CN113335376A - Differential anti-drag control method between wheels during steering of distributed independent drive electric automobile - Google Patents

Differential anti-drag control method between wheels during steering of distributed independent drive electric automobile Download PDF

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CN113335376A
CN113335376A CN202110718145.5A CN202110718145A CN113335376A CN 113335376 A CN113335376 A CN 113335376A CN 202110718145 A CN202110718145 A CN 202110718145A CN 113335376 A CN113335376 A CN 113335376A
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change rate
driving
tire pressure
wheel
pressure change
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CN113335376B (en
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张文俊
王巍
刘竹星
王耀
陈庆樟
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Changshu Institute of Technology
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
    • B62D—MOTOR VEHICLES; TRAILERS
    • B62D5/00—Power-assisted or power-driven steering
    • B62D5/04—Power-assisted or power-driven steering electrical, e.g. using an electric servo-motor connected to, or forming part of, the steering gear
    • B62D5/0418—Electric motor acting on road wheel carriers
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
    • B62D—MOTOR VEHICLES; TRAILERS
    • B62D5/00—Power-assisted or power-driven steering
    • B62D5/04—Power-assisted or power-driven steering electrical, e.g. using an electric servo-motor connected to, or forming part of, the steering gear
    • B62D5/0457—Power-assisted or power-driven steering electrical, e.g. using an electric servo-motor connected to, or forming part of, the steering gear characterised by control features of the drive means as such
    • B62D5/046—Controlling the motor
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
    • B62D—MOTOR VEHICLES; TRAILERS
    • B62D6/00—Arrangements for automatically controlling steering depending on driving conditions sensed and responded to, e.g. control circuits
    • B62D6/001—Arrangements for automatically controlling steering depending on driving conditions sensed and responded to, e.g. control circuits the torque NOT being among the input parameters
    • 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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  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Steering Control In Accordance With Driving Conditions (AREA)
  • Feedback Control In General (AREA)

Abstract

The invention discloses a differential anti-drag control method between wheels during steering of a distributed independent drive electric automobile, which is characterized by comprising the following steps of: s1, acquiring the wheel speed of the driving wheel when the automobile turns; s2, taking the wheel speed of one driving wheel as a reference wheel speed, and reversely pushing the decision-making expected wheel speed of the other driving wheels according to the Ackermann steering model; s3, controlling the output torque of each motor for driving wheels by a PI controller, wherein the input of the PI controller is the difference value between the real-time wheel speed of the driving wheels and the decision-making expected wheel speed, the output of the PI controller is the output torque coefficient of the motor, the proportional parameter of the PI controller is preset, and the integral parameter of the PI controller is controlled by a fuzzy controller; the output of the fuzzy controller is an integral parameter, and the input of the fuzzy controller is the change rate of the tire pressure of the driving wheel and the change rate of the yaw velocity of the vehicle. The invention combines the PI controller with the fuzzy control, controls the wheel speed of each wheel by the change rate of the tire pressure of the driving wheel and the change rate of the yaw velocity of the vehicle, and ensures the stability of the vehicle.

Description

Differential anti-drag control method between wheels during steering of distributed independent drive electric automobile
Technical Field
The invention relates to a wheel speed control method of an electric automobile, in particular to a differential anti-drag control method between wheels during steering of a distributed independent driving electric automobile.
Background
In an ideal state, the relationship between the turning radius of each wheel and the rotating speed of the driving wheels can distribute driving torque through a mechanical differential mechanism and realize the differential function for the traditional vehicle. For the distributed independent driving electric wheel automobile, the constraint of a mechanical differential is lacked, so that the control of the rotating speed and the torque of the driving wheels on two sides is more complicated and the flexibility of rotating speed and torque distribution is realized. The four wheels are independently driven freely, and even under the condition of finished wheel speed control planning, the phenomenon of dragging between the wheels is still inevitable. Once the phenomenon of dragging between wheels occurs, the slip rate and the cornering stiffness of a single tire can be separated from the original estimation range, so that the deviation occurs in the original wheel speed control scheme or the original torque output control method, and the running stability of the whole vehicle is further caused.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a differential anti-drag control method between wheels when a distributed type independent driving electric automobile is steered, so that the dragging phenomenon between the wheels is avoided, and the running stability of the automobile is ensured.
The technical scheme of the invention is as follows: a differential anti-drag control method between wheels during steering of a distributed independent drive electric automobile comprises the following steps:
s1, acquiring the wheel speed of the driving wheel when the automobile turns;
s2, taking the wheel speed of one driving wheel as a reference wheel speed, and reversely pushing the decision-making expected wheel speed of the driving wheel according to the Ackermann steering model;
s3, controlling the output torque of each motor for driving wheels by a PI controller, wherein the input of the PI controller is the difference value between the real-time wheel speed of the driving wheels and the decision-making expected wheel speed, the output of the PI controller is the output torque coefficient of the motor, the proportional parameter of the PI controller is preset, and the integral parameter of the PI controller is controlled by a fuzzy controller; the output of the fuzzy controller is an integral parameter, and the input of the fuzzy controller is the change rate of the tire pressure of the driving wheel and the change rate of the yaw velocity of the vehicle.
Further, in step S2, when the wheel speed of one driving wheel is taken as the reference wheel speed, the wheel speed change rate of each driving wheel is calculated, and the real-time wheel speed of the driving wheel with the smallest wheel speed change rate among all the driving wheels is taken as the reference wheel speed.
Further, in the step S2, a plurality of time periods are set, each time period uses the wheel speed of one driving wheel as a reference wheel speed, the expected wheel speed of the driving wheel is reversely pushed according to the Ackermann steering model, and the average value of the expected wheel speeds of the time periods is used as the decision-making expected wheel speed of the driving wheel.
Further, the real-time wheel speed of the driving wheel with the minimum wheel speed change rate in all the driving wheels is taken as the reference wheel speed in each time period.
Further, the driving wheel tire pressure change rate, the vehicle yaw rate change rate, and the integration parameter are each set to five fuzzy sets.
Further, the fuzzy sets of the driving wheel air pressure change rate are set as P1, P2, P3, P4 and P5, the fuzzy sets of the vehicle yaw velocity change rate are set as A1, A2, A3, A4 and A5, the fuzzy sets of the integral parameters are set as I1, I2, I3, I4 and I5, and the fuzzy inference rule of the integral parameters is as follows: when the driving tire pressure change rate is P1 and the vehicle yaw rate change rate is a1, a2 or A3, the integration parameter is I3; when the driving tire pressure change rate is P1 and the vehicle yaw rate change rate is A4, the integral parameter is I4; when the driving tire pressure change rate is P1 and the vehicle yaw rate change rate is A5, the integral parameter is I5; when the driving tire pressure change rate is P2 and the vehicle yaw rate change rate is A1, the integral parameter is I2; when the driving tire pressure change rate is P2 and the vehicle yaw rate change rate is a2 or A3, the integration parameter is I3; when the driving tire pressure change rate is P2 and the vehicle yaw rate change rate is a4 or a5, the integration parameter is I5; when the driving tire pressure change rate is P3 and the vehicle yaw rate change rate is a1 or a2, the integration parameter is I2; when the driving tire pressure change rate is P3 and the vehicle yaw rate change rate is A3, the integral parameter is I3; when the driving tire pressure change rate is P3 and the vehicle yaw rate change rate is A4, the integral parameter is I4; when the driving tire pressure change rate is P3 and the vehicle yaw rate change rate is A5, the integral parameter is I5; when the driving tire pressure change rate is P4 or P5 and the vehicle yaw rate change rate is a1, the integration parameter is I1; when the driving tire pressure change rate is P4 or P5 and the vehicle yaw rate change rate is a2, the integration parameter is I2; when the driving tire pressure change rate is P4 or P5 and the vehicle yaw rate change rate is A3, the integration parameter is I3; when the driving tire pressure change rate is P4 and the vehicle yaw rate change rate is a4 or a5, the integration parameter is I4; when the driving tire pressure change rate is P5 and the vehicle yaw rate change rate is A4, the integral parameter is I3; when the driving tire pressure change rate is P5 and the vehicle yaw rate change rate is a5, the integration parameter is I4.
Furthermore, the discourse domain of the driving wheel air pressure change rate is set as [ -1, 4], the membership function of the fuzzy set P1 is a triangular function, the discourse domain of the corresponding driving wheel air pressure change rate is [ -1, 0], the membership degree of P1 is 1 when the driving wheel air pressure change rate is-1, and the membership degree of P1 is 0 when the driving wheel air pressure change rate is 0; the membership function of the fuzzy set P2 is a triangular function, and the discourse domain of the corresponding driving wheel tire pressure change rate is [ -0.5, 0.5 ]; the membership function of the fuzzy set P3 is a triangular function, and the domain of the corresponding driving wheel tire pressure change rate is [0, 2 ]; the membership function of the fuzzy set P4 is a triangular function, and the discourse domain of the corresponding driving wheel tire pressure change rate is [1, 4 ]; the membership function of the fuzzy set P5 is a trapezoidal function, the domain of the corresponding tire pressure change rate of the driving wheel is [2.5, 5], the membership of P5 is 0 when the tire pressure change rate of the driving wheel is 2.5, and the membership of P5 is 1 when the tire pressure change rate of the driving wheel is [3, 4 ].
Further, the domain of the vehicle yaw angular velocity change rate is set to be [ -8, 8], the membership function of the fuzzy set A1 is a trapezoidal function, the corresponding domain of the vehicle yaw angular velocity change rate is [ -8, -3], the A1 membership is 1 when the vehicle yaw angular velocity change rate is [ -8, -7], and the A1 membership is 0 when the vehicle yaw angular velocity change rate is-3; the membership function of the fuzzy set A2 is a triangular function, and the corresponding discourse domain of the vehicle yaw velocity change rate is [ -5, -1 ]; the membership function of the fuzzy set A3 is a triangular function, and the corresponding discourse domain of the vehicle yaw velocity change rate is [ -3, 3 ]; the membership function of the fuzzy set A4 is a triangular function, and the corresponding discourse domain of the vehicle yaw velocity change rate is [1, 5 ]; the membership function of the fuzzy set A5 is a trapezoidal function, the domain of the corresponding vehicle yaw velocity change rate is [3, 8], the A5 membership is 0 when the driving wheel tire pressure change rate is 3, and the A5 membership is 1 when the vehicle yaw velocity change rate is [7, 8 ].
Furthermore, the domain of discourse of the integral parameters is set to [0.8, 1.2], the membership function of the fuzzy sets I1, I2, I3, I4 and I5 is a triangular function, the domain of the integral parameters corresponding to the fuzzy set I1 is [0.8, 0.85], the membership of I1 is 1 when the integral parameters are 0.8, and the membership of I1 is 0 when the integral parameters are 0.85; the domain of integration parameters corresponding to the fuzzy set I2 is [0.8, 0.9 ]; the domain of discourse of the integral parameters corresponding to the fuzzy set I3 is [0.85, 1.15], the domain of the integral parameters corresponding to the fuzzy set I4 is [1.1, 1.2], the domain of the integral parameters corresponding to the fuzzy set I5 is [1.15, 1.2], the membership degree of I5 is 0 when the integral parameters are 1.15, and the membership degree of I5 is 1 when the integral parameters are 1.2.
Further, when the fuzzy controller outputs the integral parameter, a maximum membership method is adopted according to the fuzzy set of the integral parameter to obtain an accurate numerical value.
Compared with the prior art, the invention has the advantages that:
the output of the PI controller is proportional to the integral of the input error signal. The method is mainly used for eliminating static difference and improving the zero-difference degree of a system (the system in the invention refers to a controlled vehicle hub motor). The strength of the integration depends on the integration time constant T, and the larger the T, the weaker the integration and the stronger the opposite.
In terms of the present invention, in the actual application, considering that the actual vehicle structural parameters, i.e. the parameters of the controlled system (the system in the present invention refers to the controlled vehicle hub motor), do not change significantly with time, the parameter change of the PI controller is mainly adjusted according to the time-varying signals (i.e. the signals changing with time t, the steering wheel angle and pedal force input by the driver in the present invention, and the system state variables, the yaw rate change rate in the present invention, and the tire pressure change rate) input by the system, and such control situation is easy to occur the steady-state error changing with time, which causes the system output to vibrate near the actual expectation, thereby affecting the control expectation. The steady-state error of the system is adjusted in real time through parameter self-tuning (fuzzy algorithm), namely the parameter I is adjusted in time, so that the system error is just relieved, and the method is particularly important in the aspect of realizing system stability.
Drawings
FIG. 1 is a schematic flow chart of a differential anti-drag control method between wheels during steering of a distributed independent drive electric vehicle.
Fig. 2 is a schematic diagram of the Ackermann steering model.
FIG. 3 is a schematic diagram of a membership function of a tire pressure change rate of a driving wheel.
Fig. 4 is a diagram showing a membership function of a change rate of the yaw rate of the vehicle.
FIG. 5 is a diagram illustrating a membership function of the integral parameter I.
Detailed Description
The present invention is further illustrated by the following examples, which are not to be construed as limiting the invention thereto.
Referring to fig. 1, the inter-wheel differential anti-drag control method for a distributed independently driven electric vehicle according to the present embodiment includes the following steps:
s1, acquiring the wheel speed of the driving wheel when the automobile turns; the distributed independent drive electric automobile is assumed to be four-wheel drive, and the four-wheel hub motor encoder obtains the real-time four-wheel speed. Information on position, angle, and lap are provided in angular increments, each angular increment being assigned a unique code. Taking a fixed step length t, and solving the last time t1Angle theta of1And the next time t2Angle theta of2The deviation increment between step lengths is delta theta, and the real-time angular speed is omega;
wherein the content of the first and second substances,
Δθ＝θ2-θ1
Figure BDA0003135680720000041
s2, reversely pushing the expected wheel speeds of the other driving wheels according to the Ackermann steering model by taking the wheel speed of one driving wheel as a reference wheel speed;
the real-time change rate of the four-wheel speed is calculated by combining the real-time angular speed omega with the previous moment wheel speed, the step length is set to be a fixed value delta t, and the previous moment wheel speed is omega1The wheel speed at the next moment is omega2Drawing up a real-time wheel speed change rate alpha;
Figure BDA0003135680720000042
the real-time wheel speed change rate of four wheels is obtained according to the method, and is respectively alphai，i＝1，2，3，4。
In the steering process of the vehicle, according to the Ackermann steering model shown in FIG. 2, the steering angle of the inner and outer front wheels is deltainAnd deltaoutThe steering angle of the vehicle is delta, L and W are respectively the wheel base and the wheel base of the vehicle, R0Is the vehicle steering radius, RinAnd RoutRespectively the turning radii of the inner and outer driving wheels, and v the vehicle running speed, i.e. the speed around the turning center O at the midpoint of the rear wheel axis.
According to the Ackermann vehicle steering model, the motion state of the electric vehicle can be analyzed. In the ackermann steering geometry, the center of a circle of a path through which each wheel passes is approximately intersected with the instantaneous steering center of the extension line of the rear axle, so that steering is smoother, and the following relation can be obtained:
R0＝L/tanδ (1)
r0＝L/sinδ (2)
Rin＝R0-W/2 (3)
Rout＝R0+W/2 (4)
Figure BDA0003135680720000051
Figure BDA0003135680720000052
at this time, the rotation speed relationship of the driving wheels at the two sides is as follows:
ω＝v/R0 (7)
vrin＝ωL/tanδin (8)
vrout＝ωL/tanδout (9)
vfin＝ωL/sinδin (10)
vfout＝ωL/sinδout (11)
considering that the wheel speed has the smallest rate of change, it means that the wheel has the smallest relative slip with the ground, and is closest to the stable wheel speed.
Wheel speed ω at which the rate of change of wheel speed is minimumξ1The expected wheel speed of four wheels for ensuring the stability of the vehicle is reversely pushed according to the Ackerman steering model as the reference wheel speed
Figure BDA0003135680720000053
The desired wheel speed of the drive wheel in which the rate of change of the wheel speed is the smallest is its own wheel speed.
For example, if the wheel speed change rate of the right rear wheel is the minimum under the current vehicle condition, the wheel speed of the right rear wheel is used
Figure BDA0003135680720000054
The expected vehicle speeds of the remaining three wheels are derived based on the reference wheel speed, and the specific formula is as follows
Figure BDA0003135680720000055
Figure BDA0003135680720000056
Figure BDA0003135680720000057
Drawing up a fixed step length delta t, and repeating the calculation to obtain the wheel speed omega with the minimum wheel speed change rate at the next momentξFurther calculating the expected wheel speed of four wheels at the next moment; repeating the steps, calculating a plurality of (3, 4 times) different expected wheel speeds in a short time period, and taking the average value as the expected wheel speed for decision
Figure BDA0003135680720000058
And S3, controlling the output torque of each motor for driving wheels by a PI controller, wherein the input of the PI controller is the difference value between the real-time wheel speed of the driving wheels and the decision-making expected wheel speed, the output of the PI controller is the output torque coefficient of the motor, and the integral parameter of the PI controller is controlled by a fuzzy controller.
Compared with the traditional centralized driving automobile, the distributed independent driving electric automobile omits a clutch, a speed reducer, a gearbox, a differential mechanism and other traditional transmission devices. Each driving vehicle is directly driven to rotate by a corresponding motor, the output torque of the independent wheel is adjusted by utilizing a PI controller of the independent wheel hub motor mcu according to the real-time estimated wheel speed as a target value, and the estimated ideal vehicle speed is realized. The difference between the real-time wheel speed and the expected wheel speed is reduced to ensure that the dragging coefficient gradually tends to infinitesimal small, and meanwhile, the stability of the whole vehicle needs to be ensured. The invention is realized by monitoring the change rate of the vehicle yaw velocity and the change rate of the tire pressure of the tire.
The proportional parameter of the PI controller is preset, and the integral parameter of the PI controller is controlled by a fuzzy controller; the output of the fuzzy controller is an integral parameter, and the input of the fuzzy controller is the change rate of the tire pressure of the driving wheel and the change rate of the yaw velocity of the vehicle.
Specifically, a yaw velocity measurer of an ESP system arranged on a vehicle body is used for obtaining the real-time yaw velocity of the whole vehicle, and a fixed step length t is taken from the last moment t1Yaw rate α of1And the next time t2Yaw rate α of2Calculating the rate of change of yaw rate in real time by the deviation increment delta alpha between step lengthsT。
Wherein the content of the first and second substances,
Figure BDA0003135680720000061
a tire pressure detection device is arranged in the inner hub, so that the cornering stiffness of the tire is deduced, and the cornering stiffness is transmitted into a can system through the mcu in real time. Thus, the real-time tire pressure change rate delta P is calculated.
Calculating the I parameter value by adopting a fuzzy algorithm, wherein the input of the fuzzy algorithm is the tire pressure change rate delta P and the real-time yaw velocity change rate alphaTThe rate of change. Considering that the tire pressure reflects the running state of each wheel of the vehicle in real time and is related to the real-time vehicle posture, the tire pressure change rate delta P is used as the input of the controller; the yaw rate can be directly measured in real time by the yaw rate detector, and the invention changes the yaw rate in real time by the change rate alpha of the yaw rate in consideration of the fact that the yaw rate is an important parameter of the driving state of the vehicleTThe rate of change is also used as an input to this controller. The coverage range of the tire pressure change rate Δ P is set to [ -1, 4 [ -1]It is divided into 5 fuzzy sets: negative (P1), medium (P2), positive small (P3), positive medium (P4) and positive large (P5). Change rate alpha of real-time yaw angular velocityTThe universe of discourse of the change rate is set as [ -8, 8 [ ]]It is divided into 5 fuzzy sets: negative large (a1), negative small (a2), zero (A3), positive small (a4), and positive large (a 5). I parameter valueIs set to [0.8, 1.2]]It is divided into 5 fuzzy sets: less than (I1), slightly less than (I2), approximately (I3), slightly more than (I4), and more than (I5). Through simulation analysis, the value range input by the fuzzy algorithm is equal to the range of the corresponding discourse domain, so that the change rate delta P of the tire pressure and the change rate alpha of the real-time yaw velocity of the tire are obtained according to the design membership function without scale transformationTIs fuzzified by the precise amount of (c).
As shown in fig. 3, the membership degree function of the fuzzy set P1 of the tire pressure change rate Δ P is a triangular function, the domain of the corresponding driving wheel tire pressure change rate is [ -1, 0], the membership degree P1 is 1 when the driving wheel tire pressure change rate is-1, and the membership degree P1 is 0 when the driving wheel tire pressure change rate is 0; membership functions of fuzzy sets P2, P3 and P4 are triangular functions, the discourse domain of the driving wheel air pressure change rate corresponding to the fuzzy set P2 is [ -0.5, 0.5], the discourse domain of the driving wheel air pressure change rate corresponding to the fuzzy set P3 is [0, 2], and the discourse domain of the driving wheel air pressure change rate corresponding to the fuzzy set P4 is [1, 4 ]; the membership function of the fuzzy set P5 is a trapezoidal function, the domain of the corresponding tire pressure change rate of the driving wheel is [2.5, 5], the membership degree of P5 is 0 when the tire pressure change rate of the driving wheel is 2.5, and the membership degree of P5 is 1 when the tire pressure change rate of the driving wheel is [3, 4 ].
As shown in fig. 4, the rate of change α of the yaw rate in real timeTThe membership function of the fuzzy set A1 is a trapezoidal function, and the corresponding discourse domain of the vehicle yaw rate change rate is [ -8, -3]The vehicle yaw rate is [ -8, -7 [ -8 [ -7 [ ]]When the membership degree of A1 is 1, when the change rate of the vehicle yaw angular velocity is-3, the membership degree of A1 is 0; membership function of the fuzzy sets A2, A3 and A4 is a triangular function, and discourse domain of the vehicle yaw velocity change rate corresponding to the fuzzy set A2 is [ -5, -1]The universe of discourse of the vehicle yaw rate of change corresponding to the fuzzy set A3 is [ -3, 3]The term of the vehicle yaw rate change rate corresponding to the fuzzy set A4 is [1, 5]](ii) a The membership function of the fuzzy set A5 is a trapezoidal function, and the corresponding discourse domain of the yaw rate change rate of the vehicle is [3, 8%]When the tire pressure change rate of the driving wheel is 3, the membership degree of A5 is 0, and the change rate of the yaw rate of the vehicle is [7, 8]]The membership degree of A5 is 1.
The fuzzy inference rule is shown in table 1 below.
Figure BDA0003135680720000071
The basic idea of the fuzzy inference rule is as follows: the larger the change rate of the yaw velocity is, the more unstable the vehicle tends to be, and the larger the value of the parameter I is at the moment; from the viewpoint of vehicle acceleration safety, the larger the tire pressure variation value is, the more the magnitude of the longitudinal acceleration needs to be suppressed, the smaller the I parameter value is. As shown in fig. 5, membership functions of the fuzzy sets I1, I2, I3, I4 and I5 of the integral parameter I are triangular functions, domains of integral parameters corresponding to the fuzzy set I1 are [0.8, 0.85], I1 membership is 1 when the integral parameter is 0.8, and I1 membership is 0 when the integral parameter is 0.85; the domain of the integral parameter corresponding to the fuzzy set I2 is [0.8, 0.9 ]; the domain of integration parameters corresponding to the fuzzy set I3 is [0.85, 1.15], the domain of integration parameters corresponding to the fuzzy set I4 is [1.1, 1.2], the domain of integration parameters corresponding to the fuzzy set I5 is [1.15, 1.2], the membership degree of I5 is 0 when the integration parameters are 1.15, and the membership degree of I5 is 1 when the integration parameters are 1.2. And determining a fuzzy set of the I parameter through a fuzzy rule, obtaining an I parameter precision result by adopting a maximum membership method, completing parameter determination, and finally controlling the output of the hub motor by the PI controller.

Claims (10)

1. A differential anti-drag control method between wheels during steering of a distributed independent drive electric automobile is characterized by comprising the following steps:
s1, acquiring the wheel speed of the driving wheel when the automobile turns;
s2, taking the wheel speed of one driving wheel as a reference wheel speed, and reversely pushing the decision-making expected wheel speed of the driving wheel according to the Ackermann steering model;
s3, controlling the output torque of each motor for driving wheels by a PI controller, wherein the input of the PI controller is the difference value between the real-time wheel speed of the driving wheels and the decision-making expected wheel speed, the output of the PI controller is the output torque coefficient of the motor, the proportional parameter of the PI controller is preset, and the integral parameter of the PI controller is controlled by a fuzzy controller; the output of the fuzzy controller is an integral parameter, and the input of the fuzzy controller is the change rate of the tire pressure of the driving wheel and the change rate of the yaw velocity of the vehicle.
2. The method as claimed in claim 1, wherein in step S2, when the wheel speed of one driving wheel is taken as a reference wheel speed, the wheel speed change rate of each driving wheel is calculated, and the real-time wheel speed of the driving wheel with the smallest wheel speed change rate among all the driving wheels is taken as the reference wheel speed.
3. The inter-wheel differential anti-drag control method for the steering of the distributed type independent drive electric vehicle as claimed in claim 1, wherein a plurality of time periods are set in the step S2, each time period is based on the wheel speed of one driving wheel, the expected wheel speed of the driving wheel is reversely deduced according to the Ackermann steering model, and the average value of the expected wheel speeds of the time periods is used as the expected wheel speed of the driving wheel.
4. The method for controlling differential speed anti-drag of wheels during steering of a distributed type independent drive electric vehicle according to claim 3, wherein the real-time wheel speed of the driving wheel with the smallest wheel speed change rate among all the driving wheels is taken as a reference wheel speed in each time period.
5. The inter-wheel differential anti-drag control method when the distributed type independently driven electric vehicle steers according to claim 1, wherein the driving wheel tire pressure change rate, the vehicle yaw rate change rate and the integration parameter are all set to five fuzzy sets.
6. The inter-wheel differential anti-drag control method in the steering of the distributed independently driven electric vehicle as claimed in claim 5, wherein the fuzzy sets of the driving wheel air pressure change rates are set as P1, P2, P3, P4 and P5, the fuzzy sets of the vehicle yaw angular velocity change rates are set as A1, A2, A3, A4 and A5, the fuzzy sets of the integral parameters are set as I1, I2, I3, I4 and I5, and the fuzzy inference rules of the integral parameters are: when the driving tire pressure change rate is P1 and the vehicle yaw rate change rate is a1, a2 or A3, the integration parameter is I3; when the driving tire pressure change rate is P1 and the vehicle yaw rate change rate is A4, the integral parameter is I4; when the driving tire pressure change rate is P1 and the vehicle yaw rate change rate is A5, the integral parameter is I5; when the driving tire pressure change rate is P2 and the vehicle yaw rate change rate is A1, the integral parameter is I2; when the driving tire pressure change rate is P2 and the vehicle yaw rate change rate is a2 or A3, the integration parameter is I3; when the driving tire pressure change rate is P2 and the vehicle yaw rate change rate is a4 or a5, the integration parameter is I5; when the driving tire pressure change rate is P3 and the vehicle yaw rate change rate is a1 or a2, the integration parameter is I2; when the driving tire pressure change rate is P3 and the vehicle yaw rate change rate is A3, the integral parameter is I3; when the driving tire pressure change rate is P3 and the vehicle yaw rate change rate is A4, the integral parameter is I4; when the driving tire pressure change rate is P3 and the vehicle yaw rate change rate is A5, the integral parameter is I5; when the driving tire pressure change rate is P4 or P5 and the vehicle yaw rate change rate is a1, the integration parameter is I1; when the driving tire pressure change rate is P4 or P5 and the vehicle yaw rate change rate is a2, the integration parameter is I2; when the driving tire pressure change rate is P4 or P5 and the vehicle yaw rate change rate is A3, the integration parameter is I3; when the driving tire pressure change rate is P4 and the vehicle yaw rate change rate is a4 or a5, the integration parameter is I4; when the driving tire pressure change rate is P5 and the vehicle yaw rate change rate is A4, the integral parameter is I3; when the driving tire pressure change rate is P5 and the vehicle yaw rate change rate is a5, the integration parameter is I4.
7. The differential anti-drag control method between wheels during the steering of the distributed independently driven electric vehicle as claimed in claim 6, wherein the discourse domain of the tire pressure change rate of the driving wheel is set as [ -1, 4], the membership function of the fuzzy set P1 is a triangular function, the discourse domain of the corresponding tire pressure change rate of the driving wheel is [ -1, 0], the P1 membership degree is 1 when the tire pressure change rate of the driving wheel is-1, and the P1 membership degree is 0 when the tire pressure change rate of the driving wheel is 0; the membership function of the fuzzy set P2 is a triangular function, and the discourse domain of the corresponding driving wheel tire pressure change rate is [ -0.5, 0.5 ]; the membership function of the fuzzy set P3 is a triangular function, and the domain of the corresponding driving wheel tire pressure change rate is [0, 2 ]; the membership function of the fuzzy set P4 is a triangular function, and the discourse domain of the corresponding driving wheel tire pressure change rate is [1, 4 ]; the membership function of the fuzzy set P5 is a trapezoidal function, the domain of the corresponding tire pressure change rate of the driving wheel is [2.5, 5], the membership of P5 is 0 when the tire pressure change rate of the driving wheel is 2.5, and the membership of P5 is 1 when the tire pressure change rate of the driving wheel is [3, 4 ].
8. The inter-wheel differential anti-drag control method for the steering of the distributed independently driven electric vehicle as claimed in claim 6, wherein the domain of the vehicle yaw rate change rate is set to be [ -8, 8], the membership function of the fuzzy set A1 is a trapezoidal function, the corresponding domain of the vehicle yaw rate change rate is [ -8, -3], the A1 membership is 1 when the vehicle yaw rate change rate is [ -8, -7], and the A1 membership is 0 when the vehicle yaw rate change rate is-3; the membership function of the fuzzy set A2 is a triangular function, and the corresponding discourse domain of the vehicle yaw velocity change rate is [ -5, -1 ]; the membership function of the fuzzy set A3 is a triangular function, and the corresponding discourse domain of the vehicle yaw velocity change rate is [ -3, 3 ]; the membership function of the fuzzy set A4 is a triangular function, and the corresponding discourse domain of the vehicle yaw velocity change rate is [1, 5 ]; the membership function of the fuzzy set A5 is a trapezoidal function, the domain of the corresponding vehicle yaw velocity change rate is [3, 8], the A5 membership is 0 when the driving wheel tire pressure change rate is 3, and the A5 membership is 1 when the vehicle yaw velocity change rate is [7, 8 ].
9. The differential anti-drag control method between the steering wheels of the distributed independently-driven electric vehicle as claimed in claim 6, wherein the domain of discourse of the integral parameter is set as [0.8, 1.2], the membership function of the fuzzy sets I1, I2, I3, I4 and I5 is a triangular function, the domain of the integral parameter corresponding to the fuzzy set I1 is [0.8, 0.85], the membership of I1 is 1 when the integral parameter is 0.8, and the membership of I1 is 0 when the integral parameter is 0.85; the domain of integration parameters corresponding to the fuzzy set I2 is [0.8, 0.9 ]; the domain of discourse of the integral parameters corresponding to the fuzzy set I3 is [0.85, 1.15], the domain of the integral parameters corresponding to the fuzzy set I4 is [1.1, 1.2], the domain of the integral parameters corresponding to the fuzzy set I5 is [1.15, 1.2], the membership degree of I5 is 0 when the integral parameters are 1.15, and the membership degree of I5 is 1 when the integral parameters are 1.2.
10. The differential anti-drag control method between the wheels during the steering of the distributed independent drive electric automobile according to claim 9, wherein when the fuzzy controller outputs an integral parameter, a maximum membership method is adopted according to the fuzzy set of the integral parameter to obtain a precise numerical value.
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