the formula, v_relRepresenting the relative speed of the front and rear vehicles, x representing the actual distance of the front and rear vehicles, a_relRepresenting the relative acceleration of the front and rear vehicles. Usually, the preceding vehicle is stationary orWhen the vehicle runs at a constant speed, a 1-order TTC calculation formula is adopted; when the front vehicle brakes, a 2-order TTC calculation formula is adopted.

Zhang et al uses the time span between workshops to make up for the TTC deficiency. The inter-vehicle time distance thw (time highway) represents the time required for the host vehicle to reach the current position of the preceding vehicle at the current vehicle speed. The calculation formula of the time interval between the vehicles is as follows:

wherein x represents the actual distance between the front and rear vehicles, v_egoThe vehicle speed of the host vehicle is indicated. When x is the target deceleration a adopted by the vehicle_aimWhen the vehicle decelerates to the distance when the vehicle stops, x satisfies:

substituting the two formulas to obtain a workshop time interval threshold value:

by comparing the relationship between THW and its threshold, a decision is made whether to apply braking to the vehicle.

In addition, SchwarzC establishes a two-dimensional TTC algorithm and uses a geometric method to estimate the use conditions of the formulas of the two-dimensional TTC, and the method has higher accuracy under complex traffic environment.

Since the denominator of the TTC expression is velocity or acceleration, these values are likely to be zero resulting in infinite TTC, and to radically solve this problem, Walker et al studied and used TTC as the inverse of time to collision in 2012^-1As an evaluation index adapted to the characteristics of a driver, the method effectively solves the problem that the TTC is possibly infinite to cause rule failure. TTC corresponding to different alarm levels is determined by combining statistical data of foreign driver algorithm^-1A desired range. His team later 2014Using the margin of collision avoidance time T_bufferThe method is characterized in that a graded braking algorithm suitable for different driver characteristics is designed according to the index, and the algorithm substantially considers the acceleration of front and rear vehicles on the basis of TTC so that the algorithm is more suitable for the change of working conditions.

Many scholars in China use the concept of the TTC reciprocal algorithm. Based on two main AEB control algorithms (a collision time inverse algorithm and a safe distance algorithm) at present, Leiwei et al carries out joint simulation through three kinds of software of PreScan, Carsim and MATLAB, analyzes and researches the braking effect of the AEB control algorithms under various working conditions and the relation between the vehicle speed and the vehicle distance. The result shows that when the vehicle speed is lower than 60km/h, the inverse collision time algorithm in the text has higher braking safety and riding comfort. Li Lin et al proposed an autonomous emergency braking system collision avoidance strategy in 2015 considering driver braking behavior, analyzed driver's emergency braking behavior in real traffic conditions at first, and based on TTC^-1The risk estimation algorithm is constructed by the algorithm; and then a control strategy for early warning two-stage braking in two stages is developed. In addition, the brake behavior of the driver is also considered in the TTC algorithm by the lanfeng chong et al, and a team of the driver analyzes the brake deceleration degree of the brake system according to the emergency brake data of the driver in deep investigation of the automobile rear-end collision accident and determines the threshold value of the pre-collision time under the condition of considering the riding comfort of the driver. In addition, for the problem that the road adhesion coefficient can affect the braking distance, Han and the like use a combined-slip tire model to estimate the road adhesion coefficient, add the road adhesion coefficient into a TTC algorithm, and adaptively adjust the threshold value of TTC, so that the AEB system can have ideal braking effect on different roads.

The safe distance algorithm researches the current situation: the core idea of the safe distance algorithm is to judge whether to perform braking operation or not by calculating the distance information of the vehicle and the most dangerous target vehicle. The following are several classical safe distance algorithms.

(1) The basic formula of Mazda's safe distance algorithm is as follows:

wherein d represents a safety distance, v₁Indicates the vehicle speed, v₂Representing the most dangerous target vehicle speed ahead, a₁Indicating the braking deceleration of the vehicle, a₂Representing the most dangerous target deceleration, t₁Representing the driver reaction delay time, t₂Representing the brake system delay time, d₀Indicating a minimum stopping distance.

I.e., the minimum distance the two vehicles are allowed to be apart after parking, and the braking system is activated when the distance between the vehicle and the most dangerous object is less than the safe distance.

(2) The safe distance algorithm proposed by Honda:

the Honda company provides an early warning safety distance and a braking safety distance on the basis of test data. The early warning distance threshold formula is as follows:

d_w＝2.2v_rel+6.2

in the formula, v_relIndicating the relative speed of the front and rear vehicles.

The braking distance threshold value calculation formula is as follows:

in the formula (d)_brIndicating the braking distance, d_wIndicating the warning distance, v_relRepresenting relative speed of front and rear vehicles, v₁Representing the speed of the vehicle, v₂Representing the most dangerous target vehicle speed ahead, a₁Representing the maximum braking deceleration of the vehicle, a₂Representing the most dangerous target maximum deceleration.

Unlike the Mazda algorithm, the system does not make a decision by direct comparison of the distance to a threshold, but rather defines a risk coefficient epsilon:

in the formula (d)_wIndicating an early warning distance，d_brIndicating the stopping distance and d the actual distance. When the risk coefficient epsilon is more than 1, the vehicle is in a safe state; when the epsilon is more than 0 and less than 1, the vehicle gives an alarm, and the alarm level is higher along with the smaller epsilon value; when the danger coefficient epsilon is less than 0, the active brake is opened immediately.

(3) Toyota's safe distance algorithm:

the Toyota company safety distance algorithm is an algorithm provided from the perspective of subjective feeling of driving, when a vehicle runs, a driver firstly predicts the running state of the vehicle, obtains the distance between the two vehicles, compares the distance with the psychological period safety distance, and if the distance is smaller than the psychological expected safety distance of the driver, the system starts automatic early warning or automatic braking.

Considering the psychological expectation safe distance, and based on different motion states of the front vehicle, the safe distance algorithm is divided into two safe distance algorithms of 'the front vehicle is static, uniform or accelerated' and 'the front vehicle is braked emergently'.

When the front vehicle is at rest (a)_f＝0)

Wherein t is_FEstimated time for the driver, v_relIs the relative velocity, a_fIs the front vehicle acceleration, a_rIs the acceleration of the bicycle, v_r，v_fRespectively, the speed of the bicycle and the speed of the front vehicle, d_limIs the driver psychologically expects a safe distance.

When the front vehicle is at a constant speed or accelerating (a)_fNot less than 0), which is relatively safe and often pressed against a for computational convenience_fProcess 0.

When the front vehicle is emergently braked (a)_f< 0), there are:

in addition to the classical algorithms proposed by these enterprise companies, many scholars have improved these safe distance algorithms to improve their security and handling stability.

The Xushijiang of Nanjing aerospace university improves the safe distance algorithm, and the safe distance algorithm built in the method is divided into two situations in detail under the condition that the deceleration of a front vehicle is greater than or equal to zero, and can effectively improve the active safety of an automobile and the operation stability of the automobile when the relative speed is small and the relative speed is large.

In addition, many scholars add other factors to the safe distance algorithm, making the algorithm more reliable. Since the safe distance algorithm ignores the reaction of the driver compared with the collision time algorithm, many studies at home and abroad improve the reaction and the operation behavior of the driver.

The reaction of the driver is considered in the Liying brother safety distance algorithm. And (4) looking up relevant data due to different reaction time of different drivers to the reminding signal, obtaining a considerable number of samples, and performing regression processing to finally obtain a calculation formula. And establishing an automatic emergency braking algorithm of the vehicle in Simulink by utilizing a PID control method, and carrying out combined simulation with a vehicle complete algorithm. The rationality of the designed calculation method and the automatic emergency braking algorithm is verified through simulation, and the existing AEB system is optimized to a certain extent.

In order to obtain the reaction time of the driver more accurately, the Sun Ning of Jilin university carries out fuzzy reasoning on the reaction time of the driver, substitutes the reaction time into the Honda safe distance algorithm for braking, and effectively solves the problem that the Honda model cannot realize collision avoidance under a high-speed working condition after verification. The safety distance obtained by the improved Honda algorithm is close to the distance just avoiding collision, and a sufficient distance is reserved for the parking distance.

In addition to the reaction time of the driver, many people at home and abroad study the state of the driver during the braking process. Jiangshu university Jiangshun et al establishes an automatic emergency braking control strategy based on driving state recognition, introduces an impact degree to analyze the driving state aiming at the problem that an AEB system does not consider the driving state of a driver, corrects the minimum safe distance in a safe distance algorithm, and adopts CarSim/Simulink to analyze the AEB system, and the result shows that the algorithm has advantages in coordinating the critical safe distance and better accords with the expectation of the driver. Denis N et al established a safe distance algorithm based on a braking process on a PHEV, and proposed a driving pattern recognition scheme, which is adapted to real-time driving conditions, and combines the state of the automobile in the driving and automatic braking processes, so that the energy consumption is greatly reduced while considering the safety.

In addition, in order to enable vehicles carrying the AEB system to adapt to different pavements, a normal group and the like add factors of pavement adhesion coefficients on the basis of a safe distance algorithm, select a proper vehicle safe distance algorithm, add consideration to factors of pavement adhesion coefficients, slope angles and the like on the basis of the safe distance algorithm, provide an improved safe distance algorithm, obtain a distance and relative speed change curve of front and rear vehicles under the driving state of the vehicles on 4 typical pavements through combined simulation of CarSim and Matlab/Simulink, and verify the reliability of the algorithm.

Generally speaking, students at home and abroad mostly study on the response of a driver, the operation mode of the driver and the state of the driver aiming at a safety distance algorithm, and study on the optimization aspects of braking stability, braking behavior of the driver and safety aiming at a collision time algorithm. The reason is that the safe distance algorithm is inferior to the TTC algorithm in the driver experience because the time concept is not involved, and much research is done on the driver; the TTC algorithm does not relate to a distance concept, and is inferior to the safe distance algorithm in safety, so that deeper research is carried out on safety to make up for the deficiency of the algorithm. In addition, the learners also take environmental factors (such as road adhesion coefficient and slope angle) into consideration in the algorithm to enable the algorithm to meet the requirements of multiple working conditions.

The current trend towards both algorithms is still security, followed by enhanced comfort. The safety mainly refers to whether the collision is completely avoided or not, and if the collision occurs, the smaller the relative speed in the collision is, the higher the safety is. Comfort is mainly three points, the first is the degree of impact, i.e. the rate of change of deceleration; secondly, whether the minimum inter-vehicle distance between two workshops is too early or too late; and thirdly, whether the driver is adaptive to the moment of the early warning signal or not and whether the driver is in a false alarm state or not. In addition, whether the AEB control strategy can achieve ideal effects in various complex working conditions is still an important subject of the AEB control algorithm.

Through the above analysis, the problems and defects of the prior art are as follows: the AEB function is relatively mature at present, but the problems of insufficient identification precision, high collision avoidance rate and the like still exist; although some results have been achieved in the research of AEB technology in China, the research on AEB in China needs to be further advanced compared with abroad. The braking prediction model in the AEB braking system model is not accurate enough.

The significance of solving the problems and the defects is as follows: the method has important significance for calculating the braking distance, evaluating the braking effect and controlling the AEB effect.

Disclosure of Invention

Aiming at the problems in the prior art, the invention provides an electric automobile braking system model and an establishing method thereof.

The invention is realized in such a way that the method for establishing the electric automobile brake system model comprises the following steps:

step one, establishing a simulation model of an electric automobile AEB;

step two, establishing interfaces of CarSim and Simulink software;

step three, building a braking system model based on Simulink;

and step four, verifying the simulation model.

Further, in the first step, the establishing of the simulation model of the electric vehicle AEB includes:

an AEB simulation model is built in the CarSim, and the AEB simulation model comprises a whole vehicle model, a perception sensor model and a road model.

(1) Whole vehicle model

Establishing a whole vehicle model, and inputting actual whole vehicle parameters; establishing two whole vehicle models, wherein one is an electric vehicle carrying an AEB function; the other is a lock target vehicle of the front vehicle, i.e., the electric vehicle.

For the whole vehicle model, for the electric vehicle, a whole vehicle model is built in the CarSim by using the vehicle sample parameters.

Inputting the vehicle parameters into a vehicle model of CarSim, and establishing the vehicle model; the whole vehicle model comprises the whole vehicle mass, the distance from a front shaft to a rear shaft to a center of mass, a wheel base, the center of mass height and the tire rotational inertia parameters; wherein the mass of the whole vehicle is modified along with the actual working condition.

The speed and the acceleration are considered during modeling, and other structural parameters do not make special requirements. The C-Class model provided by CarSim was used, containing settings for vehicle structural parameters, body appearance, and tire parameters.

(2) Perception sensor model

The radar ranging sensor is used for obtaining a main sensor of the relative distance, the relative speed and the acceleration of the vehicle from the front vehicle, and the camera is used for identifying a front object and assisting the radar in ranging; the radar and the camera are arranged for observing the running state of the vehicle in the forward collision early warning system, and the type selection and the arrangement of the millimeter wave radar and the camera are carried out according to the AEB system architecture. And according to the parameters of the radar and the camera, building a model of the millimeter wave radar and the camera in the CarSim. For the arrangement of the sensor, according to design requirements, the millimeter wave radar is hidden in the center of the front bumper, and the camera is arranged behind an interior rearview mirror in the vehicle.

(3) Road model

The road model is established by setting the road gradient, the road adhesion coefficient, the rolling resistance coefficient and the environmental parameters, and the model can be adjusted differently according to different working conditions. According to the verification conditions of the safe distance model, the road condition is specified to be straight-line driving, the front vehicle and the rear vehicle are positioned on the same lane, wherein the front vehicle is a target vehicle, and the rear vehicle is a test vehicle loaded with the designed AEB system.

Further, in step two, the interface establishment of the CarSim and Simulink software includes:

in the process of carrying out the joint simulation, the CarSim is used for providing data such as an automobile dynamic model, an event, road surface parameters and the like; the establishment and optimization of the safe distance algorithm are completed in Simulink, so the establishment of an interface between two pieces of software must be completed.

Outputting the parameters of the automobile, the road surface model and the front and rear automobiles built in the CarSim into the Simulink by using a special Simulink interface in the CarSim and a Sendto Simulink instruction; and constructing information of the front vehicle in the CarSim, and determining output parameters of the front target vehicle and the output parameters of the vehicle. The input parameter of the vehicle is the brake pressure IMP _ PBK of 4 cylinders, and the brake pressure is controlled through a brake force distribution strategy; the output parameters are the control pressure Pbk _ Con of the vehicle, the center speed Vx _ SM of the vehicle, the acceleration Ax of the vehicle, the relative distance Dis 1_1 with the front vehicle and the relative speed SpdS1_1 with the front vehicle. The motion state of the front vehicle is calculated by measuring the relative distance and the relative speed measured by the radar and measuring the speed and the acceleration of the vehicle, and then the motion state is input into a safe distance algorithm.

Further, in step three, the establishment of the braking system model based on Simulink includes:

building a braking system model for simulating a braking coordination time and a braking force rising curve in a braking process; the brake system comprises an I-Booster driving structure, a brake pedal, a brake pipeline, an ESP/electromagnetic valve, a brake wheel cylinder and a power supply.

The structure of the driving device of the brake system consists of a motor-control unit, a brake master cylinder, a deep drawing steel plate shell and an interface. The brake pipeline is a hydraulic pipeline and transmits braking force by taking liquid as a medium. The ESP is used for adjusting the braking force of the brake wheel cylinder by controlling the pressure reducing valve and the pressure increasing valve after receiving the superior signal.

Three important factors influencing braking force and braking coordination time are selected to establish a hydraulic braking model: firstly, a hydraulic pipeline; a second electromagnetic valve; and thirdly, braking the wheel cylinder.

Further, in step three, the establishment of the braking system model based on Simulink further includes:

(1) hydraulic pipeline model

The dynamic characteristic of the brake oil pressure of the hydraulic pipeline is simulated by establishing a hydraulic pipeline model, and a first-order inertia link is adopted for description, as shown in a formula (3):

wherein P(s) represents the actual hydraulic brake fluid pressure, MPa; p₀(s) represents a target oil pressure of the brake, MPa; tau is a constant used for reflecting the dynamic characteristic of the brake and is obtained through a hydraulic pipeline test.

(2) Electromagnetic valve model

The switching time of the solenoid valve directly affects the response time of the brake system. Since the modeling of the hydraulic brake system only considers the braking force and the braking coordination time, only the time delay characteristic of the electromagnetic valve model is considered when the electromagnetic valve model is established. The method is characterized in that related tests are carried out on the switching time of the electromagnetic valve under the working conditions of load and no load, and the switching time is usually 1ms to 10 ms. Because the time is short and the difference of the switching time between different electromagnetic valves is small, simulation research is performed corresponding to corresponding time according to the number and the state of the electromagnetic valves. The system comprises two electromagnetic valves, a pressure increasing valve and a pressure reducing valve, and the total delay time of the electromagnetic valves is determined to be 10 ms.

(3) Brake wheel cylinder model

In the braking process, the pressure of the hydraulic pipeline presses the piston of the wheel cylinder, and the piston pushes the brake block to tightly press the brake disc to stop the wheel from running. The brake model can be simplified into a piston dynamics model, and the mechanical characteristics of the pressure input of the brake wheel cylinder to the brake torque output are simulated.

Through the analysis of the stress in the process of the piston movement, a mechanical relation shown as an equation (4) can be obtained according to Newton's second law:

in the formula, P represents a wheel cylinder input pressure, MPa; a represents a piston cross-sectional area, m²(ii) a m represents the moving mass equivalent to the piston of the wheel cylinder, kg; k_pRepresents the brake stiffness, N/m; c_pRepresenting a damping coefficient; f₀Represents the dry friction of the system, N; x_pRepresenting the wheel cylinder piston displacement, m.

At this time, the positive pressure of the piston acting on the brake disc is:

by applying laplace transform to equation (4), we can obtain:

in engineering practice, the piston is stressed in balance because the piston and the brake block are basically pressed on the brake disc all the time, i.e. the piston is stressed in balance

The effect of dry friction is small and can be neglected, and the following relation is given:

from equations (5), (6), (7) and (8), the positive pressure acting on the brake disc can be:

the braking torque at the wheels can be expressed as:

in the formula, r₁Represents the effective friction radius, m; η represents the brake effectiveness factor.

The brake model can be simplified into a second-order inertia element as described by equation (9) for simulation. The deviation of the braking force from the target braking force can be gradually reduced by using a PID control method for the actual braking force.

And substituting parameters according to the analysis of the model, establishing a simulation model of the brake system based on Simulink, wherein the simulation model consists of electromagnetic valve time delay, PID control, a hydraulic pipeline first-order system and a brake wheel cylinder second-order system.

Further, in step four, the verifying of the simulation model includes:

because the system uses the simplified model, the structure and the characteristics in the system do not need to be considered, and the influence on the braking distance and the braking coordination time is only considered, whether the simulated braking effect is consistent with the actual vehicle braking effect is only verified, and the braking time and the braking distance are real and reliable when the automatic emergency braking is triggered in the simulation. The effect verification scheme based on the emergency braking in Simulink and CarSim combined simulation is as follows:

(1) the method comprises the following steps that a built CarSim/Simulink electric automobile simulation platform is used, and the whole automobile simulation parameters in the CarSim are consistent with the real automobile parameters; wherein the parameters comprise the finished vehicle service quality and tire parameters; the environmental settings are as consistent as possible, including road adhesion coefficient and gradient;

(2) and (4) simulating according to the real vehicle test conditions, and comparing and analyzing the real vehicle brake distance and brake time with the Iboost brake system.

Another object of the present invention is to provide a system for building an electric vehicle brake system model using the method for building an electric vehicle brake system model, the system comprising:

the simulation model establishing module is used for establishing a simulation model of the AEB of the electric automobile;

the interface establishment module is used for establishing interfaces of CarSim and Simulink software;

the brake system model establishing module is used for establishing a brake system model based on Simulink;

and the simulation model verification module is used for verifying the simulation model.

It is a further object of the invention to provide a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of:

(1) establishing a simulation model of an electric automobile AEB;

(2) establishing interfaces of CarSim and Simulink software;

(3) building a braking system model based on Simulink;

(4) and verifying the simulation model.

It is another object of the present invention to provide a computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of:

(1) establishing a simulation model of an electric automobile AEB;

(2) establishing interfaces of CarSim and Simulink software;

(3) building a braking system model based on Simulink;

(4) and verifying the simulation model.

The invention also aims to provide an information data processing terminal which is used for realizing the establishment system of the electric automobile brake system model.

By combining all the technical schemes, the invention has the advantages and positive effects that: the model of the braking system of the electric automobile provided by the invention is based on the AEB system architecture design of the electric automobile, models of the AEB system and main components are established, and an interface between AEB simulation analysis platform software of the electric automobile is established. The main content of the invention comprises:

(1) simulation analysis software was analyzed. According to the simulation requirements of the AEB system, three types of software including automobile dynamics simulation, scene simulation and program compiling are analyzed, CarSim and MATLAB/Simulink are selected as system simulation software, and the characteristics of the two types of software are analyzed in detail.

(2) A simulation model of the AEB of the electric automobile is established. According to the project requirements and the design requirements of the AEB system, a whole vehicle model, a radar model, a camera model and a road model are established in the CarSim, and the AEB control strategy is laid for verification. Besides, a CarSim/Simulink software interface is established, and the information content exchanged between the software is edited and determined.

(3) A braking system model based on Simulink is established. The structure and the function of the brake system and the characteristics of the I-Booster are analyzed, main relevant factors of the brake system reaction time are extracted according to the AEB brake system verification requirement, the characteristics of a hydraulic pipeline, an electromagnetic valve and a brake wheel cylinder are analyzed, and a hydraulic pipeline model, an electromagnetic valve model and a brake wheel cylinder model are established in Simulink.

(4) And comparing and verifying the simulation models of the AEB and the braking system. The invention carries out the comparative analysis of the actual vehicle test data of emergency braking and the simulation test result on the AEB simulation platform, and the comparative analysis result shows that the simulation analysis data of the simulated braking distance and the braking time are basically consistent with the actual vehicle test data, and the simulation model has good accuracy.

The invention takes an electric automobile as a research object, takes the improvement of the active safety of the electric automobile as a main target, designs an AEB system architecture of the electric automobile, establishes an AEB simulation test platform of the electric automobile, researches an AEB control strategy, performs simulation analysis on the AEB control strategy and performs software development. The invention designs an electric automobile AEB system architecture based on the functional requirements of the electric automobile AEB system, and the architecture comprises a hardware architecture and a software architecture of the system. The driving information perception module, the controller module and the executor module are analyzed and researched, inter-module communication and network protocols are designed, and type selection and installation arrangement design are carried out on perception sensors.

On the basis of an AEB system architecture of the electric vehicle, the invention builds an AEB simulation test platform based on the CarSim/Simulink electric vehicle. According to the simulation requirements of the AEB system, mainstream simulation software is contrastively analyzed and selected, so that a whole vehicle model, a radar model, a camera model, a road model and a brake system model are established, and the established brake system model is verified by using test data of a corresponding vehicle model.

Aiming at the longitudinal safe vehicle distance, the invention establishes a safe distance algorithm and a collision time algorithm, makes a fusion strategy based on the safe distance algorithm and the collision time algorithm, and optimizes the algorithms, so that the algorithms can adapt to the road surface with low adhesion coefficient and realize collision avoidance; aiming at the transverse safe vehicle distance, a dangerous target discrimination algorithm based on a radar coordinate system is constructed; with the aim of improving the safety and the comfort of the automobile, a layered control structure of the AEB is designed, control algorithms of upper and lower controllers are developed, and an AEB braking force distribution strategy is formulated.

The control strategy is subjected to simulation analysis on an electric vehicle simulation test platform, and the result shows that the safe distance algorithm and the control strategy provided by the invention can basically realize collision avoidance in an AEB test scene specified in C-NCAP, and the control strategy has great improvement on a classical algorithm on five indexes of speed reduction rate, maximum deceleration, deceleration change rate, two-vehicle minimum distance and early warning time, improves the safety of the vehicle and the comfort of a driver, and can be well adapted to a road with low road surface adhesion coefficient. In addition, an RTW automatic conversion tool is used, control strategy software is developed under the C language environment, and the application value of the software is improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments of the present invention will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 is a flowchart of a method for establishing a braking system model of an electric vehicle according to an embodiment of the present invention.

FIG. 2 is a block diagram of a system for building a braking system model of an electric vehicle according to an embodiment of the present invention;

in the figure: 1. a simulation model establishing module; 2. an interface establishing module; 3. a braking system model building module; 4. and a simulation model verification module.

FIG. 3 is a functional schematic of an AEB system provided by embodiments of the present invention.

Fig. 4 is a schematic diagram of an AEB control strategy provided by an embodiment of the present invention.

FIG. 5 is a functional schematic diagram of an AEB provided by an embodiment of the present invention.

FIG. 6 is a block diagram of an overall design architecture of an AEB system for an electric vehicle according to an embodiment of the present invention.

Fig. 7 is a schematic diagram of an AEB system hardware architecture according to an embodiment of the present invention.

FIG. 8 is a schematic diagram of the AEB system software architecture provided by the embodiments of the present invention.

Fig. 9 is a flowchart of an AEB system software application layer algorithm according to an embodiment of the present invention.

Fig. 10 is a schematic diagram of fusion of video and radar information according to an embodiment of the present invention.

Fig. 11 is a schematic diagram of a controller module according to an embodiment of the present invention.

FIG. 12 is a schematic diagram of the operation of an actuator module provided by an embodiment of the present invention.

Fig. 13 is a schematic diagram of information communication in the AEB system according to the embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

Aiming at the problems in the prior art, the invention provides an electric automobile brake system model and an establishment method thereof, and the invention is described in detail below with reference to the accompanying drawings.

As shown in fig. 1, the method for establishing the braking system model of the electric vehicle according to the embodiment of the present invention includes the following steps:

s101, establishing a simulation model of an electric automobile AEB;

s102, establishing an interface between CarSim and Simulink software;

s103, building a braking system model based on Simulink;

and S104, verifying the simulation model.

As shown in fig. 2, the system for establishing a braking system model of an electric vehicle according to an embodiment of the present invention includes:

the simulation model establishing module 1 is used for establishing a simulation model of an electric automobile AEB;

the interface establishment module 2 is used for establishing interfaces of CarSim and Simulink software;

the brake system model establishing module 3 is used for establishing a brake system model based on Simulink;

and the simulation model verification module 4 is used for verifying the simulation model.

The technical solution of the present invention will be further described with reference to the following examples.

Example (b): electric automobile AEB system and control strategy simulation analysis thereof

1. The invention designs an electric automobile AEB system architecture based on the functional requirements of the electric automobile AEB system, and the architecture comprises a hardware architecture and a software architecture of the system. The analysis researches three parts of a driving information sensing module, a controller module and an actuator module, designs inter-module communication and network protocols, and carries out model selection and installation layout design on a sensing sensor. On the basis of an AEB system architecture of the electric automobile, an AEB simulation test platform based on the CarSim/Simulink electric automobile is built. According to the simulation requirements of the AEB system, mainstream simulation software is contrastively analyzed and selected, so that a whole vehicle model, a radar model, a camera model, a road model and a brake system model are established, and the established brake system model is verified by using test data of a corresponding vehicle model. Aiming at the longitudinal safe vehicle distance, a safe distance algorithm and a collision time algorithm are established, a fusion strategy based on the safe distance algorithm and the collision time algorithm is formulated, and the algorithms are optimized, so that the method can adapt to a road surface with low adhesion coefficient and realize collision avoidance; and aiming at the transverse safe vehicle distance, a dangerous target discrimination algorithm based on a radar coordinate system is constructed. With the aim of improving the safety and the comfort of the automobile, a layered control structure of the AEB is designed, control algorithms of upper and lower controllers are developed, and an AEB braking force distribution strategy is formulated. The control strategy is subjected to simulation analysis on an electric vehicle simulation test platform, and the result shows that the safe distance algorithm and the control strategy provided by the invention can basically realize collision avoidance in an AEB test scene specified in C-NCAP, and the control strategy has great improvement on a classical algorithm on five indexes of speed reduction rate, maximum deceleration, deceleration rate change rate, two-vehicle minimum distance and early warning time, improves the safety of the vehicle and the comfort of a driver, and can be well adapted to a road surface with a low road surface adhesion coefficient. In addition, an RTW automatic conversion tool is used, control strategy software is developed under the C language environment, and the application value of the software is improved.

The research object of the invention is an electric automobile, aiming at improving the active safety of the electric automobile, designing an AEB system architecture of the electric automobile, establishing an AEB simulation test platform of the electric automobile, researching an AEB control strategy, carrying out simulation analysis on the AEB control strategy and carrying out software development.

2. AEB system architecture design of electric vehicle

Automatic Emergency Braking (AEB) is one of the important functions of advanced assisted driving electric vehicles and is an important part of active safety of smart vehicles. Most of AEB functions use sensing detection technology and modern information technology to improve the perception capability of drivers on things in front of the vehicle and assist the drivers to control the vehicle so as to guarantee the driving safety of the vehicle. The invention develops the design and analysis of the AEB system function requirement and designs the AEB system architecture of the electric automobile.

2.1 AEB System functional design and analysis

According to the requirement of an automatic emergency braking system in the actual running process of a advanced assistant driving electric automobile, the requirement of an AEB system is analyzed in detail through literature research and by combining project requirements, and a plurality of functions realized by the AEB system are provided according to the requirement, so that the subsequent overall architecture design is laid.

2.1.1 System functional requirement analysis

The main functions of the AEB system are shown in fig. 3, namely, the front obstacle can be accurately identified through the perception sensor, whether collision occurs or not is judged, the degree of danger is estimated, early warning to a driver is achieved within a reasonable time range, and if the driver fails to timely react and take proper operation, the AEB system can replace the driver to automatically brake, so that no collision is achieved or the relative collision speed is effectively reduced. The above functions are realized, and the following technical performance requirements are provided for the system and the main components.

(1) Accurate identification of a forward obstacle

(2) Effective early warning and braking

(3) Precise reaction time of the system

(4) Distribution of braking force

2.1.2AEB control System functional design

According to the demand analysis, the AEB system functions can be specifically classified into two categories: automatic early warning braking and other system functions for ensuring normal realization of AEB function.

The schematic diagram of the AEB control strategy designed by the invention is shown in FIG. 4. Firstly, after the expected collision time or distance is judged, an early warning signal is sent to a driver when the limit value is reached. The system will then determine if the driver is responding in time and if not, the system will automatically apply the appropriate braking force. In the case where the driver can react in time, the two cases are also classified as whether the braking force given by the driver is sufficient or not. If not sufficient, the system will supplement the required braking force, and this strategy may try to avoid jerky conditions caused by hard braking.

Because the automatic emergency braking system is a part of the active safety of the automobile, a driver can not completely rely on AEB to avoid all forward collision accidents, so that the early warning of the driver is very necessary, and the driving safety of the automobile can be greatly improved by reminding the driver to perform braking operation. In order to achieve better early warning and control effects, according to different danger degrees, the invention adopts a multi-layer early warning and braking mode, as shown in fig. 5. The distance between the self vehicle and the front vehicle is divided into four stages, and when the distance between the two vehicles is far, namely stage 1 in the figure, the control unit module can send an image early warning signal to the actuator part; when the distance between the two vehicles is closer, higher collision danger is generated, and in stage 2, a signal for double early warning reminding of sound and images is sent out for reminding; when the distance between the two vehicles is continuously shortened and the dangerous degree is high, a stage 3 of sending a signal for collecting partial braking of the vehicles to an actuator is reached; in stage 4, which is at a high risk level, a complete braking signal is emitted, and the maximum braking force is given.

In addition, the software and hardware of the system also need to implement other functions to ensure the implementation of the automatic early warning function and the automatic braking function, including functions of bottom layer driving, hardware thermal management, power module management, voltage driving, inter-controller communication, diagnosis, configuration/calibration, system function safety, system memory management, crystal and crystal oscillator, electromagnetic interference resistance, detection of sensor shielding, and the like, which are not described herein.

2.2 AEB System Overall architecture design

By referring to the related data, the architecture diagram of the AEB system designed by the present invention is shown in FIG. 6. An autonomous driving system is generally divided into three parts, sensing, controlling and executing. The sensing part of the AEB system is mainly used for detecting the self-vehicle and the environmental information, the self-vehicle information is acquired through a self-sensor, and the surrounding environmental information is acquired through a sensing sensor such as a video radar. The control part mainly predicts the front collision and gives an early warning braking instruction in advance. Firstly, the motion states of the vehicle and the front target are judged according to the information of each sensor, and if the vehicle and the front target are dangerous, an early warning signal is given, and the vehicle can automatically and emergently brake when the vehicle and the front target are dangerous. The execution part mainly comprises an actuator, namely an alarm device, in the automatic early warning process; and actuators in the automatic braking process, including motors, braking actuators and the like, are mainly used for automatically adjusting the torque and the braking pressure of the motors in the braking process.

2.2.1 hardware architecture design

The specific hardware architecture of the AEB system of the electric vehicle is shown in fig. 7. The intelligent vehicle with the AEB system is additionally provided with a video radar sensor, a vehicle body perception sensor and an AEB controller on the basis of hardware, and other hardware mainly comprises a vehicle running system and the like. The video radar sensor and the vehicle body perception sensor are used for collecting a vehicle signal and an environment signal in front of the vehicle, and the AEB controller is mainly used for judging the information of the front vehicle and the self vehicle to give an early warning or braking signal and transmitting the signal to the next unit to realize that the warning device gives out an early warning and the automatic braking of the vehicle. The vehicle body perception sensor of the sensor refers to a speed sensor and an acceleration sensor of the automobile, and is used for measuring and calculating the speed and the acceleration of the automobile.

According to the general architecture, the system is divided into three parts of sensing, controlling and executing in a system with an intelligent function, the general architecture of the AEB system can be divided into three parts of a driving information sensing module, a controller module and an actuator module, and each module is designed in detail in section 2.3.

2.2.2 software architecture design

The AEB system software architecture is shown in fig. 8. The software architecture of the AEB system can be divided into two parts: an application layer and a bottom layer. The application layer mainly comprises main application programs in the AEB system, video radar information processing, AEB control strategies, brake control, drive control and the like, and aims to realize the functions of the AEB system. The bottom layer mainly comprises interface programs, bottom layer driver programs and the like, wherein the bottom layer driver programs comprise CAN drivers, A/D drivers, I/O drivers, operation system related programs and the like, the CAN drivers, the A/D drivers, the I/O drivers, the operation system related programs and the like are written according to actual hardware and are simultaneously provided for API function interfaces of the application layer, and the application layer CAN call the interfaces to access the hardware without knowing the hardware, so that the realization of application functions is ensured.

The invention mainly develops the control strategy of the AEB, so that the flow of the software application layer algorithm of the AEB system is further established, as shown in FIG. 9. Firstly, confirming a front effective target by a target screening algorithm of a radar, and acquiring relative distance information and relative acceleration information of a self-vehicle and the front target by a radar ranging algorithm; meanwhile, the camera collects images, and performs image recognition and image processing. Information of the radar and the video is fused through an information fusion algorithm, so that the dimensionality of the information can be increased, and the accuracy of the two in target recognition can be improved. The AEB control strategy judges the collision danger of the automobile in two dimensions of the transverse dimension and the longitudinal dimension, sets a threshold value of a safety distance or collision time, sends out an early warning instruction when reaching the early warning threshold value, and sends out a control instruction when reaching a braking threshold value. During automatic braking, the actual braking deceleration is brought into agreement with the target value by means of a hierarchical controller algorithm. The robustness of the control can be improved by using a hierarchical control method. Furthermore, the stability of the AEB braking process can be ensured by the distribution of the braking force, and a partial energy recovery can be carried out.

2.3 Module design of AEB control System software

According to the architecture design of the AEB control system, the driving information perception module, the controller module and the actuator module are designed in detail.

2.3.1 driving information perception module

The driving information perception module is the precondition of accurate and reliable work of the AEB system, and mainly collects the signal of the vehicle and the signal of the environment in front of the vehicle through various sensors. Detecting a target in front of the vehicle through a forward ranging sensor to obtain information such as relative position and relative speed between the vehicle and each target vehicle; the information such as the speed, the acceleration and the steering wheel angle of the vehicle is obtained through various sensors of the vehicle.

The driving information perception module in the invention is mainly divided into two sub-modules: the video radar sub-module and the vehicle body perception sub-module.

The video and radar information fusion diagram is shown in fig. 10. The video radar sub-module is mainly responsible for sensing surrounding environment information, including identifying and judging the motion state of a target in front of a vehicle, identifying relevant information of a road, and then processing the detected information. Wherein, the radar detection is mainly used for determining an effective target and judging the relative distance, the relative speed and the like with the target; and the visual identification mainly comprises the steps of acquiring an image, and performing sample extraction and feature extraction. Because the information collected by the radar and the camera is different, a sub-processor MCU in the sensing module is required to fuse the data collected by the radar and the camera to finally realize the matching of the target, and the target is output through a data fusion algorithm. The type selection and arrangement of the radar and the camera are shown in section 2.4.

The vehicle body sensing submodule is mainly responsible for sensing and identifying the state of a vehicle body, obtains motion parameters of the vehicle, such as steering wheel turning angle, yaw angle, speed and acceleration of the vehicle, and the like through a sensor of the vehicle, and comprehensively considers the motion state of the vehicle and the motion state of a front vehicle to further judge collision risks.

2.3.2 controller software Module

The controller modules within the AEB system mainly include four controller modules: the system comprises an AEB controller module, a vehicle control unit module, a motor controller module and a brake controller module. A schematic diagram of the controller module of the AEB system is shown in fig. 11.

The AEB controller is used for processing information of the driving information sensing module, firstly, the relative distance, the relative speed, the relative acceleration and the like between the video radar submodule and a front vehicle are collected to be measured and calculated, and whether an early warning signal or a braking signal needs to be sent or not is judged according to the safety state of the vehicle through a safety distance algorithm in the controller. The AEB controller transmits a braking request to the vehicle control unit, and the vehicle control unit transmits a control quantity to the motor controller unit and the braking control unit after receiving the signal, so that the control on the motor braking force and the hydraulic braking force is realized respectively; in addition, the AEB controller sends an early warning signal to the vehicle control unit, and then transmits information to the warning device to realize early warning. The AEB controller transmits all signals to the vehicle control unit, so that the communication protocol of the vehicle in the early warning brake control is reserved.

2.3.3 actuator Module

The operation schematic diagram of the actuator module is shown in fig. 12, and the actuator module mainly comprises an instrument panel, an audible alarm, a motor and a hydraulic brake actuating mechanism. According to the functional design of an AEB system, early warning is divided into two layers of early warning, the first early warning has lower collision danger degree because the distance between the early warning and a front target is still longer, red image flicker is used on an instrument panel to prompt the danger of front collision, and sound early warning is not used to avoid bringing poor driving experience to a driver; and during the second early warning, the collision danger between the automobile and the front target is large, and the AEB system sends out warning sound in a sound alarm mode to realize the early warning of the driver. When the controller judges that the automobile has to be braked, an automatic braking signal is given, so that the motor torque control quantity and the braking pressure control quantity are calculated, the motor and the hydraulic braking mechanism are respectively controlled, and the automatic braking of the electric automobile is controlled through two modes of motor braking and hydraulic braking.

2.3.4 inter-Module communication and network protocol design

The AEB system in the invention is a simplified automobile network, and the bottom layer communication protocol adopts a CAN bus and adopts two modes of a Private CAN (PCAN) and a finished automobile CAN (VCAN). The radar and the camera transmit original target data of the millimeter wave radar module into respective MCU for data processing by using a Private CAN (PCAN), and finally are fused into a visual Fusion processor VFP (vision Fusion processor), and the final target information available for feature is obtained by using a target Fusion algorithm and a target screening algorithm. After the VFP fuses and processes the information, the information is transmitted to the AEB through the CAN (VCAN) of the whole vehicle so as to realize the judgment of collision danger. And information in the automobile body sensing submodule is transmitted to the AEB controller through the VCAN, and the information of the AEB controller interacts with the motor controller and the brake controller in the actuator module through the whole automobile CAN. FIG. 13 is a schematic diagram of inter-module communication within the AEB system.

2.4 model selection and installation layout design of sensor

The forward ranging sensing sensor is an important component of an AEB system, and directly influences the effect of the AEB on the accuracy of front target detection. The following ways of measuring the vehicle distance are available: laser radar ranging, ultrasonic radar ranging, microwave radar ranging, infrared radar ranging, visual ranging, and the like. The method and the application scene of the distance measurement of different sensors are different.

Various ranging sensor parameters and their performance comparison tables are shown in table 1 by reference and research literature.

TABLE 1 comparison of various ranging sensor parameters and Performance

The AEB system adopts a model selection scheme of 1R1V, namely a scheme of a millimeter wave radar and a camera, and the scheme can meet the requirement of automatic emergency braking, reduce the cost and keep enough identification precision.

2.4.1 millimeter wave radar model selection and arrangement

The radar emission source signal transmits a continuous frequency modulation signal with a certain slope outwards through the circulator and the antenna, after the antenna receives a signal reflected by a front obstacle, a frequency mixer in the device modulates a difference frequency signal, the difference frequency signal is finally converted into a rectangular pulse signal through the Doppler amplifier and the shaping circuit, the required speed difference and the distance between two vehicles are calculated according to the number of pulses, and the millimeter wave radar calculates the distance and the speed by combining the propagation speed and the transmission frequency of electromagnetic waves according to the frequency difference of the transmitted and received signals.

In the formula: s-distance between target and self-workshop; v. of_ref-target to host relative velocity; f. of_BD、f_BU-a lower difference frequency and an upper difference frequency occurring in the interval in which the frequency of the transmitted wave decreases; c-the propagation velocity of the electric wave; f. of_m-frequency modulated repetition frequency; f. of₀-a transmission center frequency.

The vehicle-mounted millimeter wave radar on the market at present can be classified into a broadband radar and a narrow-band radar according to a bandwidth, into a continuous wave radar and a pulse radar according to a working principle or a mode of electromagnetic wave emission or a waveform, and into a long-range radar (LRR), a medium-range radar (MRR), and a short-range radar (SRR) according to a detection distance. The following table describes three types of radars with respective detection ranges, and the basic properties thereof are shown in table 2.

TABLE 2 basic attribute table of various millimeter wave radars

The mainstream millimeter wave radar products in the market at present mainly include bosch corporation, delfu corporation and the like, and the mainstream products thereof are shown in table 3.

TABLE 3 vehicle millimeter wave radar mainstream product

According to the table 2 and the table 3, schematic diagrams of detection ranges of the three vehicle-mounted millimeter wave radars of the SRR, the MRR and the LRR on the vehicle are given. The millimeter wave radar is hidden in the right center of the positive bumper. The adoption of some unsuitable surface covering materials can shield millimeter waves or cause beam distortion and standing wave deterioration, so that the radar fails or the sensitivity is reduced, and therefore, a uniform PP material with the thickness of about 100mm is coated in front of the millimeter wave radar.

2.4.2 type selection and arrangement of vehicle-mounted camera

The vehicle-mounted camera is a necessary basis for the research of a vehicle forward collision early warning system, a conversion relation between an image coordinate system and a vehicle body coordinate system is established, and the position of a vehicle, a lane line and the like in an image and the corresponding position information of the real world can be obtained through the camera, so that the lane line detection and the vehicle detection are carried out.

Various camera sensor pairs are shown in table 4. In order to improve the reliability and accuracy of the detection of the front vehicle target, the invention improves the detection accuracy by using a scheme of fusion detection of a monocular camera and a millimeter wave radar, and can be more suitable for complex environments.

TABLE 4 comparison of various camera sensor parameters and performances

The Howey science and technology OV2640 network camera is designed, and is suitable for the design of the invention because of universal interface, adjustable parameters such as resolution, frame rate and the like and lower cost. The parameters are shown in Table 5.

TABLE 5 OV2640 parameter List

According to the arrangement scheme of most monocular vision sensing in the market, the camera is arranged behind the inner rearview mirror in the vehicle, the arrangement scheme can ensure that the front view is large enough, the influence of special weather such as strong light, rain, snow and the like can be effectively avoided, and the functional requirements of the AEB are met.

3. Establishment of AEB simulation platform of electric vehicle

When the AEB system is mounted on an electric vehicle, a simulation platform is generally established by using a computer, and functions of the AEB system are verified and subjected to simulation analysis, which is the primary link for mounting the AEB system of a real vehicle. According to the AEB system architecture, an AEB simulation platform of the electric automobile is built.

3.1 simulation analysis software

And (4) simulation analysis of the AEB control strategy of the electric vehicle. The invention selects Simulink to compile the algorithm and the strategy by analysis, and establishes a braking system model by the Simulink; CarSim is selected as automobile dynamics simulation software and scene simulation software. CarSim contains an interface to Simulink, allowing joint simulation.

3.1.1 CarSim software analysis

The CarSim software was developed by the university of michigan, institute of transportation for vehicle dynamics, for in-vehicle simulation. The simulation software can be used for building a vehicle dynamics model, building a driver model, building a road model and an intelligent driving environment, and evaluating the smoothness, the dynamic property and the stability of an automobile through simulation prediction. The CarSim software is widely used in the development of vehicle control systems and can achieve running speeds on computers that are more than 5 times faster than real time. Since there are very many expansion interfaces that can be simulated in conjunction with dSPACE, Matlab/Simulink, etc. software to perform or evaluate the design of the vehicle dynamics system. The invention uses the version of CarSim 2016.1, and can conveniently realize the setting of the motion states of a plurality of target vehicles.

The most advantage of CarSim is that it can not only simulate the dynamics of an automobile, but also realize scene simulation, and the control interface and the simulation interface are very intuitive, and most of the present mathematics use CarSim as scene simulation software in simulation analysis. The invention also selects CarSim as automobile dynamics software and scene simulation software.

3.1.2 MATLAB/Simulink software analysis

MATLAB is a commercial mathematical software produced by MathWorks company in America, integrates a plurality of powerful functions of numerical analysis, matrix calculation, scientific data visualization, modeling and simulation of a nonlinear dynamic system and the like into an easy-to-use window environment, and provides a comprehensive solution for scientific research, engineering design and many scientific fields which need effective numerical calculation. Simulink is a visual simulation tool in MATLAB, is a block diagram design environment based on MATLAB, is a software package for realizing dynamic system modeling, simulation and analysis, and is widely applied to modeling and simulation of advanced auxiliary driving and intelligent driving. The version of the simulation software used in the invention is MATLAB 2018 b.

Simulink has the advantage of having a visual control module, making the connection between the various modules more intuitive. In addition, CarSim can be used as a Simulink control module, which facilitates Simulink to control targets in CarSim, but requires the establishment of interfaces between CarSim and Simulink programs. Therefore, the invention selects Simulink software to write algorithms and control strategies and build partial models.

3.2 simulation modeling of electric vehicle AEB

The invention builds an AEB simulation model in CarSim, and comprises a whole vehicle model, a perception sensor model and a road model.

3.2.1 complete vehicle model

The invention establishes a whole vehicle model, inputs actual whole vehicle parameters and ensures that the simulation has actual value. Two whole vehicle models need to be established, one is an electric vehicle carrying an AEB function, namely a research object of the invention; the other is a front vehicle, namely a locking target vehicle of the electric automobile which is the research object of the invention.

For the whole vehicle model of the research object, the invention aims at the electric vehicle in the sub subject of the national research and development plan of the department of science and technology, and the whole vehicle model is built in CarSim by using the sample vehicle parameters, and the physical parameters of the whole vehicle are shown in Table 6.

Table 6 complete vehicle parameter table for simulation

The parameters of the whole vehicle are input into a whole vehicle model of CarSim, and the built whole vehicle model mainly comprises the setting of parameters such as the mass of the whole vehicle, the distance from a front shaft to a rear shaft to a mass center, the wheel base, the height of the mass center, the rotational inertia of tires and the like. The mass of the whole vehicle is modified along with the actual working condition.

For modeling of a forward locking target vehicle, only motion parameters such as speed and acceleration can be acquired on an actual road surface, so that the speed and the acceleration are mainly considered during modeling and other structural parameters do not make special requirements in order to simplify the model and reduce unnecessary workload. The invention uses the C-Class model provided by CarSim, and comprises the settings of vehicle structure parameters, vehicle body appearance, tires and other parameters.

3.2.2 perception sensor model

The radar ranging sensor is a main sensor for obtaining the relative distance, the relative speed and the acceleration of the vehicle, and the camera is used for identifying the object ahead and assisting the radar in ranging. The model of the radar and the camera is provided with the radar and the camera so as to observe the running state of the vehicle in the forward collision early warning system more intuitively and conveniently, and the type selection and the arrangement of the millimeter wave radar and the camera are carried out according to the AEB system architecture. The camera is selected from Haowei science and technology OV2640 network camera, and the millimeter wave radar is selected from MRR4 Boshi middle range radar. According to the main parameters of the radar and the camera given in the second chapter, models of the millimeter wave radar and the camera are built in the CarSim. For the arrangement of the sensor, according to the design requirements of the chapter II, the millimeter wave radar is hidden in the center of the front bumper, and the camera is arranged behind an inner rear view mirror in the vehicle.

3.2.3 road model

The road model established by the invention comprises the settings of parameters such as road surface gradient, road surface adhesion coefficient, rolling resistance coefficient, environment and the like, and the model can be adjusted differently according to different working conditions. According to the verification condition of the safe distance model, the road condition is specified to be straight-line driving, the front vehicle and the rear vehicle are positioned on the same lane, wherein the front vehicle is a target vehicle, and the rear vehicle is a test vehicle loaded with the designed AEB system.

3.3 interface establishment for CarSim and Simulink software

In the process of carrying out the joint simulation, the CarSim provides data such as an automobile dynamic model, an event and road surface parameters; the establishment and optimization of the safe distance algorithm are completed in Simulink, so the establishment of an interface between two pieces of software must be completed.

Firstly, a special Simulink interface in CarSim is used, and Sendto Simulink instructions are used for outputting parameters of the automobile built in CarSim, a road surface model and front and rear automobiles to the Simulink. And constructing information of the front vehicle in the CarSim, and determining output parameters of the front target vehicle and the output parameters of the vehicle. An interface needs to be established in the process of verifying the safe distance model. The input parameter of the vehicle is the brake pressure IMP _ PBK of 4 cylinders, and the brake pressure is controlled through a brake force distribution strategy; the output parameters are the control pressure Pbk _ Con of the vehicle, the centroid speed Vx _ SM of the vehicle, the acceleration Ax of the vehicle, the relative distance Dis 1_1 with the front vehicle and the relative speed SpdS1_1 with the front vehicle respectively. The motion state of the front vehicle is calculated by measuring the relative distance and the relative speed measured by the radar and measuring the speed and the acceleration of the vehicle, and then the motion state is input into a safe distance algorithm.

3.4 Simulink-based brake system model establishment

In order to simulate the braking effect of a real vehicle, besides the accurate construction of a vehicle-road model, a braking system model is also constructed to simulate the braking coordination time and the braking force rising curve in the braking process. The braking system used by the invention mainly comprises an I-Booster driving structure, a brake pedal, a brake pipeline, an ESP/electromagnetic valve, a brake wheel cylinder, a power supply and the like.

The drive of the braking system used in the invention is a second generation product from bosch I-boost. The second generation I-Booster is a common product in an AEB system and is based on an EHB technology, and the structure of the second generation I-Booster mainly comprises a motor-control unit, a brake master cylinder, a deep drawing steel plate shell and an interface. The I-Booster can realize shorter braking distance and reduce the impact speed, thereby reducing the accident rate and the severity of the accident and being very suitable for the requirement of automatic emergency braking. The brake pipeline is a hydraulic pipeline and transmits braking force by taking liquid as a medium. The ESP mainly has the function of adjusting the braking force of the brake wheel cylinder by controlling the pressure reducing valve and the pressure increasing valve after receiving an upper-level signal.

The braking system model is simplified because the braking system is complex, the modeling time and cost are high, partial data are difficult to obtain, and complete modeling is difficult. In the invention, three important factors influencing braking force and braking coordination time are selected to establish a hydraulic braking model: firstly, a hydraulic pipeline; a second electromagnetic valve; and thirdly, braking the wheel cylinder.

3.4.1 Hydraulic pipeline model

The dynamic characteristics of the brake oil pressure of the hydraulic line are simulated by establishing a hydraulic line model. In order to simplify the hydraulic pipeline model and reduce the complex calculation, a first-order inertia element is usually used for description, as shown in formula 3.

Wherein P(s) -the actual oil pressure (MPa) of the brake;

P₀(s) -target brake oil pressure (MPa);

tau-a constant reflecting the dynamic characteristics of the brake, obtained by hydraulic pipeline tests.

3.4.2 electromagnetic valve model

The switching time of the solenoid valve directly affects the response time of the brake system. Since the modeling of the hydraulic brake system only considers the braking force and the braking coordination time, only the time delay characteristic of the electromagnetic valve model is considered when the electromagnetic valve model is established. In the research of the national automobile safety and energy-saving national key laboratory of the Qinghua university, the relevant test is carried out on the switching time of the electromagnetic valve under the working conditions of load and no load, and the switching time is usually 1ms to 10 ms. Because the time is short and the difference of the switching time between different electromagnetic valves is small, simulation research is performed corresponding to the corresponding time according to the number and the state of the electromagnetic valves. The system comprises two electromagnetic valves, a pressure increasing valve and a pressure reducing valve, and the total delay time of the electromagnetic valves is determined to be 10 ms.

3.4.3 brake wheel cylinder model

In the braking process, the pressure of the hydraulic pipeline presses the piston of the wheel cylinder, and the piston pushes the brake block to tightly press the brake disc to stop the wheel from running. The brake model can be simplified into a piston dynamics model, and the mechanical characteristics of the pressure input of the brake wheel cylinder to the brake torque output are simulated.

Through the analysis of the stress in the piston motion process, the mechanical relation of the formula (4) can be obtained according to the Newton second law:

in the formula, P is wheel cylinder input pressure (MPa);

a-piston Cross-sectional area (m)²)；

m-moving mass (kg) equivalent to the wheel cylinder piston;

K_p-brake stiffness (N/m);

C_p-a damping coefficient;

F₀-system dry friction (N);

X_p-wheel cylinder piston displacement (m).

At this time, the positive pressure of the piston acting on the brake disc is:

by applying laplace transform to equation (4), we can obtain:

in engineering practice, the piston is stressed in balance because the piston and the brake block are basically pressed on the brake disc all the time, i.e. the piston is stressed in balance

The effect of dry friction is small and can be neglected, and the following relation is given:

from equations (5), (6), (7) and (8), the positive pressure acting on the brake disc can be:

the braking torque at the wheels can be expressed as:

wherein r is₁-an effective friction radius (m);

eta-brake effectiveness factor.

From the above analysis, the brake model can be simplified into a second-order inertia element as described by equation (9) for simulation. The deviation from the target braking force can be gradually reduced by using a PID control method for the actual braking force.

The known brake system internal engineering parameters are shown in table 7.

TABLE 7 engineering parameters in brake System

According to the analysis of the model and substituting the parameters in the table, the simulation model of the braking system is established based on Simulink, and the simulation model comprises electromagnetic valve delay, PID control, a hydraulic pipeline first-order system, a brake wheel cylinder second-order system and the like.

3.5 verification of simulation model

After the brake system Simulink simulation model is established, in order to judge the consistency of the system simulation model and the actual system, the simulation model is verified. Because the system uses the simplified model, the structure and the characteristics in the system do not need to be considered, and the influence on the braking distance and the braking coordination time is only considered, the invention only verifies whether the simulated braking effect is consistent with the braking effect of the real vehicle or not, so as to ensure that the braking time and the braking distance are real and reliable when the automatic emergency braking is triggered in the simulation. The effect verification scheme based on the emergency braking in Simulink and CarSim combined simulation is as follows:

(1) by using the CarSim/Simulink electric vehicle simulation platform built by the invention, the whole vehicle simulation parameters in the CarSim are consistent with the real vehicle parameters, and the main parameters are the whole vehicle setup quality, the tire parameters and the like. Environmental settings, such as road adhesion coefficient, grade, etc., are as consistent as possible.

(2) And (4) simulating according to the real vehicle test conditions, and comparing and analyzing the real vehicle brake distance and brake time with the Iboost brake system.

Real vehicle test scene and workstation. The vehicle type used is a PHEV electric vehicle provided with a second generation Iboost brake system. The test method comprises the steps of closing energy recovery, respectively taking data of vehicle speed sections of which the speed is reduced to 0 by 50km/h, 45km/h, 40km/h, 35km/h, 30km/h, 25km/h and 20km/h for analysis, respectively setting target brake strength to be 0.3, 0.5, 0.7 and 0.8 which are typical emergency brake working conditions, and recording brake distance and brake time. The braking distance refers to the distance traveled by the vehicle in the process of giving a braking signal until the vehicle is stationary; the braking time is the time value which is elapsed from the moment the brake signal is given to the moment the vehicle is stationary. The test conditions were dry asphalt flat pavement, with a set mass of 1538kg +130kg (two persons).

The simulation test scene is set up based on the AEB simulation test platform, and the test conditions of the simulation test scene are set to be consistent with those of the real vehicle test scene. And comparing and analyzing the braking distance of the real vehicle and the simulated braking distance. Through calculation of data points, the error between the simulated braking distance and the braking distance of the real vehicle is small, and the average value of percentage errors is 4.438%; under the working conditions that the target braking strength is 0.3g and the initial speed is 20km/h, the percentage error is 10.974% at the maximum; under the working conditions that the target braking strength is 0.8g and the initial speed is 45km/h, the difference value between the two distances is 0.90 m at the maximum. It can be seen that the difference between the braking distance under the experimental condition and the braking distance under the simulation condition of the target braking strength of 0.5g and 0.7g is small, the deviation between the target braking strength of 0.3g and the driving speed is large, the deviation between the target braking strength of 0.8g and the driving speed is large, the simulated braking distance is usually larger than the braking distance of the real vehicle, which may be caused by the deviation between the actual braking strength and the target braking strength, and the poor system stability when the braking strength is high, and the braking effect is likely to be influenced. The total deviation of the simulated braking distance and the braking distance of the real vehicle is smaller.

And comparing the real vehicle with the simulation braking time. Through calculation of data points, the simulated braking time is smaller than that of an actual vehicle, and the average value of percentage errors is 3.889%; under the working conditions that the target braking strength is 0.7g and the initial speed is 20km/h, the percentage error is 10.245% at the maximum; under the working conditions that the target braking intensity is 0.3g and the initial speed is 20km/h, the time error value is the largest and is 0.196 seconds. When the running speed is low, the simulation braking time is smaller than that of the actual vehicle test; when the running speed is slightly larger, no obvious rule exists, because the actual braking strength and the target braking strength have slight deviation due to the influence of the operation of a driver when the actual vehicle is braked; in addition, because the actual road adhesion coefficient is difficult to measure, some errors may occur during the simulation setup.

Although the braking distance and the braking time have errors in simulation and real vehicle tests, the deviation is generally small, the simulation braking effect and the real vehicle braking effect are basically consistent, and the simulation model has good authenticity. Through the comparative analysis of the real vehicle test and the simulation, the effectiveness of the AEB simulation platform of the electric vehicle is reflected laterally.

4. Research on AEB control strategy of electric vehicle

The invention analyzes the characteristics of the AEB braking process, designs a layered control system of the AEB, researches an AEB control algorithm and strategy, and researches a braking force distribution strategy.

4.1 AEB braking Process characterization

The braking process of the AEB can be divided into 5 stages which are respectively an early warning stage T₁Brake actuator coordination phase T₂Braking force increasing stage T₃Stable braking phase T₄And a brake release phase T₅。F_bRepresents the total braking force, a_bRepresenting the braking deceleration.

(1) Early warning stage T₁: early warning stage T₁Is determined by the early warning time period T₁' and T₁"constitute, represent first early warning time quantum and second early warning time quantum respectively. According to the functional design of the early warning strategy in chapter II, T is sent to the driver before automatic braking₁' and T₁' two-time early warning, giving the driver the time of operation in advance, and the first early warning time period T₁' is the process from the beginning of the first early warning to the beginning of the second early warning; second early warning time period T₁", this is the process from the beginning of the second warning to the issuing of the braking command. The early warning effect is directly influenced by the set length of the early warning time, if the time is too long, the braking distance is too far when a driver brakes, false warning is easily formed on vehicles, and the traffic traveling efficiency is influenced; if the early warning time is too short, the driver is not easy to react in time, so that risks are brought.

(2) Brake actuator coordination phase T₂: generally refers to the process from the brake clearance elimination of the brake and the electrification to the generation of electromagnetic torque, and the elapsed time is the brake system coordination time t₂。

(3) Braking force increasing phase T₃: refers to the process from zero braking deceleration to the maximum value, and the elapsed time is the braking force increasing time t₃。

(4) Continuous braking stepSegment T₄: generally, the method refers to a stage of keeping constant deceleration braking of the automobile, when the deceleration of the automobile reaches the maximum value and is constant during braking, the automobile starts to enter a continuous braking stage, and the elapsed time is a continuous braking time t₄。

(5) Brake release phase T₅: generally, it is referred to a stage in which the system automatically releases the brake to full release after a constant deceleration. When the vehicle approaches a stop, the brake deceleration does not immediately become zero, but continues for a while, and the descending process approximates an oblique straight line. Usually 0.2s to 1s, which will affect the next vehicle re-starting acceleration process. The time for gradually eliminating the braking force is set as t₅。

Through the analysis of the braking process, a braking distance formula is obtained as follows:

(1) at T₁And T₂And the step of making the vehicle make uniform linear motion, and the distance traveled is S_1.2：

S_1.2＝v₀(t₁+t₂) (11)

In the formula v₀-braking an initial speed;

t₁-a pre-warning time;

t₂-the braking system coordinates the time.

(2) At T₃Stage, the deceleration of the vehicle is linearly increased and is changed into deceleration movement, T₃The distance traveled by the stage is S₃。

The acceleration of this process is:

in the formula a₀-the maximum braking deceleration provided by the road surface adhesion coefficient phi;

t₃-a braking force increase time.

Let T denote at T₃Time elapsed in phase v₀Represents that the automobile is at T₃Initial velocity of stage, v ═ 0₀At this time, there are:

in the formula: v is the longitudinal speed of the car.

Therefore, it is not only easy to use

Changing t to t₃Substitution into (13) to obtain T₃End velocity v of a phase₃Comprises the following steps:

(3) at T₄Stage, the initial speed of the automobile is v₀Last velocity v₄Deceleration is maximum a_maxAt a constant deceleration, the distance traveled during this phase being S₄. According to the relation between the distance, the starting speed, the ending speed and the acceleration, the following steps are obtained:

the total braking distance S is obtained according to the above formula:

because of t₃Very short, of formula (17)

The term is very small and negligible, and T₅The process is so short that v₄Close to 0, the total braking distance S can be finally found to be:

4.2 AEB control strategy Overall architecture

The AEB control strategy architecture of the present invention. The automobile can acquire the motion information of a front target through the video radar submodule, and simultaneously acquire the motion information of the automobile through the automobile body sensor submodule, and the information is transmitted to the AEB controller. The AEB control algorithm mainly has the functions of judging danger after receiving the motion information of the front vehicle and the self vehicle and giving out early warning and braking instructions; the controller and other strategies in the control strategy mainly control the actuator to realize braking and ensure the expected braking effect.

The AEB control algorithm mainly comprises a safe distance algorithm, a collision time algorithm and a threshold setting part.

4.3 AEB control Algorithm study

According to the establishment of the control strategy overall architecture, the invention researches a safe distance algorithm, a collision time algorithm, an algorithm fusion strategy and a dangerous target discrimination algorithm in detail.

4.3.1 safe distance Algorithm

(1) Safe distance basic algorithm

The invention establishes a safe distance basic algorithm to assist the driver to effectively and accurately judge to keep the distance from the front vehicle.

The method is characterized in that vehicles A and B which run in the same direction on the same lane are assumed, and other avoidance modes such as steering are not considered when emergency braking is adopted. And D is the relative distance between the vehicle B and the vehicle A, wherein the vehicle B is provided with an AEB system. Let B vehicle speed v_bThe vehicle A moves forwards at a constant speed v_aAfter t seconds, the speed of the vehicle B is v_b' vehicle speed of A is v_a' the relative distance between two vehicles is changed into d, and the running distance of the vehicle A in the whole process is S_aThe driving distance of the vehicle B is S_b。

The minimum safe distance D is:

D＝S_b+d-S_a (19)

d-minimum safe distance between two vehicles;

S_b-the distance traveled by vehicle B during braking of vehicle B;

S_a-distance traveled by vehicle a during braking of vehicle B.

d-the set minimum distance between the two cars throughout the braking process.

To ensure d is constant, it is necessary to calculate S_bAnd S_aThe minimum safe distance D is found.

The calculated minimum safe distance can ensure the driving safety of the automobile under a plurality of working conditions, but the vehicle can not be absolutely safe, which is mainly caused by the uncertainty and complexity of human, automobile and road systems. Due to the conditions of the proficiency, the psychological state, the actual working condition and the like of the driver, a certain time is needed for the driver to sense, analyze and judge the information in the driving process and finally make an operation response.

In order to ensure the safety of the driver, an early warning braking mode is set. In the safety distance algorithm, if the early warning stage adopts a two-stage early warning mode, the time duration of the two early warnings is generally the same. The invention uses the time length of each early warning as t₀And (4) showing. The first early warning increases 2t on the basis of the safety critical distance₀The time of the first warning is to ensure that the driver of the B vehicle has sufficient reaction time, and the relative distance between the two vehicles during the first warning is called as a target locking distance D₁(ii) a The second early warning increases t on the basis of the safety critical distance₀The driver is warned by the time of the first warning, and the relative distance between the two vehicles during the second warning is called as the critical distance D of the danger₂(ii) a The automatic emergency braking is that when the relative distance between two vehicles reaches the minimum safe distance and is called the limit critical vehicle distance D₀. Defining the early warning stage to obtain t₀＝t₁’＝t₁”＝0.5t₁。

Since the deceleration of the vehicle B is relatively stable, the running state of the vehicle A is the main influence factor of the safe distance. Therefore, according to different front vehicle motion states, on the basis of the established safe distance algorithm, the safe distance of the three-way vehicle is analyzed and calculated according to the three classical working conditions.

When A vehicle is at rest, B vehicle is at speed v_bThe vehicle runs in the same direction as the A vehicle. Calculating the minimum safe distance D between two vehicles according to the formula (19)₀Comprises the following steps:

in the formula: d₀The minimum distance between the two vehicles;

a_bmaxthe maximum braking deceleration of the B vehicle.

When early warning is carried out for the first time, the vehicle B is considered to be at the vehicle speed v_bThe vehicle runs at a constant speed, and the relative distance between the two vehicles when early warning is carried out for the first time is as follows:

the relative distance between the two vehicles is as follows when early warning is carried out for the second time:

when the vehicle A accelerates or runs at a constant speed, the vehicle B drives at v_bWhen the speed of the vehicle is close to the speed of the vehicle A, the speed of the vehicle B is less than or equal to the speed of the vehicle A (v)_b≤v_a) The speed of the time and the vehicle B is higher than that of the front vehicle. According to analysis, when v is_b≤v_aIn time, the relative distance between the two vehicles is larger and larger or is kept unchanged, and the two vehicles cannot collide with each other. When the speed of the vehicle B is greater than that of the vehicle A (v)_b＞v_a) When the distance between two vehicles is gradually reduced to v_b＝v_aA relative distance minimum is obtained. Therefore, when the speeds of the front vehicle and the rear vehicle are equal, the dangerous time under the current working condition is determined. And respectively calculating the distance traveled by the two vehicles from the beginning to the moment, and obtaining a minimum safe distance algorithm. In the working condition, the minimum safe distance is calculated, and the A vehicle needs to be set to have the maximum braking acceleration a_amaxSpeed reducing transportAt this time, the road surface slip ratio is approximately equal, so a_amax＝a_bmax。

A. B two vehicles with unequal speeds are set as v_rThe relative speed of the A vehicle and the B vehicle measured for the radar is as follows:

v_r＝v_b-v_a (23)

calculating the driving distance S of the vehicle B_b：

Calculating the driving distance S of the A vehicle in the period_a：

Substituting equations (24) and (25) into (19) yields the minimum safe distance between the two vehicles as:

the relative distance between the A vehicle and the B vehicle is as follows when early warning is carried out for the first time:

the relative distance between the two vehicles A and B is as follows when the early warning is carried out for the second time:

at deceleration a of A vehicle_aWhen the vehicle is running at a constant speed v_bThe speed approaches the A car. From the deceleration of the A vehicle, the driver of the B vehicle judges that the A vehicle decelerates and starts deceleration braking. No matter the A and B vehicles stop first or stop at the same time, the relative distance between the two vehicles will stop at the B vehicleThe time-out reaches a minimum value. Therefore, the stop of the vehicle B is the most dangerous time under the present condition. And respectively calculating the distance traveled by the two vehicles A and B from the beginning to the moment to obtain a minimum safe distance algorithm.

Firstly, the travel distance S of the B vehicle is calculated according to the formula (19)_b：

And calculating the running distance S of the A vehicle in the time period according to the formula (20)_a：

The minimum safe distance between the two vehicles is obtained by substituting the equations (29) and (30) into (19):

the relative distance between the two vehicles is as follows when early warning is carried out for the first time:

the relative distance between the two vehicles is as follows when early warning is carried out for the second time:

(2) safety distance impact factor analysis

From the above analysis, it can be seen that the parameters affecting the vehicle distance algorithm include the real-time speed v of the vehicle B_bAnd maximum deceleration a_bAnd a relative speed v with respect to a preceding traveling vehicle target_rAnd the acceleration a of the front vehicle_aAnd the minimum safe vehicle distance d and the early warning time t₁Eliminating the brake chamberTime of slot t₂Braking force increase time t₃. Finally, the influencing factors can be classified into three categories: environmental factors, driver factors, and vehicle factors.

The influence factors are related to the quantity of the driver, namely the minimum safe vehicle distance d and the early warning time t₁. The driving habits and reaction times of the same vehicle driven by different drivers are greatly different, mainly from the reaction force, the coping ability, the psychological quality and the like of the drivers when facing the conditions, and also from the factors of the driving skills, the ages, the driving ages and the like of the drivers. Will obviously be t₁And d is set to be larger, so that the safety is safer, but the normal driving experience of a driver can be influenced.

The quantity of influencing factor associated with vehicle B includes the elimination of the brake clearance time t₂Braking force increase time t₃And vehicle real-time speed v_b. Wherein t is₂、t₃Is defined by the structural travel of the braking system of the vehicle itself and can be considered as a constant parameter. And the speed v of the vehicle B_bThe critical distance is the decisive factor of the critical distance, and the higher the vehicle speed is, the longer the braking distance is, and the higher the speed is, the more obvious the braking distance is.

The environment-dependent quantity of influence factors includes A vehicle acceleration a_aAcceleration a of vehicle B_bAnd the relative speed v of the front and rear vehicles_r. The safe vehicle distance is based on the working condition that the safety is ensured by emergency braking when the front dangerous condition is met, because the braking force of the brake is very large, the magnitude of the ground braking force is determined by the road adhesion coefficient at the moment, namely a_bmaxIs- μ g, wherein a_bmaxThe maximum deceleration of the B vehicle, mu is the road adhesion coefficient, and g is the gravity acceleration. And the degree of deceleration a of the preceding A vehicle_aThe influence of the road surface adhesion coefficient is also received when the value is larger. When the relative velocity v_rThe safety distance is easier to maintain when it is smaller, when v_rWhen the front danger target appears, the danger target is shown to be larger, and measures should be taken in time to eliminate hidden danger. Therefore, the smaller the relative speed with respect to the target vehicle, the higher the road surface adhesion condition, and the shorter the critical vehicle distance can be maintained.

(3) Safe distance algorithm considering road surface adhesion coefficient

The condition of the road surface directly affects the safety of the vehicle running on the road, and the length of the braking distance is directly determined by the road surface adhesion coefficient. The influence factors of the road adhesion coefficient are mainly as follows: tire factors, road surface factors, and vehicle speed. The peak adhesion coefficient and the slip adhesion coefficient of each road surface type are shown in table 8.

TABLE 8 road surface adhesion coefficient of typical road surface

Obviously, different typical roads have great difference between the peak adhesion coefficient and the sliding adhesion coefficient, and have great influence on the braking distance and the braking deceleration of the automobile. Road surface adhesion coefficient mu and maximum braking deceleration a_bmaxThe following relation is satisfied:

a_bmax≤μg (34)

in the formula, a_bmaxThe maximum deceleration of the B vehicle, mu is the road adhesion coefficient, and g is the gravity acceleration. As can be seen from the above formula, the road surface adhesion coefficient has a great influence on the braking distance. When the braking strength is designed, in order to avoid the situation that the actual braking acceleration cannot reach the expected acceleration, when the road surface adhesion coefficient is small, the vehicle should be braked in advance to avoid collision.

As can be seen from equation (34), the maximum braking deceleration is related to the road surface adhesion coefficient, and the maximum braking deceleration does not exceed μ g. If the vehicle runs under a road surface with a relatively low adhesion coefficient such as snow and ice, the actual braking deceleration is likely to be collided with a front target without reaching the target deceleration, so the following relational expression is introduced in the calculation of the safe distance algorithm:

in the formula (I), the compound is shown in the specification,

for an estimate of the road adhesion coefficient in VCAN,

is the estimated value of the maximum braking acceleration of the vehicle B.

The formula (35) is substituted into the formulas (22), (28) and (33) in the safe distance algorithm, so that collision avoidance under different road surface adhesion coefficients can be realized, braking is performed in advance under the working condition of low adhesion coefficient, and the collision between two vehicles caused by insufficient braking force can be effectively avoided.

4.3.2 Collision time Algorithm

The Time-to-collision algorithm TTC (Time-to-collision) refers to the Time required for both vehicles to travel while keeping the current vehicle speed until a collision occurs, from the current Time. The application range is wide, and the standard and the regulation documents such as NHTSA, ISO, ECE and the like appear. Generally, when a vehicle A running on a road is static or runs at a constant speed, a first-order TTC is adopted; when the A vehicle brakes, a second-order TTC is adopted. The basic formula of TTC is shown in (36).

In the formula, a_rThe relative acceleration of the front and rear vehicles is indicated, and x represents the actual distance between the front and rear traveling vehicles.

The TTC is used as an index to develop an early warning algorithm, the acceleration differential of the vehicle is not considered, and the TTC algorithm is entered only when the vehicle speed of the vehicle B is greater than the vehicle speed of the vehicle A. In order to avoid collision, the front and rear vehicles should satisfy the following relation:

in the formula, a_aRepresenting the acceleration of the A vehicle; a is_bRepresenting the acceleration of the vehicle B.

(1) When the running front vehicle is in a static state or runs at a constant speed:

when the current vehicle is stationary or running at a constant speed, the TTC formula is a first-order TTC formula, as shown in formula (36), according to the above analysis.

Braking threshold value TTC thereof_th0Comprises the following steps:

TTC threshold TTC of first early warning_th1Comprises the following steps:

TTC threshold TTC of second early warning_th2Comprises the following steps:

(2) when the preceding vehicle is in variable speed driving:

if

When in use

When the vehicle A does not collide before stopping, the TTC braking threshold value TTC_th0The formula of (1) is:

TTC threshold TTC of first early warning_th1Comprises the following steps:

TTC threshold TTC of second early warning_th2Comprises the following steps:

when in use

When the vehicle A collides before stopping, the TTC braking threshold value TTC is set_th0The following should be considered:

TTC threshold TTC of first early warning_th1Comprises the following steps:

TTC threshold TTC of second early warning_th2Comprises the following steps:

② if

The TTC formula is the same as formula (41), and the threshold of the two warnings is the same as formula (42) and formula (43).

4.3.3 fusion strategy of safe distance algorithm and collision time algorithm

According to the overall architecture of the AEB control strategy, the invention adopts a scheme of combining a safe distance algorithm and a collision time algorithm, namely, a TTC algorithm is used in an early warning period, and a safe distance algorithm is used in a period to be braked. In the fusion strategy, the safe distance algorithm and the TTC algorithm are all judged all the time, and when the relative distance D of the two vehicles is larger than the minimum distance D of the two vehicles in the safe distance algorithm₀Judging the early warning by using a TTC algorithm, judging whether the automobile normally runs or performs graded early warning by using a TTC threshold value, normally running the automobile when the TTC value is large, and if the TTC value is large<＝TTC_th1Then send out the first early warningOf the TTC, if TTC is continuously reduced<＝TTC_th2If so, sending out a second early warning signal; when D is present<D₀The method directly outputs expected deceleration and automatically and emergently brakes the automobile, and uses a safe distance algorithm to judge and output the expected deceleration.

4.3.4 dangerous object discrimination algorithm

In the driving process, the AEB system detects the motion state of a front target by using a millimeter wave radar and a camera, the motion state of the self-vehicle sensor is monitored, and the relative distance between the self-vehicle sensor and a front vehicle is kept at a safe level through AEB control. Since the chosen millimeter wave radar is used for horizontal detection in a sector area with a horizontal view angle of-50 to +50, it is clear that not every forward object detected by the radar is at risk of collision. Under the condition that other automobile auxiliary ranging sensors or data chains are not adopted and only millimeter wave radars and video sensors are adopted, the longitudinal distance and the transverse distance of a vehicle need to be calculated when the vehicle runs on different lanes or roads without road sign lines, so that a radar ranging coordinate system comprising a driving vehicle, a vehicle-mounted millimeter wave radar and a front target vehicle needs to be established.

The millimeter wave radar is installed in the center position of the foremost end of the vehicle, the position where the radar is located is the origin of coordinates (0,0), a straight line which is parallel to the ground through the origin of coordinates and coincides with the driving direction of the driving vehicle is an x-axis, an axis which is parallel to the ground through the origin of coordinates and perpendicular to the driving direction of the driving vehicle is a y-axis, and a radar ranging coordinate system is established.

If the vehicle A and the vehicle B always run along the x axis, the vehicle A is not in the running range of the vehicle B due to the overlarge y value, so that the two vehicles cannot collide with each other. However, the vehicle A is actually in the radar detection range of the vehicle B, so a strategy is made to screen dangerous targets to avoid false alarms.

And when the radar detects a front target, selecting a point closest to the radar of the vehicle B as a reference point. Vehicle a selects the point in the lower left corner as a reference. The radar can detect the relative distance l between the radar and a front target vehicle and the included angle alpha between the connecting line of the reference point and the radar and the x axis, and the front target vehicle can be regarded as a potential dangerous vehicle when the parameters meet the following relations:

in the formula: b-vehicle width; h is₀-lateral safety distance.

h₀If the value of (A) is too small, the safety of the automobile can be directly influenced, and the rear-view mirror can possibly collide with the automobile A; h is₀If the value is too large, a false alarm can be caused, and h is taken out through the comprehensive consideration of AEB on the feeling of a driver₀Is 0.4 m.

4.4AEB hierarchical control System

Related research on AEB vehicle dynamics control can be divided into two categories according to its control structure: direct control systems and hierarchical control systems.

4.4.1 Upper-level control algorithm of hierarchical control system

The task of AEB dynamic control is to decide whether to adopt braking to the vehicle and the corresponding braking intensity according to the relative motion state of the front target vehicle and the vehicle after determining the most dangerous target. The current algorithm which is more commonly used is to adopt a control strategy of graded braking for a vehicle, and the braking strength is divided into two levels or even three levels. When the speed of the vehicle is low, collision avoidance can be realized in a short time through low braking deceleration; when the vehicle speed is high, collision avoidance is difficult to achieve with low braking strength within a limited time, and the problem can be solved by increasing the braking strength.

The target braking strength researched and designed by the invention is related to the speed of the vehicle and the road adhesion coefficient.

The invention adopts larger braking force to brake when the braking intensity is set to be more than 40km/h, and the braking intensity is set to be 0.8 g; setting the braking strength to be 0.5g at the speed of 20km/h to 40 km/h; the braking strength was set to 0.3g at 20Km/h or less. The strategy of target brake intensity grading ensures that the total time of the braking process is less than 2.5s when the braking process is less than 40km/h, and accords with the psychological expectation of a driver; and ensures the collision avoidance of the automobile with great braking strength when the speed per hour exceeds 40 km/h. However, since the braking strength is affected by the road surface adhesion coefficient, the braking strength may not be obtained when the road surface adhesion coefficient is small. The estimation of the road adhesion coefficient is realized by acquiring information of tires or information of a road by using a sensor, transmitting the information to a VCU (vehicle control unit) through a VCAN (video camera and audio unit), and further processing the information. The AEB controller obtains this parameter road adhesion coefficient through the VCU and makes further decisions to optimize the safe distance algorithm. Further, in consideration of the formula (35), when the maximum braking deceleration does not reach μ g, the braking intensity is set to μ g.

4.4.2 lower-layer control algorithm of hierarchical control system

The task of the lower layer controller algorithm is to control the actual deceleration to be consistent with the expected deceleration, namely the AEB control algorithm calculates the expected deceleration, then the difference value of the actual deceleration measured by the sensor of the automobile is input into the lower layer control algorithm, the feedback closed loop is completed, and the difference value of the actual deceleration and the expected deceleration gradually approaches zero.

And (4) algorithm flow of a lower-layer controller. The expected deceleration is calculated by a safe distance algorithm, then the difference value of the expected deceleration and the actual deceleration measured by a sensor of the automobile is input into an algorithm of a lower layer controller and then input into a longitudinal dynamic system to complete a feedback closed loop, so that the difference value of the two approaches to zero.

Because the expected acceleration of the vehicle is a fixed value output by the safe distance model, the control task is simpler, and the influence of the vehicle and environmental factors is less, the invention adopts a PID (proportion integration differentiation) regulation mode to carry out an upper-layer control algorithm. The most important part in using the PID controller is the adjustment of three parameters of proportion, integral and differential.

In control systems employing PID controllers, determination of the proportional, integral and derivative coefficients is of paramount importance. Since the P, I, D coefficients have different influences on the control effect of the controlled object, a relatively ideal control effect can be achieved by setting the numerical values of the three coefficients. Compared with other even more complex control methods, generally speaking, the same control effect can be achieved theoretically by reasonably selecting the three parameters. The parameters of the PID controller are usually tuned using empirical methods, which do not require additional algorithms, and are simple and efficient. Firstly, the method is tried and worked out according to empirical values of three coefficients of an AEB system under a PID control method, and then according to the influence of each parameter of a controller on a lower layer controller, the method is modified according to the influence of the three parameters on a new system while observing the operation of the system until an ideal state is reached.

The underlying control is modeled using Simulink. The difference between the expected acceleration and the actual acceleration is input into a PID module, and the result calculated by PID is output to the automobile dynamic system of CarSim. Where the source of the desired acceleration is associated with a safe distance algorithm and the true acceleration is derived from the measurement of acceleration in CarSim.

4.5AEB brake force distribution strategy

The AEB brake distribution strategy of the electric vehicle mainly comprises two parts: distributing braking force of front and rear shafts; and distributing the motor braking force and the hydraulic braking force. The present invention mainly studies these two allocation strategies.

4.5.1 front and rear axle brake force calculation

According to the braking force distribution requirement of the invention, in order to reduce the calculation amount, the whole automobile is regarded as a rigid body, and when the automobile is braked along a horizontal road surface, the stress condition is met.

For electric vehicles, F_xbf，F_xbrTotal braking force of front and rear wheels:

in the formula: f_xbrf，F_xbrrRespectively is the regenerative braking force of the front and the rear shafts; f_xbmf，F_xbmrRespectively, the front and rear axle mechanical braking force.

Order to

Where z is the braking intensity.

F_req＝F_xbf+F_xbr＝mg·z (52)

In the formula:

L_athe distance from the center of mass of the automobile to the center line of the front axle is m;

L_bthe distance from the center of mass of the automobile to the central line of the rear axle is m;

l is the distance between the front axle and the rear axle of the automobile and is in the unit of m;

m is the mass of the automobile in kg;

h_gis the height of the center of mass of the automobile, and the unit is m;

simultaneous equations (50), (51), and (52) obtain the axle-to-axle brake force distribution relationship when the front and rear wheels are simultaneously locked:

in the formula, F_xbf、F_xbrFront and rear axle braking forces respectively; f is the maximum braking force.

The coupled type (52) and (53) obtain the braking force of the front axle:

the braking force of the rear axle is obtained from equation (53):

front and rear axle relative braking force F_f、F_rRespectively as follows:

the relationship between the relative braking forces of the front and rear axles of the research vehicle according to the invention can be derived from equation (57).

The relationship between the wheel braking forces of the front axle and the rear axle satisfying the curve I is as follows:

4.5.2 calculation of Motor braking force and Hydraulic braking force

For an electric automobile, when the automobile is braked, a motor is in a power generation mode, and negative torque acting on an output shaft of the motor is converted into braking force on wheels through a transmission, a main speed reducer and a differential mechanism. The maximum braking force which can be generated on the front motor and the rear motor is respectively as follows:

in the formula (I), the compound is shown in the specification,

T_mfis the negative torque of the output shaft of the front shaft motor;

T_mris the negative torque of the output shaft of the rear shaft motor;

F_mfthe maximum braking force output by the front axle motor;

F_mrthe maximum braking force output by the rear axle motor;

k_SOCis an energy storage influencing factor.

The safety and service life of the energy storage system must also be considered due to regenerative braking control of the electric machine. When the SOC of the storage battery is lower, the storage battery is allowed to be charged, and when the SOC of the storage battery is higher, the storage battery is forbidden to be charged, so that the service life of the storage battery is prevented from being shortened. It is therefore necessary to introduce the energy storage influencing factor k in this case_SOCAnd setting the value to satisfy the following relationship:

the invention selects a maximized recovered braking force control strategy, namely, the motor braking force is preferentially selected under the condition that the rotating speed of a motor and the SOC of a storage battery meet the requirement of charging in the braking process, and the residual braking force is supplemented by the friction braking force under the condition that the motor braking force is not enough to reach the braking force of a required braking executing mechanism, wherein the calculation formula of the friction braking force is as follows:

F_f＝F_fmax-F_mf (62)

F_r＝F_rmax-F_mr (63)

4.5.3 brake force distribution strategy

In the braking process of the vehicle, the braking executing mechanism realizes the deceleration braking of the vehicle through the coordination work of the friction brake and the motor. According to the AEB braking force distribution strategy designed by the invention, Tm _ max is the maximum braking torque of a motor, Tf _ req is the required braking torque of a front wheel, Tr _ req is the required braking torque of a rear wheel, and SOC is the nuclear power state of a power supply. When the brake actuating mechanism receives the brake signal, the maximum brake torque which can be sent out by the motor is firstly compared with the required brake torque of the front wheel. After the vehicle parameters are substituted into the given vehicle parameters, the required braking torque of the front wheels is larger than the braking torque of the rear wheels through the analysis of a formula (57), namely Tf _ req is larger than Tr _ req. The parameters of the front motor and the rear motor are the same, and the following three conditions can be divided according to the braking torque: the maximum braking torque of the motor can meet the requirement of braking force of front and rear shafts at the same time, namely Tm _ max is less than or equal to Tr _ req; in order to maximize the recovery of braking energy, the front axle and the rear axle are braked by the motors at the moment to complete the whole braking process of the vehicle. The maximum braking torque of the motor can only meet the requirement of the braking force of the rear axle, but the maximum braking torque of the motor cannot meet the requirement of the front axle, namely Tr _ req is larger than Tm _ max and is not larger than Tf _ req; an insufficient front axle braking force is provided by the front axle hydraulic braking force in this case; the maximum braking torque of the motor cannot meet the braking force requirement of any shaft, namely Tm _ max is larger than Tf _ req; at the moment, the front and rear axle brakes are all adjusted to supplement the residual expected braking force, and the deceleration braking of the vehicle is jointly completed through the coordination of the motor and the brakes.

Furthermore, the evaluation is required to first evaluate the state of charge of the power supply system. When the SOC of the power supply system is more than or equal to 0.9, the battery is in a state close to full charge, so that the service life of the battery is prolonged, the battery is prevented from being overcharged, regenerative braking is not carried out at the moment, and the expected braking force is completely provided by hydraulic braking force, so that the emergency braking process of the vehicle is completed; when the SOC of the power supply is more than 0.8 and less than 0.9, the motor and the hydraulic pressure are simultaneously braked under the influence of an energy storage influence factor in a formula (61); when the SOC of the power supply is less than 0.8, the battery can be charged when the battery is in a power-down state, and pure motor braking is performed at the moment.

5. Simulation analysis and software development of AEB control strategy of electric vehicle

5.1 Emulation analysis scheme for AEB control strategy

The AEB control algorithm in the invention contains factors of road adhesion coefficients, and in order to verify the effect of the control strategy under different road adhesion coefficients, the invention selects four different typical roads: the dry asphalt, the wet asphalt, the snow and the ice surface are used as test pavements, eight working conditions specified in C-NCAP are used for testing, and safety distance algorithm, TTC and safety distance fusion algorithm and optimized TTC and safety distance fusion algorithm are compared, so that the safety and comfort aspects are mainly evaluated. In order to avoid confusion, the safe distance algorithm is called algorithm one, the TTC algorithm is called algorithm two, the TTC and safe distance fusion algorithm is called algorithm three, and the TTC and safe distance fusion algorithm for optimizing the target braking strength, setting the early warning time and considering the road adhesion coefficient is called algorithm four on the basis.

According to the literature and data analysis, the evaluation indexes of the AEB simulation test of the invention are as follows: firstly, the speed reduction rate; maximum deceleration; the deceleration rate change rate (impact rate); fourthly, the minimum distance between the two vehicles is obtained; and fifthly, early warning time.

The speed reduction rate is the percentage of reduction of the speed in actual collision and the relative speed of two vehicles before braking, and the larger the speed reduction rate is, the better the collision avoidance effect is. The speed reduction rate of 100% indicates complete collision avoidance, and the speed reduction rate of 0 indicates that the automatic braking does not play a collision avoidance role. The velocity reduction rate λ is formulated as:

in the formula, V_re-the collision velocity;

V_ri-braking the relative speed of the two vehicles in front.

The acceleration peak value and the impact degree peak value both belong to objective evaluation indexes of the riding comfort of the vehicle. The objective evaluation method is to evaluate the performance of a vehicle based on data of a certain characteristic parameter of the vehicle as an evaluation criterion. The objective evaluation method has the advantages over the subjective evaluation method in that parameters required in the evaluation index can be obtained through testing, and the evaluation index is not influenced by personal habits of evaluators. The impact degree refers to the change rate of the longitudinal acceleration of the whole vehicle, and the smaller the impact degree in the automatic braking process is, the better the smoothness of the mode switching process is, and the better the comfort is.

The expression of the longitudinal impact of the automobile is as follows:

in the formula: j-longitudinal impact of the vehicle (m/s)³)；

a-longitudinal acceleration (m/s) of the whole vehicle²)；

v-longitudinal speed (m/s) of the whole vehicle.

The minimum distance between the front and rear vehicles is an evaluation index which can reflect the safety of the AEB and can reflect the comfort. If the minimum distance between the front vehicle and the rear vehicle is 0, the two vehicles collide, which indicates that AEB does not completely avoid collision; if the minimum distance between the front vehicle and the rear vehicle is too small, panic can be caused to a driver, and collision can be caused by the influence of other factors under other working conditions; if the minimum distance between the front and rear vehicles is too large, the driver is likely to feel that the AEB function is meaningless and normal driving of the driver is affected. Currently mainstream AEB systems require a minimum distance between 0.5m and 1.5 m.

The early warning time of the AEB system is not too early or too late, the early warning can cause a false alarm, the audible and visual alarm can influence the normal driving of a driver, and the early warning too late can cause the driver to be unreachable. The proper early warning time can give the driver a better functional experience.

5.2 simulation testing and analysis

And (3) carrying out simulation test on three working conditions of the front vehicle, namely, the front vehicle is static, the front vehicle is at a constant speed and the front vehicle decelerates. In the AEB simulation test, two vehicles are required to be positioned on the same straight road and in tandem, wherein the rear vehicle carries the AEB control strategy researched by the invention. According to the specification of AEB test in C-NCAP, the specific division can be 8 working conditions, as shown in Table 9.

TABLE 9 AEB project test conditions in C-NCAP

In the stationary working conditions (CCRs) of the front vehicle, the initial relative distance between the two vehicles is set to be 50 meters, the front vehicle is stationary, and the rear vehicle approaches the front vehicle at the speeds of 20km/h, 30km/h and 40km/h respectively; in the constant speed working condition (CCRm) of the front vehicle, the initial relative distance between the two vehicles is set to be 50 meters, the front vehicle drives forwards at the speed of 20km/h, and the rear vehicle approaches to the front vehicle at the speeds of 30km/h, 45km/h and 65km/h respectively; in the preceding vehicle deceleration condition (CCRb), the preceding vehicle and the following vehicle all run at a speed of 50km/h, and then the preceding vehicle suddenly brakes suddenly at a deceleration of 4m/s2, wherein the initial relative distance between the two vehicles is 12 meters and 40 meters.

And testing the AEB control algorithm and strategy through a CarSim/Simulink combined simulation platform. The test contents include 4 algorithms, 8 working conditions contained in C-NCAP, and tests were performed on 4 typical roads.

The speed reduction rate of each algorithm under different road surfaces.

The conditions specified in C-NCAP. Because the road surface adhesion coefficient of snow and ice is very low, the deceleration of the front vehicle 4m/s2 in two working conditions cannot be realized, so that the simulation test is only carried out under the working conditions of CCRs and CCRm under the snow and ice road surfaces, and the simulated working conditions are as follows: eight working conditions of dry asphalt pavement and wet asphalt pavement, six working conditions of snow surface and ice surface, and 28 working conditions in total. Under the dry asphalt pavement, except one condition under the deceleration working condition of the front vehicle, 4 algorithms under other working conditions can realize complete collision avoidance, but the collision avoidance degrees under other typical pavements are different. The algorithm IV can be well suitable for different road surfaces, when the algorithm is used, the complete collision avoidance is not realized only under the CCRs-3 working condition under the ice surface, the speed reduction rate is 95.602%, and the complete collision avoidance is realized under the other working conditions. The other three algorithms do not realize complete collision avoidance on snow and ice surfaces, the speed reduction rate is low, the speed reduction rate of the other three algorithms on the snow road surface is 10-20%, the speed reduction rate of the other three algorithms on the ice surface is 5-15%, and the collision avoidance effect is not obvious.

The minimum relative distances of the four algorithms under the dry asphalt and wet asphalt pavements in all test scenes of the C-NCAP are shown in a table 10, wherein x represents the collision of two vehicles, the minimum relative distance of the two vehicles under the dry asphalt pavement is represented by (i) the minimum relative distance of the two vehicles under the wet asphalt pavement is represented by (ii) the minimum relative distance of the two vehicles under the wet asphalt pavement. The conditions of two kinds of road surfaces, namely dry asphalt and wet asphalt, are compared only because the conditions of two vehicles colliding on the snow field and the ice surface are many and are not convenient to compare. It can be seen from the table that the first algorithm and the second algorithm are relatively conservative when the road adhesion coefficient is large, and sometimes the distance is 3 meters or more than 4 meters after parking, and then the third algorithm provided by the invention is relatively aggressive when the dry asphalt road is parked, can be parked at a relatively ideal distance, and is not equal to 0.2m to 0.8m, but the third algorithm cannot adapt to the change of the road adhesion coefficient. The algorithm IV can well adapt to the change of the road surface, and the minimum vehicle distance is between 0.6 and 1 meter, so that the ideal minimum front and rear vehicle distance is obtained.

TABLE 10 minimum relative distance between two cars

The fourth algorithm can be excellent in two evaluation indexes of the speed reduction rate and the minimum relative distance between two vehicles, mainly because the influence of the road adhesion coefficient is considered, but is not considered in other algorithm strategies. The fourth algorithm requires that the automobile can obtain the road adhesion coefficient in real time, and then the parameters are substituted into the algorithm, when the road adhesion coefficient is judged to be small, the system can give an early warning and automatically brake in advance, and the collision between the front automobile and the rear automobile caused by overlong braking time and braking distance due to the small road adhesion coefficient is avoided. When the automobile runs on a real road, a certain error exists in real-time estimation of the road adhesion coefficient, the road adhesion coefficient fluctuates around a real value during estimation of the road adhesion coefficient, but the size of the road adhesion coefficient can still be roughly estimated, so that the AEB system has practical significance in carrying the optimized algorithm.

The acceleration peak and jerk peak of the four algorithms are shown in table 11, and the simulation results are shown in table 12. Because the target brake intensity is set in the upper controller, the automobile can be braked without using the maximum brake force when the speed of the automobile is less than 40km/h, and the peak value of the acceleration and the peak value of the impact degree are reduced under three working conditions that the speed of the rear automobile is less than 40 km/h. The test data shows that the acceleration peak value can be reduced by about forty percent, and the reduction rate of the impact peak value is different from 8 percent to 45 percent. Vehicle speeds below 40km/h are usually present in urban road sections, the driver comfort can be increased by reducing the acceleration peaks and jerk peaks, and the driver discomfort caused by the AEB function can be avoided as much as possible.

TABLE 11 acceleration Peak and jerk Peak for the four algorithms

TABLE 12 comparison of simulation results of acceleration peak and jerk peak for different algorithms

Compared with a safe distance algorithm, a TTC algorithm and a fusion algorithm, the optimized algorithm has better performance in both the acceleration peak value and the impact degree peak value because the target braking strength is set in the upper-layer controller. When the speed is lower, the emergency braking can be realized with lower force, and the impact of the automobile on a driver during braking is avoided while the safety is ensured.

The warning durations of the four algorithms in the test are shown in table 13. As for the warning time, there is no clear specification in AEB test in C-NCAP, whereas in the evaluation of AEB & FCW by E-NCAP (Ohondrian New Car safety evaluation Settlement), it is specified that the warning time for the driver should not be less than 1.3 seconds. Secondly, the early warning time under different working conditions should be kept consistent as much as possible, so that the best warning effect of the driver can be achieved. As can be seen from the table, the safe distance algorithm is not stable under the working conditions that the front vehicle is static and the front vehicle is at a constant speed, the early warning effect is not ideal, the TTC-containing algorithm can well control the early warning time, and the driver can feel better. However, the four algorithms do not perform well under the last two working conditions, and especially under the working condition of CCRb (12m), the early warning time is short. After the optimization of the control algorithm, the problem can be well remedied, namely when the current vehicle starts to brake, the threshold value of TTC can be properly increased, so that the early warning duration can be properly increased, the early warning time can be controlled to be more than 1.4s under the working condition of C-NCAP, and the requirement of regulations in E-NCAP can be met.

Watch 13 duration of warning

From the test results, the researched novel control strategy, namely the optimized TTC + safe distance algorithm and strategy can basically realize collision avoidance in the AEB test scene specified in the C-NCAP, and the algorithm strategy has great improvement on the classical algorithm on five indexes of speed reduction rate, maximum deceleration, deceleration change rate, minimum distance between two vehicles and early warning time, improves the safety of the vehicle and the comfort of a driver, and can be well adapted to the road surface with low road surface adhesion coefficient. The test result basically meets the expected design requirement, and has certain practical application value.

5.3 AEB control strategy software development

After the AEB control strategy is verified, software development of the control strategy is required to facilitate testing of the algorithmic strategy on real vehicles or other hardware.

5.3.1 software development tool

The design and development of the electronic control unit of the automobile are usually realized in a serial mode, the design period is long, the cost is high, the workload of a programmer is greatly increased when the design and development contents are more, and the simulation analysis on a hardware platform is difficult. When models, algorithms and strategies are designed by Simulink, a Real-time code generation toolkit RTW (Real-time works) in Simulink can be used to improve development efficiency.

5.3.2 code Generation

A software development method based on Simulink/RTW generally comprises the following design processes: firstly, designing an algorithm and a model by using Simulink graphic modeling software, carrying out simulation verification, and generating codes by using RTW after verifying that the algorithm and the model can meet the design requirements.

The invention relates to a software development process of an AEB control strategy. Firstly, according to the requirements and function analysis of an electric vehicle AEB system, an AEB control strategy is researched and realized on Simulink, meanwhile, a relevant model is established, and a simulation test is carried out on the control strategy by using an electric vehicle AEB simulation test platform. After the test result reaches the expected effect, a code automatic generation tool RTW is used for generating C/C + + codes, the codes are transplanted into Visual Studio for debugging, and corresponding application layer interfaces and hardware interfaces are established.

In the RTW transcoding process, code conversion needs to be set in a tab, simulation time is set to inf, and Fixed-step is selected from server options for compiling, so that errors cannot be reported when C/C + + codes are generated. The code was viewed using Visual Studio after it was generated. The generated files mainly comprise four types: header files of control policies, system header files, program files of control policies, and source files of parameters. The head file of the algorithm comprises a function and a data interface statement, and a function library corresponding to the program file can be found during compiling. After the Simulink is converted into the C code, in order to ensure that the C code can be successfully debugged, a system header file needs to be called to ensure normal operation of the code, so that a corresponding header file should be found in the system and placed in a directory of a program file. The program file of the control strategy mainly comprises all the control strategies converted by Simulink and a part of models. The source file of the parameters includes all algorithm-related parameter values, and includes all constants, products and values of switch in simulink.

The Simulink program of the algorithm needs to be connected with CarSim during simulation, so that the simulation provides models of automobile dynamics, a whole automobile, roads and the like. The procedure of the CarSim module is not converted when Simulink is converted into C language, resulting in many interfaces left, which can be converted into application layer interfaces in hardware. The application layer interface definition is shown in table 14.

TABLE 14 application layer interface definitions for software

In the section of the invention, code generation of C language is carried out, and in subsequent research work, hardware-in-loop debugging is carried out on the code. And then embedding the code into an AEB controller or an ADAS controller of the real vehicle for real vehicle debugging to further verify the validity of the control strategy.

In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When used in whole or in part, can be implemented in a computer program product that includes one or more computer instructions. When loaded or executed on a computer, cause the flow or functions according to embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL), or wireless (e.g., infrared, wireless, microwave, etc.)) manner. The computer readable storage medium can be any available medium that can be accessed by a computer or a data storage device including one or more available media integrated servers, data centers, and the like. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.

The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.

Claims (10)

1. The method for establishing the electric automobile brake system model is characterized by comprising the following steps of:

step one, establishing a simulation model of an electric automobile AEB;

step two, establishing interfaces of CarSim and Simulink software;

step three, building a braking system model based on Simulink;

and step four, verifying the simulation model.

2. The method for building the braking system model of the electric vehicle according to claim 1, wherein in the step one, the building of the simulation model of the AEB of the electric vehicle comprises:

an AEB simulation model is built in the CarSim, and the AEB simulation model comprises a whole vehicle model, a perception sensor model and a road model;

(1) whole vehicle model

Establishing a whole vehicle model, and inputting actual whole vehicle parameters; establishing two whole vehicle models, wherein one is an electric vehicle carrying an AEB function; the other is a front vehicle, namely a locking target vehicle of the electric automobile;

for a whole vehicle model, aiming at an electric vehicle, a whole vehicle model is built in a CarSim by using sample vehicle parameters;

inputting the vehicle parameters into a vehicle model of CarSim, and establishing the vehicle model; the whole vehicle model comprises the whole vehicle mass, the distance from a front shaft to a rear shaft to a center of mass, a wheel base, the center of mass height and the tire rotational inertia parameters; wherein the mass of the whole vehicle is modified along with the actual working condition;

the speed and the acceleration are considered during modeling, and other structural parameters do not make special requirements; using a C-Class model provided by CarSim, and setting vehicle structure parameters, vehicle body appearance and tire parameters;

(2) perception sensor model

The radar ranging sensor is used for obtaining a main sensor of the relative distance, the relative speed and the acceleration of the vehicle from the front vehicle, and the camera is used for identifying a front object and assisting the radar in ranging; arranging a radar and a camera for observing the running state of a vehicle in a forward collision early warning system, and selecting and arranging the millimeter wave radar and the camera according to an AEB system architecture; according to the parameters of the radar and the camera, building a model of the millimeter wave radar and the camera in the CarSim; for the arrangement of the sensor, according to the design requirement, the millimeter wave radar is hidden in the center of the front bumper, and the camera is arranged behind an inner rearview mirror in the vehicle;

(3) road model

The road model is established, the road model comprises the settings of road surface gradient, road surface adhesion coefficient, rolling resistance coefficient and environmental parameters, and the model can be adjusted differently according to different working conditions; according to the verification conditions of the safe distance model, the road condition is specified to be straight-line driving, the front vehicle and the rear vehicle are positioned on the same lane, wherein the front vehicle is a target vehicle, and the rear vehicle is a test vehicle loading the designed AEB system.

3. The method for establishing the braking system model of the electric vehicle according to claim 1, wherein in the second step, the interface establishment of the CarSim and Simulink software comprises:

in the process of carrying out the joint simulation, the CarSim is used for providing data such as an automobile dynamic model, an event, road surface parameters and the like; the establishment and optimization of a safe distance algorithm are completed in Simulink, so that the establishment of an interface between two pieces of software must be completed;

using a special Simulink interface in CarSim and using a Send to Simulink instruction to output parameters of the automobile, the road surface model and the front and rear automobiles built in CarSim into Simulink; constructing information of a front vehicle in CarSim and determining output parameters of a front target vehicle and output parameters of the vehicle; the input parameter of the vehicle is the brake pressure IMP _ PBK of 4 cylinders, and the brake pressure is controlled through a brake force distribution strategy; the output parameters are the control pressure Pbk _ Con of the vehicle, the centroid speed Vx _ SM of the vehicle, the acceleration Ax of the vehicle, the relative distance Dis 1_1 with the front vehicle and the relative speed SpdS1_1 with the front vehicle respectively; the motion state of the front vehicle is calculated by measuring the relative distance and the relative speed measured by the radar and measuring the speed and the acceleration of the vehicle, and then the motion state is input into a safe distance algorithm.

4. The method for building the braking system model of the electric automobile according to claim 1, wherein in step three, the building of the braking system model based on Simulink comprises:

building a braking system model for simulating a braking coordination time and a braking force rising curve in a braking process; the braking system comprises an I-Booster driving structure, a brake pedal, a braking pipeline, an ESP/electromagnetic valve, a brake wheel cylinder and a power supply;

the structure of the driving device of the brake system consists of a motor-control unit, a brake master cylinder, a deep drawing steel plate shell and an interface; the brake pipeline is a hydraulic pipeline and transmits braking force by taking liquid as a medium; the ESP is used for adjusting the braking force of the brake wheel cylinder by controlling the pressure reducing valve and the pressure increasing valve after receiving the superior signal;

three important factors influencing braking force and braking coordination time are selected to establish a hydraulic braking model: firstly, a hydraulic pipeline; a second electromagnetic valve; and thirdly, braking the wheel cylinder.

5. The method for building the braking system model of the electric vehicle according to claim 1, wherein in step three, the building of the braking system model based on Simulink further comprises:

(1) hydraulic pipeline model

The dynamic characteristic of the brake oil pressure of the hydraulic pipeline is simulated by establishing a hydraulic pipeline model, and a first-order inertia link is adopted for description, as shown in a formula (3):

wherein P(s) represents the actual hydraulic brake fluid pressure, MPa; p₀(s) represents a target oil pressure of the brake, MPa; tau is a constant used for reflecting the dynamic characteristic of the brake and is obtained through a hydraulic pipeline test;

(2) electromagnetic valve model

The switching time of the electromagnetic valve can directly influence the reaction time of the brake system; because the modeling of the hydraulic braking system only considers the braking force and the braking coordination time, only the time delay characteristic of the electromagnetic valve model is considered when the electromagnetic valve model is established; carrying out related tests on the switching time of the electromagnetic valve under the working conditions of load and no load, wherein the switching time is usually 1ms to 10 ms; because the time is short and the difference of the switching time between different electromagnetic valves is small, simulation research is performed corresponding to corresponding time according to the number and the state of the electromagnetic valves; the system comprises two electromagnetic valves, a pressure increasing valve and a pressure reducing valve, and the total delay time of the electromagnetic valves is determined to be 10 ms;

(3) brake wheel cylinder model

In the braking process, the pressure of the hydraulic pipeline presses the piston of the wheel cylinder, and the piston pushes the brake block to tightly press the brake disc to stop the wheel from running; the brake model can be simplified into a piston dynamics model, and the mechanical characteristic of the pressure input of the brake wheel cylinder to the brake torque output is simulated;

through the analysis of the stress in the process of the piston movement, a mechanical relation shown as an equation (4) can be obtained according to Newton's second law:

in the formula, P represents a wheel cylinder input pressure, MPa; a represents a piston cross-sectional area, m²(ii) a m represents the moving mass equivalent to the piston of the wheel cylinder, kg; k_pRepresents the brake stiffness, N/m; c_pRepresenting a damping coefficient; f₀Represents the dry friction of the system, N; x_pRepresents a wheel cylinder piston displacement, m;

at this time, the positive pressure of the piston acting on the brake disc is:

by applying laplace transform to equation (4), we can obtain:

in engineering practice, the piston is stressed in balance because the piston and the brake block are basically pressed on the brake disc all the time, i.e. the piston is stressed in balance

The effect of dry friction is small and can be neglected, and the following relation is given:

from equations (5), (6), (7) and (8), the positive pressure acting on the brake disc can be:

the braking torque at the wheels can be expressed as:

in the formula, r₁Represents the effective friction radius, m; η represents the brake effectiveness factor;

the brake model can be simplified into a second-order inertia link described by an equation (9) to carry out analog simulation; using a PID control method for the actual braking force to enable the braking force to gradually reduce the deviation from the target braking force;

and substituting parameters according to the analysis of the model, establishing a simulation model of the brake system based on Simulink, wherein the simulation model consists of electromagnetic valve delay, PID control, a hydraulic pipeline first-order system and a brake wheel cylinder second-order system.

6. The method for building the braking system model of the electric vehicle according to claim 1, wherein in step four, the verifying the simulation model comprises:

because the system uses a simplified model, the structure and the characteristics in the system do not need to be considered, and the influence on the braking distance and the braking coordination time is only considered, whether the simulated braking effect is consistent with the braking effect of the real vehicle or not is only verified, so that the braking time and the braking distance are real and reliable when the automatic emergency braking is triggered in the simulation; the effect verification scheme based on the emergency braking in Simulink and CarSim combined simulation is as follows:

(1) the method comprises the following steps that a built CarSim/Simulink electric automobile simulation platform is used, and the whole automobile simulation parameters in the CarSim are consistent with the real automobile parameters; wherein the parameters comprise the finished vehicle service quality and tire parameters; the environmental settings are as consistent as possible, including road adhesion coefficient and gradient;

(2) and (4) simulating according to the real vehicle test conditions, and comparing and analyzing the real vehicle brake distance and brake time with the Iboost brake system.

7. An electric vehicle brake system model building system to which the electric vehicle brake system model building method according to any one of claims 1 to 6 is applied, characterized by comprising:

the simulation model establishing module is used for establishing a simulation model of the AEB of the electric automobile;

the interface establishment module is used for establishing interfaces of CarSim and Simulink software;

the brake system model establishing module is used for establishing a brake system model based on Simulink;

and the simulation model verification module is used for verifying the simulation model.

8. A computer device, characterized in that the computer device comprises a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to carry out the steps of:

(1) establishing a simulation model of an electric automobile AEB;

(2) establishing interfaces of CarSim and Simulink software;

(3) building a braking system model based on Simulink;

(4) and verifying the simulation model.

9. A computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of:

(1) establishing a simulation model of an electric automobile AEB;

(2) establishing interfaces of CarSim and Simulink software;

(3) building a braking system model based on Simulink;

(4) and verifying the simulation model.

10. An information data processing terminal, characterized in that the information data processing terminal is used for realizing the system for establishing the braking system model of the electric vehicle according to claim 7.

Images