CN110228470B - Fuel saving rate real-time calculation method based on hidden vehicle model prediction - Google Patents

Fuel saving rate real-time calculation method based on hidden vehicle model prediction Download PDF

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CN110228470B
CN110228470B CN201910475682.4A CN201910475682A CN110228470B CN 110228470 B CN110228470 B CN 110228470B CN 201910475682 A CN201910475682 A CN 201910475682A CN 110228470 B CN110228470 B CN 110228470B
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real
torque
oil consumption
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CN110228470A (en
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闫勇军
高炳钊
汪介瑜
董世营
孙呈祥
陈虹
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Jilin University
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
    • B60R16/00—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for
    • B60R16/02—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements
    • B60R16/023—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements for transmission of signals between vehicle parts or subsystems
    • B60R16/0231—Circuits relating to the driving or the functioning of the vehicle
    • B60R16/0232—Circuits relating to the driving or the functioning of the vehicle for measuring vehicle parameters and indicating critical, abnormal or dangerous conditions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
    • B60W30/14—Adaptive cruise control
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • B60W40/06—Road conditions
    • B60W40/076—Slope angle of the road
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0019—Control system elements or transfer functions
    • B60W2050/0028—Mathematical models, e.g. for simulation
    • B60W2050/0031—Mathematical model of the vehicle
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/80—Technologies aiming to reduce greenhouse gasses emissions common to all road transportation technologies
    • Y02T10/84—Data processing systems or methods, management, administration

Abstract

The invention belongs to the technical field of vehicle information, and particularly relates to a real-time oil saving rate calculation method based on hidden vehicle model prediction. The calculation method comprises the following steps: step one, calculating the real vehicle oil consumption controlled by an optimized cruise control system; calculating the oil consumption of the reference cruise control system through the virtual hidden model, wherein the parameters of the virtual hidden vehicle model are reset by real vehicle data according to the switching of the following targets, so that the model error is reduced as much as possible; and step three, comparing the oil consumption of the two systems calculated in the step one and the step two to obtain the oil saving rate. The invention adopts a hidden oil consumption model method, so that the oil consumption calculation result can better reflect the influence of traffic conditions on the cruise system, and the error of the oil consumption comparison result is reduced, thereby solving the real-time oil saving rate and solving the problem of the existing cruise system in real-time oil consumption calculation.

Description

Fuel saving rate real-time calculation method based on hidden vehicle model prediction
Technical Field
The invention belongs to the technical field of vehicle information, and particularly relates to a real-time oil saving rate calculation method based on hidden vehicle model prediction.
Background
With the development of V2X communication technologies such as 5G and the like, map information, ambient environment information and the like can be used for controlling vehicles, and the possibility is provided for realizing the purposes of energy conservation and emission reduction through multi-level planning, optimization and control in the smart city interconnection information environment. The development planning of the national level is provided by all countries in the world aiming at energy conservation and emission reduction under the intelligent network connection.
The method fully utilizes big data information to realize energy conservation and emission reduction in the era of intelligent networking and becomes a future key research and development direction for domestic and foreign vehicle enterprises. Ford automotive corporation filed a patent for designing a predictive cruise energy-saving control system by utilizing nonlinear model predictive control in 2017, and international vehicle enterprises and suppliers such as general, daily products, continents, ZF and the like also developed corresponding researches. The domestic invention patent CN 201710291137.0 authorizes a real-time prediction cruise control system based on economical driving, which comprises: the information acquisition module: the system is used for acquiring the running state information of the current vehicle and the front vehicle, wherein the running state information comprises speed information, the distance between the current vehicle and the front vehicle and road traffic speed limit information in a predicted distance; transmitting the collected information to a vehicle dynamics model building module; the vehicle dynamics model building module builds a vehicle dynamics model according to the acquired traffic speed limit information and the driving state information of the front vehicle and the vehicle, builds a control problem and determines an optimized target and a satisfied constraint condition; a rolling time domain optimization calculation module: based on the control problems and constraint conditions provided by a vehicle dynamics model building module, the optimal gear sequence, the optimal engine torque and the optimal brake force display solution are obtained through optimization by a method of combining a Ponderland minimum value principle and a dichotomy, and the optimal control law is determined.
However, when the real-time oil consumption of the system is calculated and the oil saving effect of the system is tested, the prediction cruise system and the benchmarking reference system are generally adopted to respectively perform multiple tests on the same route, and the average value of the oil consumption of one hundred kilometers of each system is obtained to calculate the oil saving rate. As a conventional method for calculating the oil consumption in the real vehicle test, the method is simple and practical, but has a plurality of defects, for example, the working conditions of the traffic environments of two comparison systems in the real vehicle test are different, and the effect of the control system on the oil saving effect cannot be accurately and objectively proved under the condition of eliminating the influence of the external environment. In addition, in the development task of mass production of the system, the fuel-saving effect is displayed to the passengers in real time in a mode of increasing the endurance mileage when the system is started. Therefore, the definition of the oil-saving reference and the real-time display of the oil-saving effect become problems which need to be solved urgently.
Domestic patent CN 201810783843.1 discloses a real-time estimation method for automobile oil consumption based on a mobile terminal, which comprises the following steps: acquiring X-axis data and Y-axis data of an acceleration sensor and X-axis data of a gyroscope, and acquiring vehicle running acceleration with a gravity component through the X-axis data of the acceleration sensor; obtaining the road gradient through Y-axis data of an acceleration sensor, and obtaining the road gradient through X-axis data of a gyroscope; fusing data collected by an acceleration sensor and a gyroscope to obtain corrected acceleration and an optimal slope estimation value, and obtaining the real acceleration of the automobile according to the corrected acceleration; and integrating the real acceleration of the automobile to obtain the running speed of the automobile. The method and the device realize real-time estimation of the vehicle running acceleration, speed and oil consumption by using the embedded sensor of the mobile terminal, and are used for evaluating the green degree of the driving behavior of the driver, so that the driver is helped to develop green driving habits, and the fuel consumption is reduced. The method can realize real-time estimation of the vehicle oil consumption and has good reference significance.
Domestic patent CN 201410816008.5 discloses a method and a device for predicting oil consumption, the method is: determining at least one target road condition information contained in the road to be predicted according to the electronic map, calling a fuel consumption model corresponding to the target road condition information from a preset corresponding relation between the road condition information and the fuel consumption model for each target road condition information, calculating the fuel consumption of the road to be predicted under each target road condition according to the target road condition information and the corresponding fuel consumption model, and finally determining the fuel consumption and the value of the road to be predicted under each target road condition as the total fuel consumption of the road to be predicted. According to the scheme, the fact that the road to be predicted possibly contains various different target road condition information is considered, the preset corresponding oil consumption model is called to predict oil consumption, the finally obtained total oil consumption is closer to the actual situation, and the accuracy is higher.
The above patents all provide an effective method for calculating the oil consumption of the vehicle, but when the oil saving rate of a control system is obtained, a method for setting the reference oil consumption as a constant value is often adopted, the consideration of the difference of the working conditions of the traffic environments of two comparison systems during the real vehicle test is lacked, and the effect of the control system on the oil saving effect cannot be accurately and objectively proved under the condition of eliminating the influence of the external environment.
Disclosure of Invention
According to the invention, a hidden oil consumption model method is adopted, so that the oil consumption comparison result can be changed according to the change of the traffic environment, the error of the oil consumption comparison result is reduced, and the problems of the existing predicted oil consumption are solved.
The technical scheme of the invention is described as follows by combining the attached drawings:
a real-time oil saving rate calculation method based on hidden vehicle model prediction comprises the following steps:
step one, calculating the real vehicle oil consumption controlled by an optimized cruise control system;
step two, calculating the oil consumption of the reference cruise control system through the virtual hidden vehicle model;
and step three, comparing the oil consumption of the two systems calculated in the step one and the step two to obtain the oil saving rate.
The specific method of the first step is as follows:
obtaining the relative distance and the relative speed information of a front target object required by the optimized cruise control system for calculation and the state information of the vehicle, namely the vehicle speed, the engine rotating speed, the engine torque, the current gear and the gradient curvature information of a front road from a sensing information module and a power CAN information module of the vehicle, performing rolling optimization by the optimized cruise control system by using model prediction control after obtaining the information, and then calculating the required braking deceleration and driving torque by combining the Ponderland gold minimum value principle and the dichotomy; the braking deceleration and the driving torque command which are calculated in real time by the optimized cruise control system are output to EMS and ESC of the vehicle through a vehicle power CAN to be executed, so that the real vehicle is controlled to follow or cruise; at the moment, the engine torque and the rotating speed of the real vehicle are obtained through CAN communication and then input into the oil consumption calculation module, the instantaneous oil consumption is obtained through searching the oil consumption MAP table, and then the real vehicle oil consumption under the control of the optimized cruise control system is calculated through dynamic correction.
The specific method of the second step is as follows:
obtaining the relative distance and relative speed information of a front target object required by the reference cruise system for calculation and the state information of the vehicle, namely the vehicle speed, the engine rotating speed, the engine torque and the current gear from a sensing information module and a power CAN information module of the vehicle, and then calculating the braking deceleration and the driving torque required by the control vehicle for cruise running under the current relative time distance, namely the relative distance, divided by the vehicle speed by the reference cruise control system through a PID algorithm; calculating expected braking deceleration and driving torque, entering a virtual hidden vehicle model to control the vehicle to run, and resetting parameters of the virtual hidden vehicle model and parameters of a vehicle-following target object by using real parameters of the vehicle, namely the vehicle speed of the vehicle, the engine speed, the engine torque, the current gear and front vehicle parameters, namely the relative distance and the relative speed between the front vehicle and the vehicle, when the vehicle-following target object changes, so that vehicle speed accumulated errors in the virtual hidden vehicle model caused by model errors are eliminated, and front vehicle information obtained by an environment sensing module is simultaneously sent to the real vehicle and the virtual vehicle, so that the hidden vehicle model can run in a real traffic scene.
The construction method of the virtual hidden vehicle model comprises the following steps:
the vehicle model built by the Simulink comprises a torque and throttle opening conversion module, an engine module, a transmission system module and a longitudinal dynamics module; the vehicle model has the input of the required engine torque and the braking deceleration and the output of the engine speed and the real engine torque.
In the torque and throttle opening conversion module, because the control algorithm gives a required engine torque command, the required engine torque command needs to be converted into a throttle opening command to be executed by the engine module; in the module, the opening degree of a throttle valve is obtained by table look-up of the real rotating speed of the engine and the required engine torque, and then the opening degree of the throttle valve is regulated by PID (proportion integration differentiation) to enable the real engine torque to follow the required engine torque; when the required engine torque is less than zero, the throttle opening is zero; the table lookup data is obtained from real engine bench data, but the data of the full throttle opening is lacked in the data table, the full throttle opening condition rarely exists under the real condition, so that the data of the maximum throttle opening is assumed to be 84% of the throttle opening in the table, and an error exists;
the engine module is built by multiplying the obtained throttle opening by the maximum output torque of the engine at the current engine speed to obtain the effective output torque of the engine at the current time, then comparing the effective output torque with the rated maximum output torque of the engine to obtain the minimum value to obtain the output torque of the engine at the current time, obtaining the maximum average effective pressure BMEP at the current time by the maximum output torque of the engine at the current engine speed through data provided in the engine bench data, and obtaining the maximum average effective pressure BMEP of the engine through a formula after obtaining the maximum average effective pressure BMEP of the engine
T(Nm)＝BMEP(bar)*V(L)/(4*pi*0.01)
Calculating to obtain the effective output torque of the engine of the vehicle at the current rotating speed, wherein V represents the engine displacement, and other parameters are substituted by real vehicle parameters;
the construction of the transmission system module is that the opening of a throttle valve obtained by the engine module and the vehicle speed enter the transmission system module to determine a gear at the current moment and a corresponding transmission ratio, the engine module outputs real torque to be multiplied by the transmission ratio and the efficiency of a hydraulic torque converter, then the multiplication is divided by the radius of a wheel to obtain driving torque of a transmission shaft, the driving torque of the transmission shaft is added with braking torque to obtain torque of the transmission shaft, the torque of the transmission shaft is output to a vehicle longitudinal dynamic model, and the rotational speed of the engine is obtained by reversely deducing the vehicle speed to search the efficiency of; a state machine model is adopted, and instructions of gear-up and gear-down are determined based on two parameters of vehicle speed and throttle opening.
The original formula of the vehicle longitudinal dynamics model is as follows:
Ft＝Ff+Fw+Fj+Fi。
wherein, FtFor driving force, the unit is N, which is zero since the vehicle is coasting in neutral, FfExpressed as F, is the frictional resistance in NfMg · f; wherein Mg is vehicle weight, the unit is N, F is rolling resistance coefficient of the vehicle, and F iswThe specific expression form is the air resistance of the vehicle in N
Figure GDA0002955811460000041
Wherein C isAIs the coefficient of air resistance, v0The unit is the speed of the vehicle when the vehicle starts to slide, and the unit is m/s, rho is the air density, and the unit is Kg/m3S is the frontal area of the vehicle, and the unit is m2，FjIs inertial force of vehicle, and has specific expression of Fj＝M·apWherein M is the mass of the whole vehicle in kg, apIs the coasting acceleration in neutral, in m/s2，FiIs the ramp resistance of the vehicle, with the unit of N; the longitudinal dynamics model is a traditional vehicle longitudinal dynamics equation, but the mass M and the gradient alpha of the whole vehicle are parameters to be identified, and influence is exerted on the vehicle longitudinal dynamics.
The identification process of the gradient alpha is as follows:
according to the working principle of the acceleration sensor, when the vehicle goes up a slope or goes down a slope, the acceleration measured by the acceleration sensor is actually the sum of the longitudinal acceleration of the vehicle and the gravity acceleration along the slope;
the calculation formula of the gradient is as follows:
Figure GDA0002955811460000042
wherein, avAcceleration of the vehicle derived for speed, asenThe current road gradient can be obtained by dividing the difference of the acceleration measured by an acceleration sensor of the vehicle by the acceleration g; since there is a transmission line buffeting, a vehicle speed signal has a dither of a fixed frequency, and the period of the dither decreases as the vehicle speed increases, when differentiating the vehicle speed, one period is selected for differentiation, after differentiation, a limiter is performed and a low-pass filter is used for filtering, and an acceleration signal of the vehicle also needs to be limited and filtered by a low-pass filter.
The identification process of the finished automobile mass M is as follows:
identifying the whole vehicle mass M by adopting a recursive least square algorithm;
the conventional vehicle longitudinal dynamics equation is:
Ft＝Fw+Mgf+Mgi+Ma
wherein, FtFor the driving force of the vehicle, since the vehicle driving force data can not be directly obtained, the driving force output by the engine is required to obtain the real driving force of the vehicle through the conversion of a transmission system, FwThe air resistance for the vehicle to run, Mgf the rolling resistance for the vehicle to run, Mgi the ramp resistance for the vehicle, i the sine of the slope; ma is the acceleration resistance of the vehicle;
converting to a recursive least squares format, one can obtain:
γ(k)＝P(k-1)a_e(k)[a_e(k)P(k-1)a_e(k)+μ(k)]-1
wherein e is process white noise, and the above formula is transformed again to obtain:
Ftw＝θ·a_e+e
wherein θ ═ M represents a parameter to be identified, and a _ e ═ gf + gi + a represents an observable data vector;
according to the least square principle, the least square recursion format of the system can be obtained as follows:
Figure GDA0002955811460000051
γ(k)＝P(k-1)a_e(k)[a_e(k)P(k-1)a_e(k)+μ(k)]-1
P(k)＝μ(k)-1[I-γ(k)a_e(k)]P(k-1)
where μ (k) is the forgetting factor at time k, γ (k) is the gain matrix at time k, Ftw(k) Represents the system input at time k,
Figure GDA0002955811460000052
representing the quality of the identification at time k, in recursive least squares identification,
Figure GDA0002955811460000053
and γ (k) needs to be preset with an initial value, I represents the identity matrix, and p (k) represents the transition matrix at time k.
The specific process of calculating the oil consumption by using the oil consumption model is as follows:
the oil consumption model adopts a method of looking up the engine oil consumption MAP by the engine rotating speed and the engine torque, but the MAP is statically calibrated by an engine bench test, and the actual oil consumption data of the oil consumption meter needs to be corrected to achieve dynamic compensation.
Figure GDA0002955811460000054
Wherein FinsIn order to optimize the accumulated oil consumption before the correction of the real vehicle under the control of the cruise control system or the benchmark cruise control system,
Figure GDA0002955811460000055
the instantaneous oil consumption found through the rotating speed and the torque of the engine is represented, the integral can obtain the accumulated oil consumption, and then the corrected oil consumption is obtained through correction;
Ffinal＝a·Fins 2+b·Fins+c
the formula being a correction function, FfinalAnd a, b and c are correction formula parameters.
The specific process of the third step is as follows:
the calculated fuel consumption of the reference cruise control system is compared with the real fuel consumption of the optimized cruise control system every 1km, so that the fuel saving rate of the optimized cruise control system compared with the reference cruise control system can be obtained;
Figure GDA0002955811460000061
wherein phi is the fuel saving rate, FoptFor optimizing the fuel consumption of the real vehicle under the control of a cruise control system, FbenchmarkFuel consumption of a hidden vehicle model under the control of a reference cruise control system; in addition, due to the optimized cruise controlThe oil consumption of the system is also calculated in an accumulated way and is not cleared, so that the hundred kilometer oil consumption of the optimized cruise control system can be obtained; and finally, inputting the finally obtained oil saving rate and the hundred kilometers of oil consumption into a human-computer interaction interface for displaying through CAN communication.
The invention has the beneficial effects that:
1. according to the invention, an accurate vehicle longitudinal dynamics model is constructed through a reasonable model building method, and is inspired by model prediction control, when a following target changes, a hidden model and a preceding vehicle state parameter are reset by using real vehicle parameters, fuel consumption calculation of vehicle cruise control is divided into a plurality of following stages according to the following target, vehicle parameter updating is carried out by switching based on the following target, the speed fluctuation of the vehicle caused by switching of the following target under a real following condition is depicted, and each virtual hidden vehicle model can well reflect the following control effect of a reference controller algorithm under the same traffic scene, so that the following driving style of the reference control algorithm can be maximally embodied.
2. According to the method, the road gradient is estimated by utilizing CAN bus information and a running equation, and the problem of vehicle model precision is solved by identifying the mass of the whole vehicle by using a recursive least square method;
3. according to the method, the traditional method for calculating the oil consumption of the engine by searching the oil consumption MAP table by using the rotating speed and the torque of the engine is improved and corrected through actual oil consumption data, so that the calculated oil consumption of the engine is more accurate.
Drawings
FIG. 1 is a schematic diagram of a system for real-time calculation and comparison of fuel consumption of a vehicle using a hidden vehicle model;
FIG. 2 is a diagram of a fuel consumption calculation architecture of a virtual hidden vehicle model;
FIG. 3 is a Simulink model diagram of a vehicle model;
FIG. 4 is a schematic diagram of slope estimation;
FIG. 5 is a block diagram of a slope estimation design;
FIG. 6 is a schematic diagram of a recursive least squares mass identification architecture;
fig. 7 is a schematic diagram of a fuel consumption model data correction module.
Detailed Description
On the premise of not changing a system software architecture, the invention provides a vehicle oil consumption real-time calculation and comparison system adopting a hidden vehicle model. The system principle is shown in figure 1, under the condition that the real vehicle is controlled to run by the optimization control algorithm, the fuel consumption of the optimization control algorithm is obtained by interpolation calculation of the engine speed and the torque of the real vehicle, and the fuel consumption is updated once every 1km and is not cleared. At the same time, a virtual hidden vehicle model controlled by a reference contrast algorithm is also run in the controller. Now that a virtual hidden vehicle model is employed, the model error problem becomes unavoidable. In order to solve the problem, the method is inspired by a model prediction control principle, when a following vehicle target changes, a real vehicle parameter and a front vehicle parameter are used for resetting a virtual vehicle model parameter and a following vehicle target parameter, so that a vehicle speed accumulated error in a virtual hidden vehicle model caused by a model error is eliminated, front vehicle information obtained by an environment sensing module is simultaneously sent to a real vehicle and a virtual vehicle, and the problem that the reliability of a fuel consumption comparison result is reduced due to different traffic environments can be effectively solved. The interpolation of the engine speed and the torque of the hidden vehicle model results in the fuel consumption of the benchmark contrast algorithm, which will also be updated every 1km and cumulatively increased. Therefore, the fuel consumption of the real vehicle is compared with the fuel consumption of the virtual vehicle, the real-time fuel consumption rate of the optimization control algorithm can be obtained, and the increased endurance mileage can be calculated.
The method greatly enables the benchmark comparison algorithm to operate in the same traffic environment of the optimization control algorithm in a simulation mode, and by means of the idea of model prediction control, the fuel consumption of the benchmark comparison algorithm in the same vehicle following target is predicted, when the vehicle following target in front is changed, the vehicle speeds of two vehicles are forcibly kneaded, the calculation and comparison of the fuel consumption are performed in a rolling mode, and errors of fuel consumption comparison results caused by irreproducibility of the traffic environment when the cruise control algorithm is measured and optimized can be effectively avoided.
A specific virtual hidden model oil consumption calculation framework is shown in fig. 2. After the vehicle information and the environment information enter a reference comparison algorithm, an engine demand driving torque and a brake deceleration command are output to the virtual hidden vehicle model, the real engine torque and the engine rotating speed of the virtual hidden vehicle model are output to the oil consumption calculation module to calculate the accumulated oil consumption of the virtual vehicle, the accumulated oil consumption is sent out once every 1km, and the accumulated oil consumption and the real vehicle oil consumption are calculated to obtain the oil saving rate.
However, the use of vehicle models also raises another problem — the accuracy of the models. Mismatching between the vehicle model and a real vehicle causes misalignment of the vehicle running state, so that distortion of real-time oil consumption of a reference comparison algorithm system is caused.
For a hidden vehicle model, a precise vehicle longitudinal dynamics model is built in Simulink and comprises an engine model, a transmission system model and a longitudinal dynamics model, wherein the influence of the change of the gradient and the finished vehicle mass during the vehicle running on the vehicle oil consumption is also very large, so that the gradient of a road is estimated by using CAN bus information and a running equation, and the finished vehicle mass is identified by using a recursive least square method to solve the precision problem of the vehicle model.
The specific implementation of the invention can be divided into three steps:
step one, calculating the real vehicle oil consumption controlled by an optimized cruise control system; the method comprises the following specific steps:
obtaining information such as relative distance and relative speed of a front target object required by the optimized cruise control system for calculation and state information of the vehicle, namely the vehicle speed, the engine rotating speed, the engine torque, the current gear, the gradient curvature of a front road and the like from a sensing information module and a power CAN information module of the vehicle, performing rolling optimization by the optimized cruise control system by using model prediction control after obtaining the information, and calculating the required braking deceleration and driving torque by combining the Pondyli gold minimum value principle and the dichotomy; the braking deceleration and the driving torque command which are calculated in real time by the optimized cruise control system are output to EMS and ESC of the vehicle through a vehicle power CAN to be executed, so that the real vehicle is controlled to follow or cruise; at the moment, the engine torque and the rotating speed of the real vehicle are obtained through CAN communication and then input into the oil consumption calculation module, the instantaneous oil consumption is obtained through searching the oil consumption MAP table, and then the real vehicle oil consumption under the control of the optimized cruise control system is calculated through dynamic correction.
The specific process of the oil consumption calculation of the oil consumption model is as follows:
the oil consumption model adopts a method of looking up the engine oil consumption MAP by the engine rotating speed and the engine torque, but the MAP is statically calibrated by an engine bench test, and needs to be corrected by the real oil consumption data of the oil consumption meter to achieve dynamic compensation, and the structural frame diagram of the MAP is shown in FIG. 7. The fitting formula is:
Figure GDA0002955811460000081
wherein, FinsIn order to optimize the accumulated oil consumption before the correction of the real vehicle under the control of the cruise control system or the benchmark cruise control system,
Figure GDA0002955811460000082
the instantaneous oil consumption found through the rotating speed and the torque of the engine is represented, the integral can obtain the accumulated oil consumption, and then the corrected oil consumption is obtained through correction;
Ffinal＝a·Fins 2+b·Fins+c
the formula being a correction function, FfinalAnd a, b and c are correction formula parameters.
Calculating the oil consumption of the reference cruise control system through the virtual hidden model; the method comprises the following specific steps:
obtaining information such as relative distance and relative speed of a front target object required by a reference cruise system for calculation and state information of the vehicle, namely vehicle speed, engine rotating speed, engine torque and current gear from a sensing information module and a power CAN information module of the vehicle, and then calculating braking deceleration and driving torque required by controlling the vehicle to cruise under the condition that the current relative time distance, namely the relative distance, is divided by the vehicle speed by the reference cruise control system through a PID algorithm; calculating expected braking deceleration and driving torque, entering a virtual hidden vehicle model to control the vehicle to run, and resetting parameters of the virtual hidden vehicle model and parameters of a vehicle-following target object by using real parameters of the vehicle, namely the vehicle speed of the vehicle, the engine speed, the engine torque, the current gear and front vehicle parameters, namely the relative distance and the relative speed between the front vehicle and the vehicle, when the vehicle-following target object changes, so that vehicle speed accumulated errors in the virtual hidden vehicle model caused by model errors are eliminated, and front vehicle information obtained by an environment sensing module is simultaneously sent to the real vehicle and the virtual vehicle, so that the hidden vehicle model can run in a real traffic scene.
The construction method of the virtual hidden vehicle model comprises the following steps:
referring to fig. 3, the vehicle model built by Simulink comprises a torque and throttle opening conversion module, an engine module, a transmission system module and a longitudinal dynamics module; the vehicle model has the input of the required engine torque and the braking deceleration and the output of the engine speed and the real engine torque.
In the torque and throttle opening conversion module, because the control algorithm gives a required engine torque command, the required engine torque command needs to be converted into a throttle opening command to be executed by the engine module; in the module, the opening degree of a throttle valve is obtained by table look-up of the real rotating speed of the engine and the required engine torque, and then the opening degree of the throttle valve is regulated by PID (proportion integration differentiation) to enable the real engine torque to follow the required engine torque; when the required engine torque is less than zero, the throttle opening is zero; the table look-up data is obtained from real engine mount data, but the data of the full throttle is missing in the data table (the full throttle condition is rarely present in real conditions), so that the maximum throttle opening data is assumed to be 84% of the throttle opening in the table, and some unavoidable errors exist.
The engine module is built by multiplying the obtained throttle opening by the maximum output torque of the engine at the current engine speed to obtain the effective output torque of the engine at the current time, then comparing the effective output torque with the rated maximum output torque of the engine to obtain the minimum value to obtain the output torque of the engine at the current time, obtaining the maximum average effective pressure BMEP at the current time by the maximum output torque of the engine at the current engine speed through data provided in the engine bench data, and obtaining the maximum average effective pressure BMEP of the engine through a formula after obtaining the maximum average effective pressure BMEP of the engine
T(Nm)＝BMEP(bar)*V(L)/(4*pi*0.01)
The engine effective output torque of the vehicle at the current speed is calculated, wherein V represents the engine displacement, the east wind G29_ EW10 engine has the value of 1.997L, and other parameters are substituted by real vehicle parameters.
The construction of the transmission system module is that the opening of a throttle valve obtained by the engine module and the vehicle speed enter the transmission system module to determine a gear at the current moment and a corresponding transmission ratio, the engine module outputs real torque to be multiplied by the transmission ratio and the efficiency of a hydraulic torque converter, then the multiplication is divided by the radius of a wheel to obtain driving torque of a transmission shaft, the driving torque of the transmission shaft is added with braking torque to obtain torque of the transmission shaft, the torque of the transmission shaft is output to a vehicle longitudinal dynamic model, and the rotational speed of the engine is obtained by reversely deducing the vehicle speed to search the efficiency of; a state machine model is adopted, and instructions of gear-up and gear-down are determined based on two parameters of vehicle speed and throttle opening.
The original formula of the vehicle longitudinal dynamics model is as follows:
Ft＝Ff+Fw+Fj+Fi。
wherein, FtFor driving force, the unit is N, which is zero since the vehicle is coasting in neutral, FfExpressed as F, is the frictional resistance in NfMg · f; wherein Mg is vehicle weight, the unit is N, F is rolling resistance coefficient of the vehicle, and F iswThe specific expression form is the air resistance of the vehicle in N
Figure GDA0002955811460000091
Wherein C isAIs the coefficient of air resistance, v0The unit is the speed of the vehicle when the vehicle starts to slide, and the unit is m/s, rho is the air density, and the unit is Kg/m3S is the frontal area of the vehicle, and the unit is m2，FjIs inertial force of vehicle, and has specific expression of Fj＝M·apWherein M is the mass of the whole vehicle in kg, apIs the coasting acceleration in neutral, in m/s2，FiIs the ramp resistance of the vehicle, with the unit of N; the longitudinal dynamics module is a traditional vehicle longitudinal dynamics equation, but the mass M and the gradient alpha of the whole vehicle are parameters to be identified, and influence is exerted on the vehicle longitudinal dynamics.
The identification process of the gradient alpha is as follows:
the influence of the gradient on the longitudinal dynamics of the vehicle is very large, so that the tracking effect of the inter-vehicle distance or the vehicle speed is influenced; the accurate longitudinal dynamics model can effectively reduce the steady-state error of the model, thereby reducing the integral gain in the controller. At present, there are 3 methods that can estimate the road gradient in real time: estimating the road gradient by using GPS elevation information; estimating the road gradient by using CAN bus information and a running equation; an acceleration sensor is additionally added to estimate the road gradient. Since the test vehicle has an acceleration sensor output signal and the method is not coupled with the vehicle mass waiting identification parameter, the third method is adopted to calculate the gradient of the vehicle.
As can be seen from the operating principle of the acceleration sensor, when the vehicle is going uphill or downhill, the acceleration measured by the acceleration sensor is actually the sum of the vehicle longitudinal acceleration and the gravity acceleration along the slope, as shown in fig. 4.
The calculation formula for the available gradient is then:
Figure GDA0002955811460000101
wherein, avFor the acceleration of the vehicle derived from the speed,asenthe current road gradient can be obtained by dividing the difference of the acceleration measured by an acceleration sensor of the vehicle by the acceleration g; since there is a transmission line buffeting, the vehicle speed signal has a fixed frequency dither, and the period of the dither decreases as the vehicle speed increases, when differentiating the vehicle speed, one period is selected for differentiation, after differentiation, amplitude limiting and filtering processing by a low-pass filter are performed, and the acceleration signal of the vehicle also needs to be amplitude limited and filtered by a low-pass filter, and a specific design block diagram is shown in fig. 5.
The completion of slope estimation can reduce the dependence on a high-precision map, improve the running precision of the virtual hidden vehicle model, and provide a new thought and possibility for the mass production scheme of the cruise prediction algorithm.
The identification process of the finished automobile mass M is as follows:
as is known, the mass M of the whole vehicle has a great influence on the fuel consumption, and in the vehicle test process, the mass of the whole vehicle varies due to the difference in the number of people in the vehicle and the consumption of fuel on the vehicle, and if the mass of the whole vehicle is given to a vehicle model as a constant, the mass M of the whole vehicle will have a great influence on the fuel consumption of the vehicle. As the mass of the whole vehicle is a slowly-changing parameter, no high requirement is made on the real-time performance of the identification algorithm.
The invention adopts the most classical and basic parameter identification algorithm, namely the recursive least square algorithm, and firstly, the traditional vehicle longitudinal dynamics equation is as follows:
Ft＝Fw+Mgf+Mgi+Ma
wherein, FtFor the driving force of the vehicle, since the vehicle driving force data can not be directly obtained, the driving force output by the engine is required to obtain the real driving force of the vehicle through the conversion of a transmission system, FwThe air resistance for the vehicle to run, Mgf the rolling resistance for the vehicle to run, Mgi the ramp resistance for the vehicle, i the sine of the slope; ma is the acceleration resistance of the vehicle;
converting to a recursive least squares format, one can obtain:
γ(k)＝P(k-1)a_e(k)[a_e(k)P(k-1)a_e(k)+μ(k)]-1
wherein e is process white noise, and the above formula is transformed again to obtain:
Ftw＝θ·a_e+e
wherein θ ═ M represents a parameter to be identified, and a _ e ═ gf + gi + a represents an observable data vector;
according to the least square principle, the least square recursion format of the system can be obtained as follows:
Figure GDA0002955811460000111
γ(k)＝P(k-1)a_e(k)[a_e(k)P(k-1)a_e(k)+μ(k)]-1
P(k)＝μ(k)-1[I-γ(k)a_e(k)]P(k-1)
in order to avoid the phenomenon, the concept of the forgetting factor is provided, so that the correction effect of the new data on the parameter estimation value becomes stronger, the correction effect of the old data on the parameter estimation value becomes weaker, and the problem of data saturation is solved. The larger the forgetting factor is, the higher the accuracy of system recognition is, but at the same time, the longer the convergence time of the system recognition result is caused. When the forgetting factor is too small, the accuracy of system identification becomes low, but the convergence time of the system identification result is shortened. Herein, the forgetting factor is chosen to be 0.95. In the case of a recursive least-squares identification,
Figure GDA0002955811460000112
and γ (k) needs to be preset, and there are two methods for setting the initial value in general
Figure GDA0002955811460000113
The element in (1) is zero or a smaller parameter, and gamma (k) is equal to alpha.I, wherein alpha is 105～1010The real number of (2). After the recursive equation of the recursive least square mass identification is obtained, the recursive least square mass identification structure is shown in fig. 6.
The completion of the vehicle quality identification can improve the running precision of the virtual hidden vehicle model, so that the oil consumption estimation of the virtual hidden vehicle model is more accurate, and the method is the basis for establishing an oil-saving reference and displaying the oil-saving effect in real time.
Step three, comparing the oil consumption of the two systems calculated in the step one and the step two to obtain the oil saving rate; the method comprises the following specific steps:
the oil consumption model adopts a method of looking up the engine oil consumption MAP by the engine rotating speed and the engine torque, but the MAP is statically calibrated by an engine bench test, and the actual oil consumption data of the oil consumption meter needs to be corrected to achieve dynamic compensation.
Figure GDA0002955811460000121
Wherein FinsIn order to optimize the accumulated oil consumption before the correction of the real vehicle under the control of the cruise control system or the benchmark cruise control system,
Figure GDA0002955811460000122
the instantaneous oil consumption found through the rotating speed and the torque of the engine is represented, the integral can obtain the accumulated oil consumption, and then the corrected oil consumption is obtained through correction;
Ffinal＝a·Fins 2+b·Fins+c
the formula being a correction function, FfinalAnd a, b and c are correction formula parameters.
The calculated fuel consumption of the reference cruise control system is compared with the real fuel consumption of the optimized cruise control system every 1km, so that the fuel saving rate of the optimized cruise control system compared with the reference cruise control system can be obtained;
Figure GDA0002955811460000123
wherein phi is the fuel saving rate, FoptFor optimizing the fuel consumption of the real vehicle under the control of a cruise control system, FbenchmarkFuel consumption of a virtual hidden vehicle model under the control of a reference cruise control system; in addition, because the oil consumption of the optimized cruise control system is also accumulated and not cleared, the hundred kilometer oil consumption of the optimized cruise control system can be obtained; and finally, inputting the finally obtained oil saving rate and the hundred kilometers of oil consumption into a human-computer interaction interface for displaying through CAN communication.

Claims (8)

1. A real-time oil saving rate calculation method based on hidden vehicle model prediction is characterized by comprising the following steps:
step one, calculating the real vehicle oil consumption controlled by an optimized cruise control system;
step two, calculating the oil consumption of the reference cruise control system through the virtual hidden vehicle model;
step three, comparing the oil consumption of the two systems calculated in the step one and the step two to obtain the oil saving rate;
the specific method of the first step is as follows:
obtaining the relative distance and the relative speed information of a front target object required by the optimized cruise control system for calculation and the state information of the vehicle, namely the vehicle speed, the engine rotating speed, the engine torque, the current gear and the gradient curvature of a front road from a sensing information module and a power CAN information module of the vehicle, performing rolling optimization by the optimized cruise control system by using model prediction control after obtaining the information, and then calculating the required braking deceleration and driving torque by combining the Pondylid gold minimum value principle and the dichotomy; the braking deceleration and the driving torque command which are calculated in real time by the optimized cruise control system are output to EMS and ESC of the vehicle through a vehicle power CAN to be executed, so that the real vehicle is controlled to follow or cruise; at the moment, the engine torque and the rotating speed of the real vehicle are obtained through CAN communication and then input into the oil consumption calculation module, the instantaneous oil consumption is obtained through searching the oil consumption MAP table, and then the real vehicle oil consumption under the control of the optimized cruise control system is calculated through dynamic correction;
the specific method of the second step is as follows:
obtaining the relative distance and the relative speed of a front target object required by the reference cruise control system for calculation and the state information of the vehicle, namely the vehicle speed, the engine rotating speed, the engine torque and the current gear from a sensing information module and a power CAN information module of the vehicle, and then calculating the braking deceleration and the driving torque required by the control vehicle for cruise running under the current relative time distance, namely the relative distance, divided by the vehicle speed by the reference cruise control system through a PID algorithm; calculating expected braking deceleration and driving torque, entering a virtual hidden vehicle model to control the vehicle to run, resetting parameters of the virtual hidden vehicle model and parameters of a following vehicle target by using real parameters of the vehicle, namely the vehicle speed, the engine torque, the current gear and front vehicle parameters, namely the relative distance and the relative speed between the front vehicle and the vehicle, when the following vehicle target changes, so as to eliminate vehicle speed accumulated errors in the virtual hidden vehicle model caused by model errors, sending front vehicle information obtained by an environment sensing module to the real vehicle and the virtual vehicle at the same time, realizing the running of the hidden vehicle model in a real traffic scene, and outputting the real engine torque and the engine speed of the virtual hidden vehicle model to an oil consumption calculation module to calculate the oil consumption of a reference cruise control system.
2. The real-time fuel saving rate calculation method based on the hidden vehicle model prediction as claimed in claim 1, wherein the construction method of the virtual hidden vehicle model is as follows:
the vehicle model built by the Simulink comprises a torque and throttle opening conversion module, an engine module, a transmission system module and a longitudinal dynamics module; the vehicle model has the input of the required engine torque and the braking deceleration and the output of the engine speed and the real engine torque.
3. The method for calculating fuel saving rate in real time based on the hidden vehicle model prediction as claimed in claim 2,
in the torque and throttle opening conversion module, because the control algorithm gives a required engine torque command, the required engine torque command needs to be converted into a throttle opening command to be executed by the engine module; in the module, the opening degree of a throttle valve is obtained by table look-up of the real rotating speed of the engine and the required engine torque, and then the opening degree of the throttle valve is regulated by PID (proportion integration differentiation) to enable the real engine torque to follow the required engine torque; when the required engine torque is less than zero, the throttle opening is zero; the table lookup data is obtained from real engine bench data, but the data of the full throttle opening is lacked in the data table, the full throttle opening condition rarely exists under the real condition, so that the data of the maximum throttle opening is assumed to be 84% of the throttle opening in the table, and an error exists;
the engine module is built by multiplying the obtained throttle opening by the maximum output torque of the engine at the current engine speed to obtain the effective output torque of the engine at the current time, then comparing the effective output torque with the rated maximum output torque of the engine to obtain the minimum value to obtain the output torque of the engine at the current time, obtaining the maximum average effective pressure BMEP at the current time by the maximum output torque of the engine at the current engine speed through data provided in the engine bench data, and obtaining the maximum average effective pressure BMEP of the engine through a formula after obtaining the maximum average effective pressure BMEP of the engine
T(Nm)＝BMEP(bar)*V(L)/(4*pi*0.01)
Calculating the effective output torque of the engine of the vehicle at the current rotating speed, wherein V represents the engine displacement;
the construction of the transmission system module is that the opening of a throttle valve obtained by the engine module and the vehicle speed enter a gear shifting module to determine a gear at the current moment and a corresponding transmission ratio, the engine module outputs real torque to be multiplied by the transmission ratio and the efficiency of a hydraulic torque converter, then the multiplication is divided by the radius of a wheel to obtain driving torque of a transmission shaft, the driving torque of the transmission shaft is added with braking torque to obtain the torque of the transmission shaft, the torque of the transmission shaft is output to a vehicle longitudinal dynamic model, and the efficiency of the torque converter is searched by reversely deducing the vehicle speed to obtain the; a state machine model is adopted, and instructions of gear-up and gear-down are determined based on two parameters of vehicle speed and throttle opening.
4. The real-time fuel saving rate calculation method based on the hidden vehicle model prediction as claimed in claim 3, characterized in that the original formula of the vehicle longitudinal dynamics model is as follows:
Ft＝Ff+Fw+Fj+Fi；
wherein, FtFor driving force, the unit is N, which is zero since the vehicle is coasting in neutral, FfExpressed as F, is the frictional resistance in NfMg · f; wherein Mg is vehicle weight, the unit is N, F is rolling resistance coefficient of the vehicle, and F iswThe specific expression form is the air resistance of the vehicle in N
Figure FDA0002946491670000021
Wherein C isAIs the coefficient of air resistance, v0The unit is the speed of the vehicle when the vehicle starts to slide, and the unit is m/s, rho is the air density, and the unit is Kg/m3S is the frontal area of the vehicle, and the unit is m2，FjIs inertial force of vehicle, and has specific expression of Fj＝M·apWherein M is the mass of the whole vehicle in kg, apIs the coasting acceleration in neutral, in m/s2，FiIs the ramp resistance of the vehicle, with the unit of N; the longitudinal dynamics module is a traditional vehicle longitudinal dynamics equation, but the mass M and the gradient alpha of the whole vehicle are parameters to be identified, and influence is exerted on the vehicle longitudinal dynamics.
5. The real-time fuel saving rate calculation method based on the hidden vehicle model prediction as claimed in claim 4, wherein the identification process of the gradient α is as follows:
according to the working principle of the acceleration sensor, when the vehicle goes up a slope or goes down a slope, the acceleration measured by the acceleration sensor is actually the sum of the longitudinal acceleration of the vehicle and the gravity acceleration along the slope;
the calculation formula of the gradient is as follows:
Figure FDA0002946491670000031
wherein, avAcceleration of the vehicle derived for speed, asenThe current road gradient can be obtained by dividing the difference of the acceleration measured by an acceleration sensor of the vehicle by the acceleration g; since there is a transmission line buffeting, a vehicle speed signal has a dither of a fixed frequency, and the period of the dither decreases as the vehicle speed increases, when differentiating the vehicle speed, one period is selected for differentiation, after differentiation, a limiter is performed and a low-pass filter is used for filtering, and an acceleration signal of the vehicle also needs to be limited and filtered by a low-pass filter.
6. The real-time fuel saving rate calculation method based on the hidden vehicle model prediction as claimed in claim 4, wherein the identification process of the vehicle mass M is as follows:
identifying the whole vehicle mass M by adopting a recursive least square algorithm;
the conventional vehicle longitudinal dynamics equation is:
Ft＝Fw+Mgf+Mgi+Ma
wherein, FtThe actual driving force of the vehicle is obtained by converting the driving force output by the engine through a transmission system because the vehicle driving force data cannot be directly obtained, FwThe air resistance for the vehicle to run, Mgf the rolling resistance for the vehicle to run, Mgi the ramp resistance for the vehicle, i the sine of the slope; ma is the acceleration resistance of the vehicle;
converting to a recursive least squares format, one can obtain:
γ(k)＝P(k-1)a_e(k)[a_e(k)P(k-1)a_e(k)+μ(k)]-1
wherein e is process white noise, and the above formula is transformed again to obtain:
Ftw＝θ·a_e+e
wherein θ ═ M represents a parameter to be identified, and a _ e ═ gf + gi + a represents an observable data vector;
according to the least square principle, the least square recursion format of the system can be obtained as follows:
Figure FDA0002946491670000041
γ(k)＝P(k-1)a_e(k)[a_e(k)P(k-1)a_e(k)+μ(k)]-1
P(k)＝μ(k)-1[I-γ(k)a_e(k)]P(k-1)
where μ (k) is the forgetting factor at time k, γ (k) is the gain matrix at time k, Ftw(k) Represents the system input at time k,
Figure FDA0002946491670000042
representing the quality of the identification at time k, in recursive least squares identification,
Figure FDA0002946491670000043
and γ (k) needs to be preset with an initial value, I represents the identity matrix, and p (k) represents the transition matrix at time k.
7. The real-time fuel saving rate calculation method based on the hidden vehicle model prediction as claimed in claim 1, wherein the specific process of the fuel consumption calculation using the fuel consumption model is as follows:
the oil consumption model adopts a method of looking up a table of engine oil consumption MAP by using the engine rotating speed and the engine torque, but the MAP table is statically calibrated by an engine bench test and needs to be corrected by real oil consumption data of an oil consumption meter to achieve dynamic compensation;
Figure FDA0002946491670000044
wherein FinsIn order to optimize the accumulated oil consumption before the correction of the real vehicle under the control of the cruise control system or the benchmark cruise control system,
Figure FDA0002946491670000045
the instantaneous oil consumption found through the rotating speed and the torque of the engine is represented, the integral can obtain the accumulated oil consumption, and then the corrected oil consumption is obtained through correction;
Ffinal＝a·Fins 2+b·Fins+c
the formula being a correction function, FfinalAnd a, b and c are correction formula parameters.
8. The real-time fuel saving rate calculation method based on the hidden vehicle model prediction as claimed in claim 1, wherein the specific process of the third step is as follows:
the calculated fuel consumption of the reference cruise control system is compared with the real fuel consumption of the optimized cruise control system every 1km, so that the fuel saving rate of the optimized cruise control system compared with the reference cruise control system can be obtained;
Figure FDA0002946491670000046
wherein phi is the fuel saving rate, FoptFor optimizing the fuel consumption of the real vehicle under the control of a cruise control system, FbenchmarkFuel consumption of a virtual hidden vehicle model under the control of a reference cruise control system; in addition, because the oil consumption of the optimized cruise control system is also accumulated and not cleared, the hundred kilometer oil consumption of the optimized cruise control system can be obtained; the oil saving rate and hundred kilometers of oil are finally obtainedAnd the energy consumption is input into a human-computer interaction interface for display through CAN communication.
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