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CN113978263A - An electric vehicle stability control method integrating driving wheel anti-skid and torque optimization - Google Patents

An electric vehicle stability control method integrating driving wheel anti-skid and torque optimization Download PDF

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CN113978263A
CN113978263A CN202111287186.XA CN202111287186A CN113978263A CN 113978263 A CN113978263 A CN 113978263A CN 202111287186 A CN202111287186 A CN 202111287186A CN 113978263 A CN113978263 A CN 113978263A
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vehicle
tire
slip
rate
torque
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崔高健
秦毓飞
商小强
李德涛
李绍松
郑馨義
赵文煜
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Changchun University of Technology
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L15/00—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles
    • B60L15/20—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles for control of the vehicle or its driving motor to achieve a desired performance, e.g. speed, torque, programmed variation of speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L15/00—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles
    • B60L15/32—Control or regulation of multiple-unit electrically-propelled vehicles
    • B60L15/38—Control or regulation of multiple-unit electrically-propelled vehicles with automatic control
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/40—Drive Train control parameters
    • B60L2240/42—Drive Train control parameters related to electric machines
    • B60L2240/423—Torque
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2260/00—Operating Modes
    • B60L2260/40—Control modes
    • B60L2260/44—Control modes by parameter estimation
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2260/00—Operating Modes
    • B60L2260/40—Control modes
    • B60L2260/48—Control modes by fuzzy logic
    • 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
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/72—Electric energy management in electromobility

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  • Power Engineering (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
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Abstract

一种驱动轮防滑与转矩优化融合的电动汽车稳定性控制方法，其特征在于，该方法包括参考模型模块、模糊控制器、驱动防滑控制器、转矩优化控制器、CarSim车辆模型；参考模型模块根据驾驶员输入和车辆纵向车速计算出参考横摆角速度和质心侧偏角；CarSim车辆模型输出汽车的实际状态量；模糊控制器根据参考横摆角速度、参考质心侧偏角和汽车的实际状态量，计算出车辆附加横摆力矩；转矩优化控制器根据整车需求力矩、附加横摆力矩以及轮胎侧向力、垂向力、两后轮转速输出两个后轮轮毂电机的需求力矩，驱动防滑控制器对左右两个驱动轮滑转率进行控制，使其保持在最优滑转率附近，两个控制器互为补充，从而实现车辆在极端路况下的稳定性控制。An electric vehicle stability control method integrating driving wheel anti-skid and torque optimization, characterized in that the method includes a reference model module, a fuzzy controller, a driving anti-skid controller, a torque optimization controller, and a CarSim vehicle model; the reference model The module calculates the reference yaw rate and the center of mass slip angle according to the driver's input and the vehicle's longitudinal speed; the CarSim vehicle model outputs the actual state quantity of the car; the fuzzy controller calculates the reference yaw rate, the reference center of mass sideslip angle and the actual state of the car according to the Calculate the additional yaw moment of the vehicle; the torque optimization controller outputs the required torque of the two rear wheel hub motors according to the required torque of the whole vehicle, the additional yaw moment, the lateral force of the tire, the vertical force, and the rotational speed of the two rear wheels. The driving anti-skid controller controls the slip rate of the left and right driving wheels to keep it near the optimal slip rate, and the two controllers complement each other, so as to realize the stability control of the vehicle under extreme road conditions.

Description

Electric automobile stability control method with driving wheel skid resistance and torque optimization fusion
The technical field is as follows:
the invention relates to the field of stability control of electric automobiles, in particular to an electric automobile stability control method with integration of drive wheel skid resistance and torque optimization.
Background art:
with the popularization of electric vehicles, the driving stability of the electric vehicles is receiving more and more attention. Various stability control methods of integrated control are widely researched and are the development direction of automobile stability control in the future.
The anti-skid control system of the driving wheel can prevent the driving wheel from excessively slipping when the vehicle is driven to run under various road conditions, and the torque optimization control strategy considers the limitation of each constraint condition and ensures that the vehicle has good dynamic property and operation stability in the driving process. Therefore, the integrated control system of the anti-skid control and the torque optimization control of the driving wheels can fully utilize the advantages of the anti-skid control and the torque optimization control, and the controllability and the stability of the vehicle are further improved.
The existing fusion control method mainly comprises a layered control method and an integrated control method. Wave of king earthquake[1]Et al propose an optimal control strategy for four-wheel independent drive electric vehicles. The scheme is composed of an upper controller and a lower controller, a sliding mode control method is adopted in the upper controller, and the longitudinal speed, the transverse speed and the yaw angle speed are used as control variables, so that the required longitudinal force, the transverse force and the yaw moment are determined. The lower controller distributes the driving/braking torque to each hub motor by adopting an optimization algorithm. In Zhouyin[2]The patent refers to the field of 'control or regulating systems and its monitoring or testing arrangements'. Zhangchengning (Zhang Chengning)[3]Et al propose a two-tier allocation strategy. The upper layer is composed of a speed tracking controller and sliding mode controllers, the speed tracking controller obtains the required longitudinal force by tracking the longitudinal speed, and the two sliding mode controllers respectively obtain the expected lateral force and the expected yaw moment by controlling the mass center side deviation angle deviation and the yaw angle speed deviation. And the lower layer controller adopts an optimal objective function obtained by a weighted least square method, comprehensively considers constraint conditions and optimizes the torques of the eight-wheel motor and the brake.
The invention content is as follows:
in order to solve the problem that the slip of a driving wheel is not considered in the existing torque optimization control of the electric automobile, the invention provides a stability control method of the electric automobile with the combination of the slip prevention of the driving wheel and the torque optimization. Firstly, optimizing the torque by adopting a torque optimization allocation control strategy; and secondly, a stability controller integrating direct yaw moment control and drive antiskid control is adopted, so that the adhesion utilization rate of tires is improved, and the driving stability of the vehicle is improved.
The technical scheme adopted by the invention for solving the technical problem is as follows:
a stability control method of an electric vehicle with driving wheel skid resistance and torque optimization fusion is characterized by comprising a reference model module, a fuzzy controller, a driving skid resistance controller, a torque optimization controller and a CarSim vehicle model; the reference model module calculates a reference yaw velocity and a mass center slip angle according to the input of a driver and the longitudinal speed of the vehicle; the CarSim vehicle model outputs the actual state quantity of the automobile; the fuzzy controller calculates the additional yaw moment of the vehicle according to the reference yaw angular velocity, the reference mass center slip angle and the actual state quantity of the vehicle; the torque optimization controller outputs the required torque of the two rear wheel hub motors according to the required torque of the whole vehicle, the additional yaw torque, the lateral force of the tire, the vertical force and the rotating speed of the two rear wheels; the drive anti-skid controller controls the slip rates of the left and right drive wheels to keep the slip rates close to the optimal slip rate, and the two controllers complement each other, so that the stability control of the vehicle under extreme road conditions is realized;
the method comprises the following steps:
step 1, designing a reference model module, and determining a reference yaw velocity and a reference centroid slip angle, wherein the process comprises the following substeps:
step 1.1, a linear two-degree-of-freedom vehicle model is used as a reference model, and the expression of a motion differential equation is as follows:
Figure BDA0003333337720000021
Figure BDA0003333337720000022
the deformation is as follows:
Figure BDA0003333337720000023
Figure BDA0003333337720000024
Figure BDA0003333337720000025
is a stability factor;
wherein m is the total mass of the automobile, u is the longitudinal speed, vyAs lateral velocity, deltafFor the steering angle of the front wheels, IzThe moment of inertia of the vehicle around the z axis, and L is the wheelbase; k is a radical of1And k is2The lateral deflection rigidity of the front and rear axes, a and b are the distances from the center of mass to the front and rear axes, gammadIs the reference yaw rate, beta, of the vehicledIs the vehicle reference centroid slip angle;
step 1.2, designing a reference yaw velocity and a reference centroid slip angle according to the motion differential equations (3) and (4) in the step 1.1, wherein the expressions are as follows:
Figure BDA0003333337720000026
Figure BDA0003333337720000027
the mass center slip angle represents the stability of the vehicle, and the smaller the mass center slip angle is, the smaller the lateral slip of the vehicle is;
considering the constraints of the road conditions, it is possible to obtain:
Figure BDA0003333337720000028
βmax＝arctan(0.02μg) (8)
in summary, the reference yaw rate and the reference centroid slip angle are:
γd＝min(|γd|,γmax)sign(γd) (9)
βd＝min(|βd|,βmax)sign(βd) (10)
where μ is the road surface adhesion coefficient, γmax、βmaxMaximum yaw angular velocity and maximum centroid slip angle, respectively;
step 2, designing a fuzzy controller, wherein the process mainly comprises the following substeps:
step 2.1, determining the structure of the fuzzy controller:
the input of the fuzzy controller is the difference value between the reference yaw velocity and the actual value, the difference value between the centroid sideslip angle and the actual value is referred, and the output is the vehicle additional yaw moment;
step 2.2, fuzzification of input and output variables:
firstly, determining fuzzy subsets of input and output variables, namely NB, NM, NS, ZO, PS, PM and PB, secondly, setting domains of the input and output variables, wherein the domains of the difference value between the reference yaw angular velocity and the actual value are { -0.2, -0.1, 0, 0.1 and 0.2}, the number of membership degrees is 5, the domains of the difference value between the reference centroid yaw angular velocity and the actual value are { -0.2, -0.1, 0, 0.1 and 0.2}, the number of membership degrees is 5, the domains of the output quantity are { -1, -0.5, 0, 0.5 and 1}, and the number of the membership degrees is 7;
step 2.3, adding fuzzy rules:
if the error of the yaw rate is positive and the actual yaw rate and the reference yaw rate are positive, an additional yaw moment opposite to the reference yaw rate in direction is needed to reduce the actual yaw rate; if the actual yaw rate and the reference yaw rate are both negative, an additional yaw moment in the same direction as the reference yaw rate is needed to increase the actual yaw rate, and if the error of the yaw rate is negative, the same principle is adopted, so that the fuzzy rule of the fuzzy control is obtained;
step 2.4, deblurring:
the area gravity center method is used as a defuzzification method, and is used for taking the gravity center of an area enclosed by a membership function curve and a horizontal coordinate as a final output value of fuzzy inference;
step 3, designing a torque optimization controller, wherein the process comprises the following substeps:
step 3.1, setting the tire load rate:
Figure BDA0003333337720000031
to avoid complex calculations, it is convenient to implement real-time online calculations, and the tire load rate is expressed as:
Figure BDA0003333337720000032
where ρ isijIs the tire load factor, FxijIs a tire longitudinal force, FyijFor lateral forces of the tire, FzijIs the vertical force of the tire, mu is the road surface adhesion coefficient;
step 3.2, solving by adopting a quadratic programming algorithm:
the objective function is:
Figure BDA0003333337720000041
e (rho) represents the average value of the tire load rate, the optimization of the average value of the tire load rate can make the overall load rate of the tire lower and improve the stability of the whole vehicle, eta represents the optimized target weighting coefficient, and the smaller eta is, the lower the weight of the average value is;
step 3.3, designing an optimization target and constraint conditions:
the longitudinal forces of the two rear wheels are used as independent variables:
x＝[Fxrl Fxrr]T (14)
Fxrl、Fxrrthe longitudinal forces of the left and right rear wheels are respectively;
converting the objective function into a standard quadratic programming problem:
Figure BDA0003333337720000042
s.tAeqx＝beq (16)
Ax≤b (17)
step 3.3.1, obtaining a quadratic programming H matrix and a c matrix by the objective function:
Figure BDA0003333337720000043
Figure BDA0003333337720000044
Fzrl、Fzrrthe vertical forces of the left rear wheel and the right rear wheel are respectively;
step 3.3.2, maximum longitudinal driving force constraint:
the constraint of the total driving torque during distribution can be considered to obtain a quadratic programming equality constraint condition, under the condition of ensuring the stable running of the vehicle, the constraint of the power demand of the whole vehicle is mainly used for optimizing two rear wheels so as to respectively achieve the maximum longitudinal driving force of the tire, and the constraint condition of the maximum longitudinal driving force of the vehicle is as follows:
Figure BDA0003333337720000045
Tdthe expected torque of the whole vehicle is obtained by adopting PID control by taking the difference value between the actual vehicle speed and the expected vehicle speed as input;
step 3.3.3, yaw moment constraint:
the vehicle stability is the basis of safe and reliable running of the vehicle, the yaw stability is the most core target of the vehicle stability control, the yaw moment can adjust the yaw movement of the vehicle to improve the stability of the vehicle, and because the longitudinal force of the tire is far greater than the lateral force of the tire, only the longitudinal force of the tire is considered when the yaw moment constraint is considered, so the additional yaw moment constraint expression is as follows:
Figure BDA0003333337720000051
step 3.3.4, maximum torque constraint of the motor:
the maximum longitudinal force provided by a single in-wheel motor is:
Figure BDA0003333337720000052
Fxijfor maximum longitudinal force, T, of the in-wheel motormaxThe maximum torque of the driving motor is R, and the effective rolling radius of the wheel is R;
step 3.3.5, constraint of pavement adhesion conditions:
according to the theory of the road surface adhesion ellipse, the tire longitudinal force and the tire lateral force are limited by the road surface adhesion condition, when the lateral tire force and the longitudinal tire force are both in the range of the road surface adhesion ellipse, the vehicle is in a stable state, once any tire force or a plurality of tire forces exceed the maximum road surface adhesion limit which can be provided by the road surface, the vehicle starts to be in an unstable state, the instability phenomenon is easy to occur, and the specific expression of the road surface adhesion condition limitation is as follows:
Figure BDA0003333337720000053
step 4, designing a driving antiskid controller, wherein the driving antiskid controller comprises the following substeps:
4.1, calculating the slip ratio of the two rear wheels:
the slip rates of the two rear wheels are as follows:
Figure BDA0003333337720000054
where u is the longitudinal speed of the vehicle, R is the rolling radius of the wheels, SijSlip ratio, omega, of two rear wheelsijThe angular velocities of the two rear wheels;
4.2, vertical force calculation:
vertical force of the two rear wheels:
Figure BDA0003333337720000055
Fzijis the vertical force of two rear wheels, m is the total vehicle mass, g is the acceleration of gravity, ayIs the transverse acceleration of the vehicle at the centroid, h is the centroid height, drIs the rear wheel track, l is the front-rear axle base, axLongitudinal acceleration at the vehicle center of mass;
4.3, longitudinal force calculation:
driving force, which is a longitudinal force between a tire and a road surface when a vehicle is accelerated:
Jαij＝Tdij-Fxij (26)
j is the moment of inertia of the two rear wheels, αijAngular acceleration of two rear wheels, TdijIs the driving torque of two rear wheels, FxijLongitudinal force of two rear wheels;
4.4, calculating the adhesion coefficient of the two rear wheels:
Figure BDA0003333337720000061
mu is the adhesion coefficient actually utilized by the two rear wheels;
4.5, calculating the peak value of the optimal slip ratio and the ground adhesion coefficient:
the μ -S function curve proposed by Burckhardt et al was chosen and expressed as follows:
μ(S)＝C1(1-e-C2S)-C3S (28)
C1、C2、C3is a fitting coefficient;
the optimal slip ratio and the peak value of the ground adhesion coefficient are respectively as follows:
Figure BDA0003333337720000062
Figure BDA0003333337720000063
wherein SoptFor optimum slip rate, mumaxIs the peak value of the ground adhesion coefficient;
4.6, designing a PID driving anti-skid controller:
the PID drives the anti-skid controller to control the actual slip rate to be close to the optimal slip rate by taking the optimal slip rate as a control target, the PID inputs the deviation between the actual slip rate and the optimal slip rate, and the output is the regulating torque of the motor;
Figure BDA0003333337720000064
Tcomfor adjusting the torque, Δ S (t) is the deviation of the actual slip rate from the optimal slip rate, t is the sampling time, KpIs a proportionality coefficient, KiIs the integral coefficient, KdIs a differential coefficient;
in conclusion, the quadratic programming algorithm controller outputs the required torque of the two rear wheel hub motors, the anti-skid controller is driven to adjust the driving torque, and the adjusted two rear wheel driving torques are input to the CarSim vehicle model, so that the vehicle stability control of the vehicle under extreme road conditions is realized.
The invention has the beneficial effects that:
the invention provides a stability control method of an electric automobile with driving wheel skid resistance and torque optimization integrated, which can effectively improve the stability and the maneuverability of the automobile by designing a torque optimization controller to output the torque required by a motor and designing an integrated controller based on direct yaw moment control and driving skid resistance control.
Drawings
Fig. 1 is a schematic diagram of the control system structure of the present invention.
Fig. 2 is a schematic view of a kinematic model of an automobile.
Detailed Description
The invention is described in detail below with reference to the figures and examples.
A stability control method of an electric vehicle with driving wheel skid resistance and torque optimization fusion is characterized by comprising a reference model module, a fuzzy controller, a driving skid resistance controller, a torque optimization controller and a CarSim vehicle model; the reference model module calculates a reference yaw velocity and a mass center slip angle according to the input of a driver and the longitudinal speed of the vehicle; the CarSim vehicle model outputs the actual state quantity of the automobile; the fuzzy controller calculates the additional yaw moment of the vehicle according to the reference yaw angular velocity, the reference mass center slip angle and the actual state quantity of the vehicle; the torque optimization controller outputs the required torque of the hub motors of the two rear wheels according to the required torque of the whole vehicle, the additional yaw torque, the lateral force of the tire, the vertical force and the rotating speed of the two rear wheels, drives the anti-skid controller to control the slip rates of the left driving wheel and the right driving wheel so as to keep the slip rates close to the optimal slip rate, and the two controllers complement each other, so that the stability control of the vehicle under the extreme road condition is realized;
the method of the present invention is specifically described below with a certain vehicle model of the CarSim vehicle simulation software as a platform, and the main parameters are shown in table 1:
TABLE 1 Main parameters of CarSim vehicles
Figure BDA0003333337720000071
The reference model module is designed for determining a reference yaw rate and a centroid slip angle, and the process comprises the following sub-steps:
in section 1.1, a linear two-degree-of-freedom automobile model is used as a reference model, and as shown in fig. 2, the motion differential equation expression is as follows:
Figure BDA0003333337720000072
Figure BDA0003333337720000073
the deformation is as follows:
Figure BDA0003333337720000081
Figure BDA0003333337720000082
Figure BDA0003333337720000083
is a stability factor.
Wherein: m is the total mass of the automobile; u is the longitudinal vehicle speed; v. ofyIs the lateral velocity; deltafIs the front wheel steering angle; i iszIs the moment of inertia of the vehicle about the z-axis; l is the wheelbase; k is a radical of1And k is2Yaw stiffness for the front and rear axes; a and b are the distances from the centroid to the front and rear axes respectively; gamma raydIs the vehicle reference yaw rate; beta is adIs the reference centroid slip angle of the automobile;
in section 1.2, the reference yaw rate and the reference centroid slip angle are designed according to equations (3) and (4), and the expressions thereof are as follows:
Figure BDA0003333337720000084
Figure BDA0003333337720000085
the mass center slip angle response reflects the stability of the vehicle, and the smaller the value of the mass center slip angle response is, the smaller the lateral slip of the vehicle is;
considering the constraints of the road conditions, it is possible to obtain:
Figure BDA0003333337720000086
βmax＝arctan(0.02μg) (8)
to sum up, the reference yaw rate and the reference centroid slip angle are:
γd＝min(|γd|,γmax)sign(γd) (9)
βd＝min(|βd|,βmax)sign(βd) (10)
where μ is the road surface adhesion coefficient, γmax、βmaxMaximum yaw angular velocity and maximum centroid slip angle, respectively;
the design of the fuzzy controller 2 includes four parts: 2.1 determining a fuzzy controller structure; 2.2 fuzzification of input and output variables; 2.3 add fuzzy rules; 2.4 deblurring;
in section 2.1, a fuzzy controller structure is determined, as follows:
the input of the fuzzy controller is the difference value between the reference yaw velocity and the actual value, the difference value between the centroid sideslip angle and the actual value is referred, and the output is the vehicle additional yaw moment;
in section 2.2, the fuzzification of the input and output quantities is mainly:
firstly, determining fuzzy subsets of input and output variables, namely NB, NM, NS, ZO, PS, PM and PB, secondly, setting the domains of the input and output variables, wherein the domains of the difference between the reference yaw angular velocity and the actual value are { -0.2, -0.1, 0, 0.1 and 0.2}, the number of membership degrees is 5, the domains of the difference between the reference centroid yaw angular velocity and the actual value are { -0.2, -0.1, 0, 0.1 and 0.2}, the number of membership degrees is 5, the domains of the output quantities are { -1, -0.5, 0, 0.5 and 1}, and the number of the membership degrees is 7.
In section 2.3, the fuzzy rules are mainly:
if the error of the yaw rate is positive and the actual yaw rate and the reference yaw rate are positive, an additional yaw moment opposite to the reference yaw rate in direction is needed to reduce the actual yaw rate; if the actual yaw rate and the reference yaw rate are both negative, an additional yaw moment in the same direction as the reference yaw rate is required to increase the actual yaw rate, and if the error of the yaw rate is negative, the same applies. The above is the fuzzy principle of fuzzy control.
In part 2.4, the area barycenter method is used for deblurring;
the design of the torque optimization controller 3 comprises three parts: 3.1 setting the tire load rate; 3.2 solving by adopting a quadratic programming algorithm; 3.3 designing an optimization target and constraint conditions;
in section 3.1, first, a tire load factor is set, the equation expression of which is as follows:
Figure BDA0003333337720000091
where ρ isijIs the tire load factor, FxijIs a tire longitudinal force, FyijFor lateral forces of the tire, FzijIs the tire vertical force, mu is the road surface adhesion coefficient.
To avoid complex calculations, facilitating real-time online calculations, the tire workload rate is expressed as:
Figure BDA0003333337720000092
in section 3.2, a quadratic programming algorithm is adopted for solving, and the expression is as follows:
Figure BDA0003333337720000093
e (rho) represents the average value of the tire load rate, the optimization of the average value of the tire load rate can make the overall load rate of the tire lower and improve the stability of the whole vehicle, eta represents the optimized target weighting coefficient, and the smaller eta is, the lower the weight of the average value is;
in section 3.3, optimization objectives and constraints are designed, including five parts: 3.3.1 obtaining a quadratic programming H matrix and a c matrix by the objective function; 3.32 maximum longitudinal driving force constraint; 3.3.3 yaw moment constraint; 3.3.4 maximum torque constraint of the motor; 3.3.5 constraint of pavement adhesion conditions;
first, the longitudinal forces of the two rear wheels are used as independent variables:
x＝[Fxrl Fxrr]T (14)
Fxrl、Fxrrthe longitudinal forces of the left and right rear wheels are respectively;
the objective function is converted into a standard quadratic programming problem,
Figure BDA0003333337720000101
s.tAeqx＝beq (16)
Ax≤b (17)
in section 3.3.1, the quadratic H and c matrices are derived from the objective function:
Figure BDA0003333337720000102
Figure BDA0003333337720000103
Fzrl、Fzrrthe vertical forces of the left rear wheel and the right rear wheel are respectively;
in the 3.3.2 part, the constraint condition of quadratic programming equality can be obtained by considering the constraint of total driving torque during distribution, under the condition of ensuring the stable running of the vehicle, the constraint of the power requirement of the whole vehicle is mainly used for optimizing two rear wheels to respectively achieve the maximum longitudinal driving force of the tire, and the constraint condition of the maximum longitudinal driving force of the designed vehicle is as follows:
Figure BDA0003333337720000104
Tdthe torque is the required torque of the whole vehicle, the difference value of the actual vehicle speed and the expected vehicle speed is used as input, and PID control is adopted to obtain the torque;
in section 3.3.3, because vehicle stability is the basis for safe and reliable driving of the vehicle, yaw stability is the most core target for vehicle stability control, and yaw moment can adjust yaw movement of the vehicle to improve the stability of the vehicle, since tire longitudinal force is much greater than tire lateral force, only tire longitudinal force is considered when considering yaw moment constraint, and therefore the yaw moment constraint expression is:
Figure BDA0003333337720000105
in section 3.3.4, a motor maximum torque constraint is designed, and the expression is as follows:
Figure BDA0003333337720000106
Tmaxthe maximum torque of the driving motor is R, and the effective rolling radius of the wheel is R;
in section 3.3.5, a road adhesion condition limit is designed based on the road adhesion ellipse theory, and a specific expression of the road adhesion condition limit is as follows:
Figure BDA0003333337720000107
the design of the drive slip controller 4 comprises 6 parts: 4.1 calculating the slip ratio of the driving wheel; 4.2 calculating vertical force; 4.3 calculating the longitudinal force; 4.4 calculating the adhesion coefficients of the two rear wheels; 4.5 calculating the optimal slip ratio and the ground adhesion coefficient; 4.6PID controller design;
in section 4.1, slip ratios for the two rear wheels are calculated, the expression for which is as follows:
Figure BDA0003333337720000111
wherein R is the rolling radius of the wheel, u is the longitudinal speed of the vehicle, SijSlip ratio, omega, of two rear wheelsijIs the angular velocity of two wheels;
in section 4.2, the vertical force of the two rear wheels is calculated, the expression is as follows:
Figure BDA0003333337720000112
wherein, FzijIs the vertical force of two rear wheels, m is the total vehicle mass, g is the acceleration of gravity, ayIs the transverse acceleration of the vehicle at the centroid, h is the centroid height, drIs the rear wheel track, l is the front-rear axle base, axLongitudinal acceleration at the vehicle center of mass;
in section 4.3, the longitudinal force of the two rear wheels is calculated, the expression:
Jαij＝Tdij-Fxij (26)
wherein J is the moment of inertia of the two rear wheels, αijAngular acceleration of two rear wheels, TdijIs the driving torque of two rear wheels, FxijLongitudinal force of two rear wheels;
in section 4.4, the adhesion coefficients of the two rear wheels are calculated, the expression being as follows:
Figure BDA0003333337720000113
wherein, muijThe adhesion coefficient actually utilized for the two rear wheels,Fxijlongitudinal force of two rear wheels, FzijIs the vertical force of the two rear wheels;
in section 4.5, the optimal slip ratio and the peak value of the ground adhesion coefficient are calculated,
the μ -S function curve proposed by Burckhardt et al was chosen and expressed as follows:
μ(S)＝C1(1-e-C2S)-C3S (28)
wherein C is1、C2、C3Is a fitting coefficient;
the optimal slip ratio and the peak value of the ground adhesion coefficient are calculated as follows:
Figure BDA0003333337720000114
Figure BDA0003333337720000115
wherein SoptFor optimum slip rate, mumaxIs the peak value of the ground adhesion coefficient;
in section 4.6, the PID is designed to drive the antiskid controller,
the PID drives the anti-skid controller to control the actual slip rate to be close to the optimal slip rate by taking the optimal slip rate as a control target, the PID inputs the deviation between the actual slip rate and the optimal slip rate, and outputs the regulation torque of the motor, wherein the expression is as follows:
Figure BDA0003333337720000121
Tcomfor adjusting the torque, Δ S (t) is the deviation of the actual slip rate from the optimal slip rate, t is the sampling time, KpIs a proportionality coefficient, KiIs the integral coefficient, KdIs a differential coefficient;
in conclusion, the quadratic programming algorithm controller outputs the required torque of the two rear wheel hub motors, the drive anti-slip controller controls the slip rates of the left and right driving wheels to keep the slip rates close to the optimal slip rate, and the two controllers complement each other, so that the stability control of the vehicle under extreme road conditions is realized.

Claims (1)

1. A stability control method of an electric vehicle with driving wheel skid resistance and torque optimization fusion is characterized by comprising a reference model module, a fuzzy controller, a driving skid resistance controller, a torque optimization controller and a CarSim vehicle model; the reference model module calculates a reference yaw velocity and a mass center slip angle according to the input of a driver and the longitudinal speed of the vehicle; the CarSim vehicle model outputs the actual state quantity of the automobile; the fuzzy controller calculates the additional yaw moment of the vehicle according to the reference yaw angular velocity, the reference mass center slip angle and the actual state quantity of the vehicle; the torque optimization controller outputs the required torque of the hub motors of the two rear wheels according to the required torque of the whole vehicle, the additional yaw torque, the lateral force of the tire, the vertical force and the rotating speed of the two rear wheels, drives the anti-skid controller to control the slip rates of the left driving wheel and the right driving wheel so as to keep the slip rates close to the optimal slip rate, and the two controllers complement each other, so that the stability control of the vehicle under the extreme road condition is realized;
the method comprises the following steps:
step 1, designing a reference model module, and determining a reference yaw velocity and a reference centroid slip angle, wherein the process comprises the following substeps:
step 1.1, a linear two-degree-of-freedom vehicle model is used as a reference model, and the expression of a motion differential equation is as follows:
Figure FDA0003333337710000011
Figure FDA0003333337710000012
the deformation is as follows:
Figure FDA0003333337710000013
Figure FDA0003333337710000014
Figure FDA0003333337710000015
is a stability factor;
wherein m is the total mass of the automobile, u is the longitudinal speed, vyAs lateral velocity, deltafFor the steering angle of the front wheels, IzThe moment of inertia of the vehicle around the z axis, and L is the wheelbase; k is a radical of1And k is2The lateral deflection rigidity of the front and rear axes, a and b are the distances from the center of mass to the front and rear axes, gammadIs the reference yaw rate, beta, of the vehicledIs the vehicle reference centroid slip angle;
step 1.2, designing a reference yaw velocity and a reference centroid slip angle according to the motion differential equations (3) and (4) in the step 1.1, wherein the expressions are as follows:
Figure FDA0003333337710000016
Figure FDA0003333337710000017
the mass center slip angle represents the stability of the vehicle, and the smaller the mass center slip angle is, the smaller the lateral slip of the vehicle is;
considering the constraints of the road conditions, it is possible to obtain:
Figure FDA0003333337710000021
βmax＝arctan(0.02μg) (8)
to sum up, the reference yaw rate and the reference centroid slip angle are:
γd＝min(|γd|,γmax)sign(γd) (9)
βd＝min(|βd|,βmax)sign(βd) (10)
where μ is the road surface adhesion coefficient, γmax、βmaxMaximum yaw angular velocity and maximum centroid slip angle, respectively;
step 2, designing a fuzzy controller, wherein the process mainly comprises the following substeps:
step 2.1, determining the structure of the fuzzy controller:
the input of the fuzzy controller is the difference value between the reference yaw velocity and the actual value, the difference value between the centroid sideslip angle and the actual value is referred, and the output is the vehicle additional yaw moment;
step 2.2, fuzzification of input and output variables:
firstly, determining fuzzy subsets of input and output variables, namely NB, NM, NS, ZO, PS, PM and PB, secondly, setting domains of the input and output variables, wherein the domains of the difference value between the reference yaw angular velocity and the actual value are { -0.2, -0.1, 0, 0.1 and 0.2}, the number of membership degrees is 5, the domains of the difference value between the reference centroid yaw angular velocity and the actual value are { -0.2, -0.1, 0, 0.1 and 0.2}, the number of membership degrees is 5, the domains of the output quantity are { -1, -0.5, 0, 0.5 and 1}, and the number of the membership degrees is 7;
step 2.3, adding fuzzy rules:
if the error of the yaw rate is positive and the actual yaw rate and the reference yaw rate are positive, an additional yaw moment opposite to the reference yaw rate in direction is needed to reduce the actual yaw rate; if the actual yaw rate and the reference yaw rate are both negative, an additional yaw moment in the same direction as the reference yaw rate is needed to increase the actual yaw rate, and if the error of the yaw rate is negative, the same principle is adopted, so that the fuzzy rule of the fuzzy control is obtained;
step 2.4, deblurring:
the area gravity center method is used as a defuzzification method, and is used for taking the gravity center of an area enclosed by a membership function curve and a horizontal coordinate as a final output value of fuzzy inference;
step 3, designing a torque optimization controller, wherein the process comprises the following substeps:
step 3.1, setting the tire load rate:
Figure FDA0003333337710000022
to avoid complex calculations, it is convenient to implement real-time online calculations, and the tire load rate is expressed as:
Figure FDA0003333337710000031
where ρ isijIs the tire load factor, FxijIs a tire longitudinal force, FyijFor lateral forces of the tire, FzijIs the vertical force of the tire, mu is the road surface adhesion coefficient;
step 3.2, solving by adopting a quadratic programming algorithm:
the objective function is:
Figure FDA0003333337710000032
e (rho) represents the average value of the tire load rate, the optimization of the average value of the tire load rate can make the overall load rate of the tire lower and improve the stability of the whole vehicle, eta represents the optimized target weighting coefficient, and the smaller eta is, the lower the weight of the average value is;
step 3.3, designing an optimization target and constraint conditions:
the longitudinal forces of the two rear wheels are used as independent variables:
x＝[Fxrl Fxrr]T (14)
Fxrl、Fxrrrespectively a left rear wheel tire and a right rear wheel tireA longitudinal force;
converting the objective function into a standard quadratic programming problem:
Figure FDA0003333337710000033
s.tAeqx＝beq (16)
Ax≤b (17)
step 3.3.1, obtaining a quadratic programming H matrix and a c matrix by the objective function:
Figure FDA0003333337710000034
Figure FDA0003333337710000035
Fzrl、Fzrrthe vertical forces of the left rear wheel and the right rear wheel are respectively;
step 3.3.2, maximum longitudinal driving force constraint:
the constraint of the total driving torque during distribution can be considered to obtain a quadratic programming equality constraint condition, under the condition of ensuring the stable running of the vehicle, the constraint of the power demand of the whole vehicle is mainly used for optimizing two rear wheels so as to respectively achieve the maximum longitudinal driving force of the tire, and the constraint condition of the maximum longitudinal driving force of the vehicle is as follows:
Figure FDA0003333337710000036
Tdthe expected torque of the whole vehicle is obtained by adopting PID control by taking the difference value between the actual vehicle speed and the expected vehicle speed as input;
step 3.3.3, yaw moment constraint:
the vehicle stability is the basis of safe and reliable running of the vehicle, the yaw stability is the most core target of the vehicle stability control, the yaw moment can adjust the yaw movement of the vehicle to improve the stability of the vehicle, and because the longitudinal force of the tire is far greater than the lateral force of the tire, only the longitudinal force of the tire is considered when the yaw moment constraint is considered, so the additional yaw moment constraint expression is as follows:
Figure FDA0003333337710000041
step 3.3.4, maximum torque constraint of the motor:
the maximum longitudinal force provided by a single in-wheel motor is:
Figure FDA0003333337710000042
Fxijfor maximum longitudinal force, T, of the in-wheel motormaxThe maximum torque of the driving motor is R, and the effective rolling radius of the wheel is R;
step 3.3.5, constraint of pavement adhesion conditions:
according to the theory of the road surface adhesion ellipse, the tire longitudinal force and the tire lateral force are limited by the road surface adhesion condition, when the lateral tire force and the longitudinal tire force are both in the range of the road surface adhesion ellipse, the vehicle is in a stable state, once any tire force or a plurality of tire forces exceed the maximum road surface adhesion limit which can be provided by the road surface, the vehicle starts to be in an unstable state, the instability phenomenon is easy to occur, and the specific expression of the road surface adhesion condition limitation is as follows:
Figure FDA0003333337710000043
step 4, designing a driving antiskid controller, wherein the driving antiskid controller comprises the following substeps:
4.1, calculating the slip ratio of the two rear wheels:
the slip rates of the two rear wheels are as follows:
Figure FDA0003333337710000044
where u is the longitudinal speed of the vehicle, R is the rolling radius of the wheels, SijSlip ratio, omega, of two rear wheelsijThe angular velocities of the two rear wheels;
4.2, vertical force calculation:
vertical force of the two rear wheels:
Figure FDA0003333337710000045
Fzijis the vertical force of two rear wheels, m is the total vehicle mass, g is the acceleration of gravity, ayIs the transverse acceleration of the vehicle at the centroid, h is the centroid height, drIs the rear wheel track, l is the front-rear axle base, axLongitudinal acceleration at the vehicle center of mass;
4.3, longitudinal force calculation:
driving force, which is a longitudinal force between a tire and a road surface when a vehicle is accelerated:
Jαij＝Tdij-Fxij (26)
j is the moment of inertia of the two rear wheels, αijAngular acceleration of two rear wheels, TdijIs the driving torque of two rear wheels, FxijLongitudinal force of two rear wheels;
4.4, calculating the adhesion coefficient of the two rear wheels:
Figure FDA0003333337710000051
mu is the adhesion coefficient actually utilized by the two rear wheels;
4.5, calculating the peak value of the optimal slip ratio and the ground adhesion coefficient:
the μ -S function curve proposed by Burckhardt et al was chosen and expressed as follows:
μ(S)＝C1(1-e-C2S)-C3S (28)
C1、C2、C3is a fitting coefficient;
the optimal slip ratio and the peak value of the ground adhesion coefficient are respectively as follows:
Figure FDA0003333337710000052
Figure FDA0003333337710000053
wherein SoptFor optimum slip rate, mumaxIs the peak value of the ground adhesion coefficient;
4.6, designing a PID driving anti-skid controller:
the PID drives the anti-skid controller to control the actual slip rate to be close to the optimal slip rate by taking the optimal slip rate as a control target, the PID inputs the deviation between the actual slip rate and the optimal slip rate, and the output is the regulating torque of the motor;
Figure FDA0003333337710000054
Tcomfor adjusting the torque, Δ S (t) is the deviation of the actual slip rate from the optimal slip rate, t is the sampling time, KpIs a proportionality coefficient, KiIs the integral coefficient, KdIs a differential coefficient;
in conclusion, the quadratic programming algorithm controller outputs the required torque of the two rear wheel hub motors, the drive anti-slip controller controls the slip rates of the left and right driving wheels to keep the slip rates close to the optimal slip rate, and the two controllers complement each other, so that the stability control of the vehicle under extreme road conditions is realized.
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