CN115503709A

CN115503709A - Vehicle speed control method, device, medium, equipment and vehicle

Info

Publication number: CN115503709A
Application number: CN202211355305.5A
Authority: CN
Inventors: 白羽鹤; 匡齐; 张利红; 孟美荣; 张高翔
Original assignee: Uisee Shanghai Automotive Technologies Ltd
Current assignee: Uisee Shanghai Automotive Technologies Ltd
Priority date: 2022-11-01
Filing date: 2022-11-01
Publication date: 2022-12-23

Abstract

The present disclosure relates to a vehicle speed control method, apparatus, medium, device, and vehicle, the method including: acquiring real-time state data of a vehicle; the real-time state data comprises real-time estimated quality, current vehicle speed, expected vehicle speed and road condition information; the road condition information includes: the current road gradient, road adhesion coefficient and road rolling resistance coefficient; determining a target torque based on the real-time status data; the target torque is used to adjust the vehicle from the current vehicle speed to the desired vehicle speed. According to the technical scheme, the target torque corresponding to vehicle speed control is determined by combining the real-time estimated mass, the current vehicle speed, the expected vehicle speed and road condition information of the vehicle, such as the current road gradient, the road adhesion coefficient, the road rolling resistance coefficient and other real-time state data, the vehicle speed can be accurately controlled in time according to different vehicle condition road conditions, namely the response speed is high, and the following performance is good.

Description

Vehicle speed control method, device, medium, equipment and vehicle

Technical Field

The present disclosure relates to the field of autonomous vehicle technology, and in particular, to a vehicle speed control method, apparatus, medium, device, and vehicle.

Background

Along with the rapid development of the electric automobile industry and the promotion of users to electric automobile functional requirements, the vehicle automatic driving system is applied to pure electric vehicles more and more for alleviate navigating mate's driving strength, improve travelling comfort, variety and the practicality of driving.

At present, in a control method applied to an electric vehicle, a vehicle speed control method generally determines a target torque and implements vehicle speed regulation control based on a current vehicle speed, an expected vehicle speed and other preset parameters, so that vehicle speed control cannot be performed according to conditions such as actual vehicle conditions and road conditions, and response speed of vehicle speed control is slow and followability is poor.

Disclosure of Invention

In order to solve the technical problems described above or at least partially solve the technical problems, the present disclosure provides a vehicle speed control method, device, medium, apparatus, and vehicle.

The present disclosure provides a vehicle speed control method of an autonomous vehicle, including:

acquiring real-time state data of a vehicle; the real-time state data comprises real-time estimated mass, current vehicle speed, expected vehicle speed and road condition information; the traffic information includes: the current road gradient, road adhesion coefficient and road rolling resistance coefficient;

determining a target torque based on the real-time status data;

the target torque is used to adjust the vehicle from the current vehicle speed to the desired vehicle speed.

Optionally, obtaining the desired vehicle speed comprises:

acquiring a to-be-processed expected vehicle speed output by a domain controller based on a current driving demand;

and limiting and filtering the expected vehicle speed to be processed to obtain the expected vehicle speed.

Optionally, obtaining the current vehicle speed includes:

acquiring the rotating speed of a motor, the radius of a wheel and the speed ratio of a vehicle;

acquiring the current vehicle speed to be processed based on the motor rotating speed, the wheel radius and the vehicle speed ratio;

and filtering the current vehicle speed to be processed to obtain the current vehicle speed.

Optionally, obtaining the real-time estimated quality comprises:

acquiring driving state data of a vehicle; the driving state data includes a current gear command, a current road gradient, a current acceleration, the current vehicle speed, and a current driving torque of a motor;

based on the driving state data, when the quality estimation triggering condition is judged to be met, continuously acquiring a road rolling resistance coefficient;

determining the real-time estimated mass based on the current vehicle speed, the current acceleration, the current road grade, the road rolling resistance coefficient, and the current drive torque.

Optionally, obtaining the current road gradient comprises: acquiring a current road gradient determined by a domain controller; the domain controller determines the current road gradient of the current position of the vehicle based on the current position of the vehicle and prior road ramp map information; and/or the domain controller determines the current road gradient based on gradient information acquired by a gradient sensor loaded on a vehicle;

acquiring the road adhesion coefficient and the road rolling resistance coefficient, including: acquiring a road attachment coefficient and a road rolling resistance coefficient determined by a domain controller; the domain controller identifies the material of the current road and the dry and wet degree of the road surface based on the image acquisition sensor, and matches the corresponding road adhesion coefficient and road rolling resistance coefficient.

Optionally, determining a target torque based on the real-time status data comprises:

generating an acceleration and deceleration control parameter by using vehicle speed difference processing and PID control based on the current vehicle speed and the expected vehicle speed;

acquiring the required torque of the motor by using a vehicle dynamics equation based on the acceleration and deceleration control parameter, the expected vehicle speed, the current vehicle speed, the real-time estimated mass, the current road gradient and the road rolling resistance coefficient;

and limiting and filtering the torque required by the motor to obtain the target torque. Optionally, generating an acceleration and deceleration control parameter by using vehicle speed difference processing and PID control based on the current vehicle speed and the desired vehicle speed includes:

determining a real-time vehicle speed difference based on the current vehicle speed and the expected vehicle speed and by combining a vehicle operation mode and a vehicle speed difference threshold;

when the integral zero clearing condition of PID control is met, generating a corresponding integral zero clearing instruction;

based on the four factors, carrying out parameter adjustment optimization of PID control to obtain a target proportional parameter, a target integral parameter and a target differential parameter; the four part factors include: the expected speed is different from the real-time speed, the mass is estimated in real time, the current road gradient is estimated, and the road attachment coefficient is estimated;

and generating the acceleration and deceleration control parameter by using a PID control algorithm based on at least the real-time vehicle speed difference, the integral zero clearing instruction, the target proportional parameter, the target integral parameter and the target differential parameter.

Optionally, before performing the parameter adjustment optimization of the PID control, the method further includes:

and when the expected vehicle speed and the actual vehicle speed meet the correction condition, correcting a proportional parameter in PID control.

Optionally, the correction condition includes:

the desired vehicle speed is greater than a vehicle speed threshold; and is

The current vehicle speed is smaller than the expected vehicle speed, and the accumulated time length lower than the vehicle speed threshold value is longer than the preset time length.

Optionally, said determining a real-time vehicle speed difference based on said current vehicle speed and said desired vehicle speed in combination with a vehicle operating mode and a vehicle speed difference threshold comprises:

when the vehicle running mode is a speed control non-preparation state under a rotating speed running mode, a non-automatic driving running mode or an automatic driving mode, the real-time vehicle speed difference is 0;

when the vehicle is in a speed control preparation state in an automatic driving mode, if the current vehicle speed direction of the vehicle is the same as the direction of the expected vehicle speed, the real-time vehicle speed difference is equal to the expected vehicle speed minus the current vehicle speed, and if the current vehicle speed direction of the vehicle is opposite to the direction of the expected vehicle speed, the real-time vehicle speed difference is equal to the expected vehicle speed plus the current vehicle speed; and the real-time vehicle speed difference is less than or equal to the vehicle speed difference threshold value.

Optionally, the integral clear condition includes at least one of the following conditions:

condition 1: when the vehicle is switched between the automatic mode and the manual mode;

condition 2: the real-time vehicle speed difference is larger than zero, and the acceleration and deceleration control parameter is smaller than zero; or the real-time vehicle speed difference is less than zero, and the acceleration and deceleration control parameter is greater than zero;

condition 3: a brake pressure command occurs;

condition 4: switching gear commands;

condition 5: switching the working modes of the motor;

condition 6: the vehicle readiness state changes.

Optionally, the parameter adjustment optimization includes proportional parameter adjustment optimization, integral parameter adjustment optimization and differential parameter adjustment optimization;

the proportion parameter adjustment optimization comprises the following steps:

adjusting and optimizing a proportional parameter based on the expected vehicle speed and the real-time vehicle speed difference; the proportional parameter is in direct proportion to the expected vehicle speed, and the proportional parameter is in direct proportion to the real-time vehicle speed difference;

adjusting and optimizing a proportion parameter based on the real-time estimated mass; wherein, the proportion parameter is in direct proportion to the real-time estimated mass;

adjusting and optimizing a proportion parameter based on the current road gradient; wherein, the proportional parameter is in direct proportion to the current road gradient;

adjusting and optimizing a proportional parameter based on the road adhesion coefficient; wherein, the proportion parameter is in inverse proportion to the road adhesion coefficient;

the integral parameter adjustment optimization comprises the following steps:

adjusting and optimizing integral parameters based on the expected vehicle speed and the real-time vehicle speed difference;

adjusting and optimizing integral parameters based on real-time estimated quality;

adjusting and optimizing an integral parameter based on the current road gradient;

adjusting and optimizing integral parameters based on the road adhesion coefficient;

the differential parameter adjustment optimization comprises the following steps:

adjusting and optimizing differential parameters based on the expected vehicle speed and the real-time vehicle speed difference;

adjusting and optimizing differential parameters based on real-time estimation quality;

adjusting and optimizing differential parameters based on the current road gradient;

and adjusting optimization based on the differential parameter of the road attachment coefficient.

Optionally, the obtaining the required torque of the motor by using the vehicle dynamics equation includes:

the motor required torque is calculated using the following equation:

wherein T represents the torque required by the motor, rho represents the air density, A represents the windward area, and C _D Representing an air resistance coefficient, v representing the current vehicle speed, f representing a road rolling resistance coefficient, m representing a real-time estimated mass, g representing a gravity coefficient, i representing the current road gradient, δ representing a vehicle rotating mass conversion coefficient after the rotating mass inertia moment is counted, a representing an acceleration and deceleration control parameter, r representing the effective radius of a tire of a vehicle, K representing a vehicle speed ratio, and η representing the transmission mechanical efficiency.

Optionally, the limiting and filtering the required torque of the motor to obtain the target torque includes:

limiting the motor demand torque based on a maximum available torque of a vehicle; the maximum available torque is determined based on the state of charge of a power battery in the vehicle, the allowable charging and discharging power of the power battery, the external characteristic torque of the motor, the driving and feedback torque limit of the motor and the fault condition of the whole vehicle;

and filtering the limited required torque of the motor to obtain the target torque.

Optionally, the method further comprises:

transmitting the target torque to a controlled component, and adjusting the vehicle from the current vehicle speed to the desired vehicle speed based on the controlled component.

The present disclosure also provides a vehicle speed control device of an autonomous vehicle, including:

the acquisition module is used for acquiring real-time state data of the vehicle; the real-time state data comprises real-time estimated quality, current vehicle speed, expected vehicle speed and road condition information; the traffic information includes: current road grade, road adhesion coefficient and road rolling resistance coefficient;

a determination module to determine a target torque based on the real-time status data;

The present disclosure also provides a computer-readable storage medium storing a computer program for performing the steps of any one of the methods described above.

The present disclosure also provides an apparatus for a vehicle, including: a processor; a memory for storing the processor-executable instructions; the processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the steps of any of the above methods.

The present disclosure also provides a vehicle including any of the above-described automotive apparatuses.

Compared with the prior art, the technical scheme provided by the disclosure has the following advantages:

the vehicle speed control method provided by the disclosure comprises the steps of acquiring real-time state data of a vehicle; the real-time state data comprises real-time estimated quality, current vehicle speed, expected vehicle speed and road condition information; the road condition information includes: current road grade, road adhesion coefficient and road rolling resistance coefficient; determining a target torque based on the real-time status data; the target torque is used to adjust the vehicle from the current vehicle speed to the desired vehicle speed. According to the technical scheme, the target torque corresponding to vehicle speed control is determined by combining the real-time estimated mass, the current vehicle speed, the expected vehicle speed and road condition information of the vehicle, such as the current road gradient, the road adhesion coefficient, the road rolling resistance coefficient and other real-time state data, the vehicle speed can be accurately controlled in time according to different vehicle condition road conditions, namely the response speed is high, and the following performance is good.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.

In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present disclosure, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.

FIG. 1 is a schematic structural diagram of a vehicle according to an embodiment of the present disclosure;

FIG. 2 is a schematic flow chart diagram illustrating a method for controlling vehicle speed according to an embodiment of the present disclosure;

FIG. 3 is a flow chart illustrating another method of controlling vehicle speed provided by the disclosed embodiment;

FIG. 4 is a schematic diagram illustrating a flow chart of generating vehicle acceleration and deceleration control parameters in a vehicle speed control method according to an embodiment of the present disclosure;

FIG. 5 is a schematic structural diagram of a vehicle speed control device according to an embodiment of the disclosure;

fig. 6 is a schematic structural diagram of another vehicle speed control device provided in the embodiment of the present disclosure.

Detailed Description

In order that the above objects, features and advantages of the present disclosure may be more clearly understood, aspects of the present disclosure will be further described below. It should be noted that the embodiments and features of the embodiments of the present disclosure may be combined with each other without conflict.

In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure, but the present disclosure may be practiced in other ways than those described herein; it is to be understood that the embodiments disclosed in the specification are only a few embodiments of the present disclosure, and not all embodiments.

The vehicle speed control method of the automatic driving vehicle provided by the embodiment of the disclosure can be applied to the technical field of vehicle control of pure electric vehicles, for example, longitudinal vehicle speed control of the pure electric automatic driving tractor is realized. The speed control of an autonomous vehicle is generally divided into lateral control and longitudinal control; lateral control may include identifying camber and turn controls, and longitudinal control may include controlling vehicle speed while traveling. The automatic driving domain controller can transmit an expected vehicle speed to the vehicle control unit through perception, planning and decision; the vehicle control unit determines a target torque adjusted corresponding to the vehicle speed according to the expected vehicle speed, the current speed, the real-time estimated quality and the road condition information; a vehicle execution end, such as a motor controller, realizes closed-loop following control of the vehicle speed through power control based on the target torque. The influence of real-time estimation quality, vehicle conditions and road condition changes of the vehicle on vehicle speed control is considered, so that the following performance of the vehicle speed control is good, stable following suitable for different scenes is achieved, response is fast, and the energy utilization rate is improved.

In some embodiments, parameters of the PID control in the vehicle speed control are corrected, adjusted and optimized based on the real-time estimated quality, vehicle condition and road condition information, so that the following stability and small overshoot of the vehicle speed can be realized, the traveling is stable, namely, the vehicle cannot be accelerated or decelerated suddenly, and the traveling smoothness is good. For example, for external disturbance, for example, by a deceleration strip or pressing on a stone, etc., targeted adjustment can be performed in time, so that the current vehicle speed quickly follows the expected vehicle speed and follows the expected vehicle speed in time.

Exemplarily, fig. 1 is a schematic structural diagram of a vehicle according to an embodiment of the present disclosure, which illustrates a system architecture of an electric unmanned automatic tractor. Referring to fig. 1, the vehicle 01 may include: the system comprises a driving Motor, a speed reducer, a differential mechanism, driving wheels, a power battery, a high-voltage distribution box, a DCDC (direct current), a lead-acid battery, a Vehicle Control Unit (VCU), a Battery Management System (BMS), a Motor Controller (MCU), an automatic driving area controller (DCU) system, an electric power steering system (EPS), an electronic parking brake system (EPB), an electronic hydraulic brake system (EHB), a Vehicle body Control system (BCM), a Tire Pressure Monitoring System (TPMS), a combination instrument system (ICM), a traction bolt controller (ATB), a trailer and the like; the above-mentioned structures are mechanically connected (shown by a thick solid line), electrically connected (shown by a dotted line), or bussed (shown by a thin solid line).

The technical scheme provided by the embodiment of the disclosure is mainly realized on the basis of a vehicle controller, a motor controller and an automatic driving domain controller. Exemplarily, reference may be made to S11, S12, and S13 shown in fig. 2: the automatic driving domain controller determines the expected running speed (namely the expected speed), the current road gradient, the road adhesion coefficient and the road rolling resistance coefficient of the vehicle through sensing, planning and decision-making, and transmits the expected running speed, the current road gradient, the road adhesion coefficient and the road rolling resistance coefficient to the vehicle control unit; the vehicle controller outputs target torque to the motor controller through speed closed-loop control regulated by real-time parameters by combining speed (including current vehicle speed and expected vehicle speed) identification processing, acceleration and deceleration control, real-time quality estimation, torque calculation, limitation, filtering processing and the like; the motor controller receives the target torque and drives the motor based thereon, so that the vehicle tracks the expected vehicle speed, and the tracking of the expected vehicle speed is quickly, stably and effectively responded.

The control method provided by the embodiment of the disclosure mainly includes steps executed by the vehicle control unit, for example, acquiring, identifying and processing real-time parameters, and outputting a target torque.

The following describes a vehicle speed control method, a vehicle speed control device, a vehicle speed control medium, a vehicle speed control device, and a vehicle according to embodiments of the disclosure, with reference to the accompanying drawings.

For example, fig. 3 is a flowchart illustrating another vehicle speed control method provided by the embodiment of the disclosure, which may be executed by a vehicle controller. Referring to fig. 3, the method may include the steps of:

and S110, acquiring real-time state data of the vehicle.

In the embodiment of the disclosure, the real-time status data is related data for representing real-time vehicle conditions and road conditions, and may include real-time estimated quality, current vehicle speed, expected vehicle speed and road condition information; the road condition information includes: current road grade, road adhesion coefficient, and road rolling resistance coefficient.

The real-time estimated mass is estimated based on the current real-time state of the vehicle, and is determined based on the actual conditions of the vehicle condition and the road condition instead of a preset fixed constant value. Therefore, the real-time estimation quality is applied to the vehicle speed control method, and accurate and timely following of the vehicle speed is facilitated.

The current vehicle speed is the actual vehicle speed of the vehicle in the current running state, the expected vehicle speed is the target vehicle speed determined based on the vehicle control demand, and the purpose of vehicle speed control is to enable the actual vehicle speed to follow the expected vehicle speed.

The road condition information is information of an actual road of the vehicle in a current scene, and can include information influencing vehicle speed control, such as a current road gradient, a road adhesion coefficient, a road rolling resistance coefficient and the like, so that accurate calculation of a target torque is realized, and timely and accurate control of vehicle speed following is realized.

The manner of acquiring each real-time status data is exemplarily described later.

And S120, determining the target torque based on the real-time state data.

Wherein the target torque is used to adjust the vehicle from the current vehicle speed to a desired vehicle speed to achieve vehicle speed following.

In the embodiment of the disclosure, the target torque is determined based on the real-time state data related to the vehicle conditions and the road conditions, and the accuracy is higher, so that the vehicle speed can be accurately controlled in time according to different vehicle condition and road conditions, namely, the response speed is higher, the following performance is better, the vehicle speed overshoot is smaller, the running is smooth, and the smoothness is good.

In some embodiments, obtaining the desired vehicle speed may specifically include:

In the embodiment of the disclosure, the current driving requirement can be associated with data such as user settings and road conditions, and the road conditions can include whether the vehicle is smooth or not, whether barriers exist or not and the like; the domain controller carries out planning control based on the current driving requirement and outputs the expected speed to be processed to the whole vehicle controller through the intelligent driving CAN bus; correspondingly, the vehicle control unit receives the expected vehicle speed to be processed, limits and filters the expected vehicle speed to obtain the expected vehicle speed. It can be understood that the pending desired vehicle speed may be a desired vehicle speed after being limited and filtered in the domain controller, and after being transmitted to the vehicle controller, the pending desired vehicle speed may be secondarily limited and filtered to exclude unreasonable data and ensure that the vehicle speed continuously changes within a preset range to ensure traveling smoothness.

In some embodiments, obtaining the current vehicle speed may specifically include:

acquiring the current speed to be processed based on the motor speed, the wheel radius and the vehicle speed ratio;

and carrying out filtering processing on the current vehicle speed to be processed to obtain the current vehicle speed.

In the disclosed embodiment, the current vehicle speed corresponds to the linear speed of the wheel. The vehicle control unit determines the current vehicle speed to be processed based on the acquired motor speed, wheel radius and vehicle speed ratio (namely transmission ratio), and performs filtering processing to eliminate inaccurate data and obtain the current vehicle speed with higher accuracy.

In other embodiments, the current vehicle speed may also be determined based on changes in the location information and changes in time of the vehicle; or otherwise determine the current vehicle speed, which is not limited herein.

In some embodiments, obtaining the real-time estimated quality may specifically include:

acquiring driving state data of a vehicle; the driving state data may specifically include a current gear command, a current road gradient, a current acceleration, a current vehicle speed, and a current driving torque of the motor;

the real-time estimated mass is determined based on a current vehicle speed, a current acceleration, a current road grade, a road rolling resistance coefficient, and a current drive torque.

In the embodiment of the disclosure, the estimation of the real-time mass of the vehicle is realized based on a program algorithm to obtain the real-time estimated mass, so that an additional sensor is not required to be added, the space of the vehicle is not required to be occupied, the investment cost of the vehicle is not increased, and the popularization and the use are convenient; meanwhile, the mass estimation can be carried out by combining the actual conditions of the vehicle and the road, the accuracy of the mass estimation is higher, and the vehicle speed can be conveniently followed in time.

For example, referring to fig. 2, this step may be performed by a vehicle mass estimation module in the vehicle controller, and specifically, the estimation of the real-time mass of the vehicle may be implemented in combination with the combination of processing processes of vehicle condition and road condition recognition, vehicle longitudinal dynamics model calculation, calculation mass limit processing, updating and mean value processing of the memory mass and the limit mass stored by the controller, and average mass kalman filtering, so that the mass of the vehicle may be estimated more accurately, the accuracy of vehicle mass estimation may be improved, and particularly, the accurate estimation of the mass of the electric unmanned automatic tractor with a large mass change may be implemented, a necessary condition for parameter adjustment may be provided for accurate vehicle speed control, and further, the vehicle speed may be followed in time.

In some embodiments, obtaining the current road gradient may specifically include: the current road grade determined by the domain controller is obtained.

The domain controller determines the current road gradient of the current position of the vehicle based on the current position of the vehicle and prior road ramp map information; and/or the domain controller determines the current road grade based on grade information collected by a grade sensor mounted on the vehicle.

In the embodiment of the disclosure, the current road gradient (i represents the current road gradient) is collected, identified and processed based on the domain controller, and is transmitted to the vehicle control unit.

Specifically, the domain controller can obtain gradient information of the current position of the vehicle based on the current position of the vehicle and road slope map information measured in advance (namely, priori), namely obtaining the current road gradient; the current road gradient is sent to a vehicle control unit by a domain controller through an intelligent driving CAN bus; correspondingly, the vehicle control unit acquires the current road gradient.

Or, the vehicle is provided with a gradient sensor, and the gradient sensor collects the gradient information of the current road and transmits the gradient information to the domain controller; the domain controller determines the gradient of the current road based on the received gradient information of the current road and sends the gradient to the whole vehicle controller through an intelligent driving CAN bus; correspondingly, the vehicle control unit acquires the current road gradient.

So set up, can acquire current road slope conveniently accurately, do benefit to the accurate estimation to vehicle mass.

In some embodiments, obtaining the road adhesion coefficient and the road rolling resistance coefficient may specifically include: and acquiring the road attachment coefficient and the road rolling resistance coefficient determined by the domain controller.

The domain controller identifies the material of the current road and the dryness and wetness degree of the road surface based on the image acquisition sensor, and matches the corresponding road adhesion coefficient and the road rolling resistance coefficient.

In the embodiment of the disclosure, the domain controller identifies and processes the road adhesion coefficient and the road rolling resistance coefficient, and transmits the road adhesion coefficient and the road rolling resistance coefficient to the vehicle control unit.

Specifically, the domain controller may identify road conditions through an image acquisition component, such as a camera, for example, identify the material of the current road and the dryness of the road surface by applying an image identification processing technology, and match the corresponding road adhesion coefficient and road rolling resistance coefficient; for example, the material of the current road may include asphalt pavement, concrete pavement, gravel pavement, ice and snow pavement, etc., and the matching correspondence between the road adhesion coefficient and the road rolling resistance coefficient and the material and the degree of dryness may be measured and stored based on a test for recall in this step for determination. The road adhesion coefficient and the road rolling resistance coefficient CAN be sent to the vehicle control unit by the domain controller through the intelligent driving CAN bus so as to be applied to vehicle speed control and realize timely and effective control of vehicle speed according to different road condition scenes.

In some embodiments, referring to fig. 2, the vehicle control unit may further provide a vehicle speed recognition processing module, a vehicle acceleration and deceleration control module, an automatic driving torque calculation module, an automatic driving torque limitation module, and an automatic driving torque filtering module; the vehicle speed identification processing module is used for identifying and processing the current vehicle speed and the expected vehicle speed (see the above in detail), the vehicle acceleration and deceleration control module is used for generating acceleration and deceleration control parameters, the automatic driving torque calculation module is used for generating the motor demand torque, and the automatic driving torque limiting module and the automatic driving torque filtering module are used for comprehensively limiting and filtering the motor demand torque to obtain the target torque. An exemplary description is given below.

In some embodiments, determining the target torque based on the real-time status data comprises:

generating an acceleration and deceleration control parameter by utilizing vehicle speed difference processing and PID control based on the current vehicle speed and the expected vehicle speed;

acquiring the required torque of the motor by utilizing a vehicle dynamics equation based on the acceleration and deceleration control parameter, the expected vehicle speed, the current vehicle speed, the real-time estimated mass, the current road gradient and the road rolling resistance coefficient;

and limiting and filtering the torque required by the motor to obtain the target torque.

In the embodiment of the disclosure, on the basis of obtaining real-time state data representing the current state of a vehicle, firstly, an acceleration and deceleration control parameter is generated by using vehicle speed difference processing and PID control, and a motion parameter adjusted from the current vehicle speed to an expected vehicle speed is obtained; then combining the expected vehicle speed, the current vehicle speed, the real-time estimated mass, the current road gradient and the road rolling resistance coefficient, namely combining parameters for representing the current vehicle condition and road condition into a vehicle dynamics equation to obtain the required torque of the motor; and finally, limiting and filtering the torque required by the motor, and ensuring that the obtained target torque is the torque within a reasonable range enabling the vehicle speed to change smoothly. Therefore, real-time parameters corresponding to the vehicle condition and road condition are combined into the vehicle speed control, the vehicle speed can be effectively followed, the overshoot is small, and the smoothness is good.

In some embodiments, fig. 4 is a schematic flow chart of a generation process of vehicle acceleration and deceleration control parameters in a vehicle speed control method provided by the embodiments of the present disclosure, which may be executed by the vehicle acceleration and deceleration control module shown in fig. 2. With reference to fig. 2 and 4, the generating of the acceleration and deceleration control parameter by the vehicle acceleration and deceleration control module may specifically include: the method comprises the following steps of vehicle speed difference processing, integral control zero clearing, proportion base number correction, PID parameter adjustment optimization, PID control and PID control output limitation.

Illustratively, the vehicle acceleration and deceleration control module can output acceleration and deceleration control parameters and PID parameters (including a proportional parameter Kp, an integral parameter Ki and a differential parameter Kd) of the vehicle according to a vehicle speed closed-loop PID control method. Specifically, the PID parameters may be obtained by looking up a table based on parameters such as an expected vehicle speed, a real-time vehicle speed difference (i.e., a difference between the expected vehicle speed and the current vehicle speed), a real-time estimated mass, a current road grade, and a road adhesion coefficient, so as to realize real-time flexible adjustment for different vehicle conditions.

In some embodiments, generating acceleration and deceleration control parameters based on a current vehicle speed and a desired vehicle speed using vehicle speed difference processing and PID control includes:

performing parameter adjustment optimization of PID control based on the four factors to obtain a target proportional parameter, a target integral parameter and a target differential parameter; the four factors include: the expected speed is different from the real-time speed, the mass is estimated in real time, the current road gradient is estimated, and the road attachment coefficient is estimated;

and generating an acceleration and deceleration control parameter by using a PID control algorithm based on at least a real-time vehicle speed difference, an integral zero clearing instruction, a target proportional parameter, a target integral parameter and a target differential parameter.

In the embodiment of the present disclosure, first, relevant parameters of real-time vehicle speed difference and PID control are determined, and then, a PID control algorithm is used to input the real-time vehicle speed difference, an integral clear instruction, and a PID parameter (including a target proportional parameter, a target integral parameter, and a target differential parameter, and also including other conventional parameters of PID control, which are not described herein), and control output is implemented through the PID control algorithm, so as to obtain an acceleration and deceleration control parameter (i.e., an acceleration and deceleration instruction).

Illustratively, the PID control algorithm can be represented as:

wherein, K _p Is a proportionality coefficient, namely a target proportionality parameter; k is _i Is an integral coefficient, namely a target integral parameter; k _d Is a differential coefficient, namely a target differential parameter; e (k) is the real-time vehicle speed difference of the kth sampling, and the unit can be km/h; a (k) is the output of the kth sampling in m/s ² A negative value corresponds to a deceleration command, and a positive value corresponds to an acceleration command.

In some embodiments, the PID control output limits specifically include:

1. the control command output by the PID does not exceed the designed maximum acceleration and deceleration limit range;

2. when the vehicle is in a non-Ready state, the limit control output is 0;

3. when the vehicle is in a non-driving gear, limiting the control output to be 0;

4. when the electronic hand brake EPB is in a non-release state, the limit control output is 0.

In the embodiment of the disclosure, the PID controls the output limit, so that the acceleration and deceleration control parameter is in a reasonable range of the maximum available acceleration and deceleration limit value and accords with the current real-time state of the vehicle, thereby being beneficial to realizing the effective control of the vehicle speed.

Exemplarily, in fig. 4, with a ₀ Showing the acceleration and deceleration control parameters generated by PID control, and showing the acceleration and deceleration control parameters after the output limitation of the PID control by a; when PID control output limitation is not performed, a ₀ As the a output.

In some embodiments, before performing the parameter tuning optimization for PID control, the method may further include:

and when the expected vehicle speed and the actual vehicle speed meet the correction condition, correcting the proportional parameter in the PID control.

In the embodiment of the disclosure, the proportion parameter in the PID control is corrected, so that the PID control is kept in a proper error control range, the following control of the vehicle speed is facilitated, the response is timely, and the effectiveness is good.

In some embodiments, the correction condition includes:

the desired vehicle speed is greater than a vehicle speed threshold; and the current vehicle speed is less than the expected vehicle speed, and the accumulated time length lower than the vehicle speed threshold value is greater than the preset time length.

In the disclosed embodiment, the vehicle speed threshold is a vehicle speed threshold indicating that the vehicle speed is too fast, and the preset duration is a duration threshold indicating that the duration is too long. Based on the above, when the expected vehicle speed is greater than the vehicle speed threshold value, namely the current vehicle speed is not tracked to the expected vehicle speed and the accumulated time length exceeds the preset time length, the proportional parameters are corrected and adjusted through the output parameters of the PID control algorithm.

For example, when the vehicle starts and the load of the vehicle becomes large, the vehicle can be started quickly by parameter correction, and the problem that the vehicle starts too slowly is solved.

In some embodiments, determining a real-time vehicle speed differential based on a current vehicle speed and a desired vehicle speed in combination with a vehicle operating mode and a vehicle speed differential threshold comprises:

when the vehicle running mode is a rotating speed running mode, a non-automatic driving running mode or a speed control non-preparation state in an automatic driving mode, the real-time vehicle speed difference is 0;

when the vehicle is in a speed control preparation state in an automatic driving mode, if the current vehicle speed direction of the vehicle is the same as the direction of the expected vehicle speed, the real-time vehicle speed difference is equal to the expected vehicle speed minus the current vehicle speed, and if the current vehicle speed direction of the vehicle is opposite to the direction of the expected vehicle speed, the real-time vehicle speed difference is equal to the expected vehicle speed plus the current vehicle speed; and all meet the requirement that the real-time vehicle speed difference is less than or equal to the vehicle speed difference threshold value.

In the embodiment of the present disclosure, a vehicle speed difference process is performed, that is, a difference process is performed between a desired vehicle speed and a current vehicle speed (i.e., an actual vehicle speed). In particular, the general real-time vehicle speed differential (in v) _e Expressed) is equal to the desired vehicle speed (in v) _D Representation) minus the current vehicle speed (in v), i.e.:

v _e ＝v _D v (sign calculation with direction correspondence)

And it can be understood that the real-time vehicle speed difference does not exceed the positive and negative threshold values, i.e., it remains within a reasonable range for vehicle speed control.

When the actual driving direction of the vehicle is consistent with the driving command direction, namely the direction of the current vehicle speed is consistent with the direction of the expected vehicle speed, the real-time vehicle speed difference is as follows:

v _e ＝v _D v (no direction, only numerical calculation)

And when the actual driving direction of the vehicle is not consistent with the driving command direction, namely the direction of the current vehicle speed is not consistent with the direction of the expected vehicle speed, the real-time vehicle speed difference is as follows:

v _e ＝v _D + v (without direction, only numerical calculation)

In addition, when the vehicle is in a rotating speed running mode, the vehicle speed control method is not started, and the real-time vehicle speed difference is 0, namely v _e =0; when the vehicle is in a non-automatic driving running mode, the automatic vehicle speed control is not carried out, namely the vehicle speed control method is not started, and v is the same _e =0; and v when the vehicle is not Ready _e ＝0。

Therefore, corresponding real-time vehicle speed difference is obtained in a targeted manner for different situations.

In some embodiments, the vehicle speed difference v used for PID integral control _eKi Difference v from real time vehicle speed _e The following relationship is satisfied:

when the acceleration and deceleration control parameter (before the PID is not limited and output) output by the PID control is larger than the maximum allowable acceleration (namely the positive value corresponding to the maximum available acceleration and deceleration limit value range), and the real-time vehicle speed difference v _e When greater than 0, the vehicle speed difference v for integral control _eKi =0, otherwise v _eKi ＝v _e 。

When the acceleration and deceleration control parameter output by the PID control is smaller than the minimum allowable acceleration (namely a negative value corresponding to the maximum available acceleration and deceleration limit value range) and the vehicle speed difference ve is smaller than 0, the vehicle speed difference v used for integral control _eKi =0, otherwise v _eKi ＝v _e 。

Therefore, the value of the vehicle speed difference used by PID integral control is limited, and the vehicle speed safety control requirement is met.

In some embodiments, the integral zero clearing is performed, so that the vehicle speed control corresponding to the current cycle can be restarted, and the influence of the previous cycle on the vehicle speed control of the current cycle is avoided. Illustratively, the integral clear condition includes at least one of the following conditions:

condition 3: a brake pressure command occurs;

condition 4: switching gear commands;

condition 5: switching the working modes of the motor;

condition 6: the vehicle readiness state changes.

In the embodiment of the disclosure, when any one of the above conditions is met, it is indicated that a new vehicle speed control cycle is switched, and PID integration zero clearing is performed at this time, so that when the new vehicle speed control cycle is started, integration is performed from 0 again, thereby avoiding the influence of the previous cycle on vehicle speed control under the cycle, and enabling vehicle speed control accuracy to be higher, thereby ensuring timely response and fast following.

In some embodiments, the parameter adjustment and optimization may specifically include: proportional parameter adjustment optimization, integral parameter adjustment optimization and differential parameter adjustment optimization.

Therefore, the adjustment and optimization of the proportional parameter, the integral parameter and the differential parameter in the PID control are realized, the PID control error is favorably reduced, and the accurate control of the vehicle speed is realized.

In some embodiments, the optimization of the scale parameter adjustment includes adjustment through four parts of related factors, and reasonable target scale parameters are comprehensively output. The steps may specifically include:

adjusting and optimizing a proportional parameter based on the real-time estimated mass; wherein, the proportion parameter is in direct proportion to the real-time estimated mass;

adjusting and optimizing a proportional parameter based on the current road gradient; wherein, the proportional parameter is in direct proportion to the current road gradient;

adjusting and optimizing a proportional parameter based on the road adhesion coefficient; wherein the proportional parameter is inversely proportional to the road adhesion coefficient.

In this embodiment, for each part of the relevant factors, a corresponding proportional parameter can be obtained through table lookup; and then calculating by combining the weight coefficients of the four parts to obtain a target proportion parameter.

Illustratively, the optimization is adjusted for a proportional parameter based on the desired vehicle speed and the real-time vehicle speed difference: the expected vehicle speed and the real-time vehicle speed difference can be used as a two-dimensional table to be input, and the proportional parameters of the partial factors can be output. Illustratively, proportional parameters corresponding to different expected vehicle speeds and real-time vehicle speed differences can be obtained through test calibration. Wherein the larger the desired vehicle speed, the larger the proportional parameter; the larger the real-time vehicle speed difference is, the larger the proportional parameter is, so that the effective regulation and control of the vehicle speed are favorably realized.

Illustratively, the optimization is adjusted for the proportional parameters based on the real-time estimated mass: the real-time estimated quality can be input as a one-dimensional table, and the proportional parameters of the part of factors are output. Illustratively, the proportional parameters corresponding to different real-time estimated masses can be obtained through experimental calibration. The larger the real-time estimation quality is, the larger the proportional parameter is, so that the effective regulation and control of the vehicle speed can be realized.

Illustratively, optimization is adjusted for the scale parameter based on the current road grade: the current road gradient can be input as a one-dimensional table, and the proportional parameters of the part of factors can be output. Illustratively, the proportional parameters corresponding to different current road gradients can be obtained through experimental calibration. The larger the gradient of the current road is, the larger the proportional parameter is, so that the effective regulation and control of the vehicle speed can be realized.

Illustratively, the optimization is adjusted for the road attachment coefficient-based scale parameter: the road attachment coefficient can be input as a one-dimensional table, and the proportional parameters of the part of factors are output. Illustratively, the proportional parameters corresponding to different road adhesion coefficients can be obtained through experimental calibration. The smaller the road adhesion coefficient is, the smaller the proportional parameter is, so that the effective regulation and control of the vehicle speed can be realized.

Therefore, proportional parameter adjustment optimization is carried out by combining the road condition information of the vehicle conditions, PID control aiming at different road conditions of the vehicle conditions can be realized, the reduction of vehicle speed control errors is facilitated, the accurate regulation and control of the vehicle speed are realized, and the follow-up is timely carried out.

In some embodiments, the optimization of the integral parameter adjustment, including adjustment by four relevant factors, comprehensively outputting a reasonable target integral parameter, may specifically include:

and adjusting and optimizing the integral parameter based on the road adhesion coefficient.

In this embodiment, for each part of the relevant factors, a corresponding proportional parameter can be obtained through table lookup; and then calculating by combining the weight coefficients of the four parts to obtain a target integral parameter.

Illustratively, the optimization is adjusted for an integral parameter based on the desired vehicle speed and the real-time vehicle speed difference: the expected vehicle speed and the real-time vehicle speed difference can be used as two-dimensional table input, and integral parameters of the partial factors are output. Illustratively, integral parameters corresponding to different expected vehicle speeds and the real-time vehicle speed difference can be obtained through experimental calibration.

Illustratively, optimization is adjusted for integration parameters based on real-time estimated quality: the real-time estimated mass can be input as a one-dimensional table, and the integral parameters of the part of factors are output. Illustratively, the integral parameters corresponding to different real-time estimated masses can be obtained by experimental calibration.

Illustratively, the optimization is adjusted for an integral parameter based on the current road gradient: the current road gradient may be input as a one-dimensional table, and the integral parameter of the part of factors may be output. For example, the integral parameters corresponding to different current road gradients can be obtained through experimental calibration.

Illustratively, the optimization is adjusted for the integration parameter based on the road adhesion coefficient: the road attachment coefficient may be input as a one-dimensional table, and the integral parameter of the part of the factors may be output. Illustratively, integral parameters corresponding to different road adhesion coefficients can be obtained through experimental calibration.

Therefore, integral parameter adjustment optimization is carried out by combining the road condition information of the vehicle conditions, PID control aiming at different road conditions of the vehicle conditions can be realized, the reduction of vehicle speed control errors is facilitated, the accurate regulation and control of the vehicle speed are realized, and the follow-up is timely carried out.

In some embodiments, the optimization of the differential parameter adjustment, including adjustment by four relevant factors, comprehensively outputs a reasonable target differential parameter, which may specifically include:

In this embodiment, for each part of the relevant factors, a corresponding differential parameter may be obtained by table lookup; and then calculating by combining the weight coefficients of the four parts to obtain a target integral parameter.

Illustratively, the optimization is tuned for a differential parameter based on the desired vehicle speed and the real-time vehicle speed difference. And taking the difference between the expected vehicle speed and the real-time vehicle speed as the input of a two-dimensional table, and outputting differential parameters of the partial factors. For example, differential parameters corresponding to different expected vehicle speeds and the real-time vehicle speed difference can be obtained through test calibration.

Illustratively, the optimization is tuned for differential parameters based on real-time estimated mass. And taking the real-time estimated mass as a one-dimensional table input, and outputting differential parameters of the part of factors. Illustratively, differential parameters corresponding to different real-time estimated masses can be obtained through experimental calibration.

Illustratively, the optimization is adjusted for differential parameters based on the current road grade. And taking the current road gradient as a one-dimensional table input, and outputting differential parameters of the part of factors. For example, differential parameters corresponding to different current road gradients can be obtained through experimental calibration.

Illustratively, the optimization is adjusted for differential parameters based on the road attachment coefficient. The road adhesion coefficient is used as a one-dimensional table input, and differential parameters of the part of factors are output. Illustratively, differential parameters corresponding to different road adhesion coefficients can be obtained through experimental calibration.

Therefore, differential parameter adjustment optimization is carried out by combining the road condition information of the vehicle conditions, PID control aiming at different road conditions of the vehicle conditions can be realized, the reduction of vehicle speed control errors is facilitated, and accurate regulation and control of the vehicle speed are realized and the vehicle speed can be followed in time.

In some embodiments, obtaining the required torque of the electric machine by using a vehicle dynamics equation may specifically include:

the motor required torque is calculated using the following equation:

In the embodiment of the disclosure, the expected vehicle speed v can be calculated based on the automatic driving torque calculation module in the vehicle control unit _D The current vehicle speed v, the real-time estimated mass m, the acceleration and deceleration control parameter a, the current road gradient i and the road rolling resistance coefficient f are used as input, and the motor required torque (namely the torque command of the motor) is obtained through calculation through a vehicle dynamics equation. Therefore, the motor demand torque calculation based on the vehicle condition and road condition is realized, and the torque calculation accuracy can be improved aiming at various different scenes, so that the timeliness and the accuracy of vehicle speed control are improved, and further, the response is timely and the following is effective.

Specifically, the formula for calculating the required torque of the motor is derived by combining the force analysis of the vehicle, and the formula is as follows:

the resistance to which the vehicle is subjected being wind resistance F _w Rolling resistance F _f Slope resistance F _i And acceleration resistance F _j The vehicle running balance equation is:

F＝F _w +F _f +F _i +F _j (1)

where F represents the tangential reaction force, i.e. the driving force, of the ground on the driving wheel of the wheel.

And, wind resistance F _w Calculated using the formula:

wherein: rho represents air density and can be 1.2258Ns ² m ^-4 (ii) a A is the frontal area, and the physical unit can be square meter (m) ² )；C _D Represents an air resistance coefficient; v is the speed of movement of the air relative to the vehicle, and is numerically equal to the current vehicle speed.

Rolling resistance F _f Calculated using the formula:

F _f ＝f·m _F g·cos(θ) (3)

wherein: f represents a resistance coefficient corresponding to the road rolling resistance, and corresponds to a result of identification and analysis of the DCU by using the image acquisition assembly for the road surface; m is _F Represents a real-time estimated mass, which may be in kilograms (kg) in physical units, and is ultimately calculated by the VCU; g represents the acceleration of gravity, which may be in physical units of meters per square meter (m/s) ² ) (ii) a θ represents a road angle in radians (rad) and a relationship with the current road grade i is θ = arctan (i); based on this, (3) formula can be changed to:

F _f ＝f·m _F g·cos(arctan(i)) (4)

illustratively, when the current vehicle speed is 0, the rolling resistance F _f Is 0.

Slope resistance F _i Calculated using the formula:

F _i ＝m _F g·sin(θ) (5)

in combination with the above conversion of the road angle and the current road gradient, equation (5) can be converted into:

F _i ＝m _F g·sin(arctan(i)) (6)

acceleration resistance F _j Calculated using the formula:

F _j ＝δm _F a (7)

wherein: a represents the current acceleration output by the vehicle speed closed-loop control; and delta represents the conversion coefficient of the rotating mass of the automobile after the inertia moment of the rotating mass is taken into account.

Substituting and organizing the above equations (1), (2), (4), (6) and (7), the obtained vehicle running balance equation is:

meanwhile, the calculation formula of the required torque of the motor corresponding to the cruise control is as follows:

wherein T represents the motor torque demand, r represents the effective radius of the tire, which may be in meters (m) in physical units; k represents a transmission ratio; eta represents the mechanical efficiency of the drive train.

By combining the equations (8) and (9), the final motor required torque calculation formula can be obtained:

in some embodiments, limiting and filtering the torque required by the motor to obtain the target torque includes:

limiting the required torque of the motor based on the maximum available torque of the vehicle (including the maximum available torque corresponding to driving and the maximum available torque corresponding to feedback); the maximum available torque is determined based on the State of Charge (SOC) of a power battery in the vehicle, the allowable charging and discharging power of the power battery, the external characteristic torque (comprising peak torque and continuous torque) of the motor, the driving and feedback torque limit of the motor and the fault condition of the whole vehicle;

and filtering the limited required torque of the motor to obtain a target torque.

In the embodiment of the disclosure, the torque required by the motor can be limited within a reasonable range by limiting the torque required by the motor, the available power of the power battery is fully utilized, and the energy utilization efficiency is improved while the vehicle speed is effectively controlled; furthermore, step change can be avoided by filtering the limited motor demand torque, so that the target torque changes smoothly, and the smoothness of vehicle speed control is better.

In some embodiments, the method further comprises:

the target torque is transmitted to the controlled component, and the vehicle is adjusted from the current vehicle speed to the desired vehicle speed based on the controlled component.

In embodiments of the present disclosure, the controlled component may be a motor controller. The vehicle control unit sends the target torque to a motor controller; correspondingly, the motor controller receives the target torque and controls the motor to operate, so that the vehicle is controlled to shift to the expected vehicle speed from the current vehicle speed.

The vehicle speed control method provided by the embodiment of the disclosure is combined with the relevant real-time state data of the vehicle condition and road condition, realizes vehicle speed control, has small overshoot, can stably, effectively and quickly respond to the expected vehicle speed aiming at various different scenes, can recover energy by motor braking energy feedback (electric braking) during deceleration, improves the utilization rate of vehicle energy, and increases the driving range; and the control logic is clear, the influence of gradient change and load change on the automatic running of the vehicle can be relieved, the vehicle speed response of the vehicle is quick, the following performance is improved, the running is smooth, the smoothness is good, and the maneuverability, the comfort and the safety of the vehicle are improved.

On the basis of the above embodiments, the embodiments of the present disclosure further provide a vehicle speed control device for an autonomous vehicle, which can perform the steps of any of the above methods to achieve corresponding beneficial effects.

For example, fig. 5 is a schematic structural diagram of a vehicle speed control device provided in an embodiment of the disclosure. Referring to fig. 5, the apparatus 40 includes: an obtaining module 41, configured to obtain real-time status data of a vehicle; the real-time state data comprises real-time estimated quality, current vehicle speed, expected vehicle speed and road condition information; the road condition information includes: current road grade, road adhesion coefficient and road rolling resistance coefficient; a determination module 42 for determining a target torque based on the real-time status data; the target torque is used to adjust the vehicle from the current vehicle speed to the desired vehicle speed.

In some embodiments, the obtaining module 41 is configured to obtain the desired vehicle speed, and includes: acquiring a to-be-processed expected vehicle speed output by a domain controller based on a current driving demand; and limiting and filtering the expected vehicle speed to be processed to obtain the expected vehicle speed.

In some embodiments, the obtaining module 41 is configured to obtain the current vehicle speed, and includes: acquiring the rotating speed of a motor, the radius of a wheel and the speed ratio of a vehicle; acquiring the current speed to be processed based on the motor speed, the wheel radius and the vehicle speed ratio; and carrying out filtering processing on the current vehicle speed to be processed to obtain the current vehicle speed.

In some embodiments, the obtaining module 41 is configured to obtain the real-time estimated quality, and includes: acquiring driving state data of a vehicle; the driving state data includes a current gear command, a current road gradient, a current acceleration, a current vehicle speed, and a current driving torque of the motor; based on the driving state data, when the quality estimation triggering condition is judged to be met, continuously acquiring the road rolling resistance coefficient; an estimated mass is determined based on the current vehicle speed, the current acceleration, the current road grade, the road rolling resistance coefficient, and the current drive torque.

In some embodiments, the obtaining module 41 is configured to obtain the current road gradient, and includes: acquiring a current road gradient determined by a domain controller; the domain controller determines the current road gradient of the current position of the vehicle based on the current position of the vehicle and prior road ramp map information; and/or the domain controller determines the current road grade based on grade information collected by a grade sensor mounted on the vehicle.

In some embodiments, the obtaining module 41 is configured to obtain a road adhesion coefficient and a road rolling resistance coefficient, and includes: acquiring a road attachment coefficient and a road rolling resistance coefficient determined by a domain controller; the domain controller identifies the material of the current road and the dry and wet degree of the road surface based on the image acquisition sensor, and matches the corresponding road adhesion coefficient and road rolling resistance coefficient.

In some embodiments, the determination module 42 is configured to determine the target torque based on real-time status data, including: generating an acceleration and deceleration control parameter by utilizing vehicle speed difference processing and PID control based on the current vehicle speed and the expected vehicle speed; acquiring the required torque of the motor by utilizing a vehicle dynamics equation based on the acceleration and deceleration control parameter, the expected vehicle speed, the current vehicle speed, the real-time estimated mass, the current road gradient and the road rolling resistance coefficient; and limiting and filtering the torque required by the motor to obtain the target torque.

In some embodiments, the determination module 42 is configured to generate the acceleration and deceleration control parameters based on the current vehicle speed and the desired vehicle speed using vehicle speed difference processing and PID control, including: determining a real-time vehicle speed difference based on the current vehicle speed and the expected vehicle speed and by combining a vehicle operation mode and a vehicle speed difference threshold; when the integral zero clearing condition of PID control is met, generating a corresponding integral zero clearing instruction; based on the four factors, carrying out parameter adjustment optimization of PID control to obtain a target proportional parameter, a target integral parameter and a target differential parameter; the four factors include: the expected speed is different from the real-time speed, the mass is estimated in real time, the current road gradient is estimated, and the road attachment coefficient is estimated; and generating an acceleration and deceleration control parameter by utilizing a PID control algorithm based on at least a real-time vehicle speed difference, an integral zero clearing instruction, a target proportional parameter, a target integral parameter and a target differential parameter.

In some embodiments, the determination module 42 is configured to modify the proportional parameter in the PID control when the desired vehicle speed and the actual vehicle speed satisfy the modification condition before performing the parameter adjustment optimization of the PID control.

In some embodiments, the correction condition includes: the desired vehicle speed is greater than a vehicle speed threshold; and the current vehicle speed is less than the expected vehicle speed, and the accumulated time length lower than the vehicle speed threshold value is greater than the preset time length.

In some embodiments, the determination module 42 is configured to determine a real-time vehicle speed difference based on the current vehicle speed and the desired vehicle speed in combination with the vehicle operating mode and the vehicle speed difference threshold, including: when the vehicle running mode is a rotating speed running mode, a non-automatic driving running mode or a speed control non-preparation state in an automatic driving mode, the real-time vehicle speed difference is 0; when the vehicle is in a speed control preparation state in an automatic driving mode, if the current vehicle speed direction of the vehicle is the same as the direction of the expected vehicle speed, the real-time vehicle speed difference is equal to the expected vehicle speed minus the current vehicle speed, and if the current vehicle speed direction of the vehicle is opposite to the direction of the expected vehicle speed, the real-time vehicle speed difference is equal to the expected vehicle speed plus the current vehicle speed; and all meet the requirement that the real-time vehicle speed difference is less than or equal to the vehicle speed difference threshold value.

In some embodiments, the integrate-and-clear condition comprises at least one of the following conditions: condition 1: when the vehicle is switched between the automatic mode and the manual mode; condition 2: the real-time vehicle speed difference is larger than zero, and the acceleration and deceleration control parameter is smaller than zero; or the real-time vehicle speed difference is less than zero, and the acceleration and deceleration control parameter is greater than zero; condition 3: a brake pressure command occurs; condition 4: switching gear commands; condition 5: switching the working modes of the motor; condition 6: the vehicle readiness state changes.

In some embodiments, the determination module 42 is configured to perform parameter tuning optimization, including proportional parameter tuning optimization, integral parameter tuning optimization, and derivative parameter tuning optimization.

In some embodiments, the determination module 42 is configured to perform a scaling parameter adjustment optimization, including: adjusting and optimizing a proportional parameter based on the expected vehicle speed and the real-time vehicle speed difference; the proportional parameter is in direct proportion to the expected vehicle speed, and the proportional parameter is in direct proportion to the real-time vehicle speed difference; adjusting and optimizing a proportional parameter based on the real-time estimated mass; wherein, the proportion parameter is in direct proportion to the real-time estimated mass; adjusting and optimizing a proportion parameter based on the current road gradient; wherein, the proportional parameter is in direct proportion to the current road gradient; adjusting and optimizing a proportion parameter based on the road adhesion coefficient; wherein the proportional parameter is inversely proportional to the road adhesion coefficient.

In some embodiments, the determination module 42 is configured to perform an integral parameter adjustment optimization, including: adjusting and optimizing integral parameters based on the expected vehicle speed and the real-time vehicle speed difference; adjusting and optimizing integral parameters based on real-time estimated quality; adjusting and optimizing an integral parameter based on the current road gradient; and adjusting and optimizing the integral parameter based on the road adhesion coefficient.

In some embodiments, the determination module 42 is configured to perform a differential parameter adjustment optimization, including: adjusting and optimizing differential parameters based on the expected vehicle speed and the real-time vehicle speed difference; adjusting and optimizing differential parameters based on real-time estimation quality; adjusting and optimizing differential parameters based on the current road gradient; and adjusting optimization based on the differential parameter of the road attachment coefficient.

In some embodiments, the determination module 42 is configured to perform the obtaining of the electric machine requested torque using a vehicle dynamics equation, including:

the motor required torque is calculated using the following equation:

wherein T represents the torque required by the motor, rho represents the air density, A represents the windward area, and C _D Representing an air resistance coefficient, v representing the current vehicle speed, f representing a road rolling resistance coefficient, m representing a real-time estimated mass, g representing a gravity coefficient, i representing the current road gradient, δ representing an automobile rotating mass conversion coefficient after the inertia moment of the rotating mass is counted, a representing an acceleration and deceleration control parameter, r representing the effective radius of a tire of the automobile, K representing the speed ratio of the automobile, and η representing the efficiency of a transmission machine.

In some embodiments, the determining module 42 is configured to limit and filter the requested torque of the motor to obtain the target torque, and includes: limiting the motor demand torque based on the maximum available torque of the vehicle; the maximum available torque is determined based on the state of charge of a power battery in the vehicle, the allowable charging and discharging power of the power battery, the external characteristic torque of the motor, the driving and feedback torque limit of the motor and the fault condition of the whole vehicle; and filtering the motor required torque obtained after limitation to obtain a target torque.

In some embodiments, the apparatus further comprises: and the output module is used for transmitting the target torque to the controlled component and adjusting the vehicle from the current vehicle speed to the expected vehicle speed based on the controlled component.

It should be noted that the apparatus shown in fig. 5 is capable of performing the steps of any one of the methods provided by the foregoing embodiments, and achieves the corresponding advantages.

The embodiment of the present disclosure further provides a computer-readable storage medium, where a computer program is stored, where the computer program is used to execute the steps of any one of the methods provided in the foregoing embodiments, and has corresponding beneficial effects.

The embodiment of the disclosure also provides vehicle equipment. Referring to fig. 6, the vehicular apparatus 40 includes: a processor 420; a memory 410 for storing instructions executable by processor 420; the processor 420, configured to read the executable instructions from the memory 410 and execute the executable instructions to implement the steps of any one of the methods provided by the above embodiments, has corresponding advantages.

The embodiment of the disclosure also provides a vehicle, which comprises any one of the vehicle devices provided by the above embodiments, and has corresponding beneficial effects.

It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or apparatus that comprises the element.

The foregoing are merely exemplary embodiments of the present disclosure, which enable those skilled in the art to understand or practice the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A vehicle speed control method of an autonomous vehicle, characterized by comprising:

acquiring real-time state data of a vehicle; the real-time state data comprises real-time estimated mass, current vehicle speed, expected vehicle speed and road condition information; the traffic information includes: current road grade, road adhesion coefficient and road rolling resistance coefficient;

determining a target torque based on the real-time status data;

2. The method of claim 1, wherein determining a target torque based on the real-time status data comprises:

and limiting and filtering the required torque of the motor to obtain the target torque.

3. The method of claim 2, wherein generating acceleration and deceleration control parameters based on the current vehicle speed and the desired vehicle speed using vehicle speed difference processing and PID control comprises:

performing parameter adjustment optimization of PID control based on the four factors to obtain a target proportional parameter, a target integral parameter and a target differential parameter; the four parts of factors include: the expected speed is different from the real-time speed, the mass is estimated in real time, the current road gradient is estimated, and the road attachment coefficient is estimated;

4. The method of claim 3, wherein prior to performing the parameter tuning optimization for PID control, the method further comprises:

5. The method according to claim 4, wherein the correction condition comprises:

the desired vehicle speed is greater than a vehicle speed threshold; and is

6. The method of claim 3, wherein determining a real-time vehicle speed differential based on the current vehicle speed and the desired vehicle speed in combination with a vehicle operating mode and a vehicle speed differential threshold comprises:

7. A vehicle speed control apparatus of an autonomous vehicle, characterized by comprising:

the acquisition module is used for acquiring real-time state data of the vehicle; the real-time state data comprises real-time estimated mass, current vehicle speed, expected vehicle speed and road condition information; the traffic information includes: the current road gradient, road adhesion coefficient and road rolling resistance coefficient;

8. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for performing the steps of the method according to any of claims 1-6.

9. An apparatus for a vehicle, comprising: a memory for storing the processor-executable instructions; the processor configured to read the executable instructions from the memory and execute the executable instructions to implement the steps of the method according to any one of claims 1-6.

10. A vehicle characterized by comprising the vehicular apparatus according to claim 9.