CN114889450A - Torque distribution control method of distributed intelligent electric vehicle - Google Patents

Torque distribution control method of distributed intelligent electric vehicle Download PDF

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CN114889450A
CN114889450A CN202210738853.XA CN202210738853A CN114889450A CN 114889450 A CN114889450 A CN 114889450A CN 202210738853 A CN202210738853 A CN 202210738853A CN 114889450 A CN114889450 A CN 114889450A
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徐友春
潘世举
李子先
李建市
朱愿
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Military Transportation Research Institute Of Chinese People's Liberation Army Army Military Transportation Academy
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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
    • 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
    • B60L2240/10—Vehicle control parameters
    • B60L2240/12—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
    • 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
    • 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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Abstract

The invention relates to a torque distribution control method of a distributed intelligent electric vehicle, which is characterized by comprising the following steps: adopts a layered structure: wherein the upper layer is a yaw moment decision layer, and the lower layer is a torque distribution layer; calculating a sliding mode expected yaw moment and calculating an expected yaw moment at the upper layer of the control system; the expected torques of the four wheels are solved and corrected at the lower layer of the control system, so that the influence of uncertain load on system control can be eliminated, and the safety and stability of the distributed electric drive vehicle are improved. Has the advantages that: the vehicle based on the invention has better adaptability to the uncertainty of the overall vehicle quality, and has the vehicle posture maintaining capability and the motion tracking capability. The influence of uncertain load on system control can be eliminated, and the method has better stability and traceability.

Description

Torque distribution control method of distributed intelligent electric vehicle
Technical Field
The invention belongs to the technical field of intelligent electric vehicles, and particularly relates to a torque distribution control method of a distributed intelligent electric vehicle.
Background
The pure electric vehicle has the advantages of small environmental pollution, wide energy sources, low cost of the whole vehicle and the like, and has good development prospect. The distributed driving electric automobile adopts the motor to directly drive the wheels of the automobile, and has the outstanding characteristics of short transmission chain, high transmission efficiency and the like. The motor is an information unit of the automobile and a quick-response execution unit, and direct yaw moment and driving anti-skid control can be realized by reasonably distributing the motor torque of each driving wheel, so that the active safety of the automobile is improved.
For the distributed electric drive vehicle, a large amount of mechanical equipment such as a heavy engine and a mechanical transmission device of the traditional vehicle is omitted, the quality of the whole vehicle is reduced, and the distributed electric drive vehicle is relatively sensitive to the change of the load parameters. In the existing research, the main focus is on a model-based control strategy, and the influence caused by load uncertainty is less considered.
Disclosure of Invention
The invention aims to overcome the defects of the technology, and provides a torque distribution control method of a distributed intelligent electric vehicle, which adopts a layered structure: the method for solving the yaw moment can improve the uncertain control performance of the system to the load; the torque distribution method can improve the safety and stability of the distributed electric drive vehicle.
In order to achieve the purpose, the invention adopts the following technical scheme: a torque distribution control method of a distributed intelligent electric vehicle is characterized by comprising the following steps: adopts a layered structure: the upper layer is a yaw moment decision layer, and the lower layer is a torque distribution layer; calculating the expected yaw moment of the sliding mode at the upper layer of the control system and carrying out self-adaptive compensation on the expected yaw moment; the method comprises the following steps of calculating the output torques of four wheels at the lower layer of a control system, namely eliminating the influence of uncertain load on system control and improving the safety and stability of the distributed electric drive vehicle, wherein the method comprises the following specific steps:
step 1): establishing a vehicle state space equation;
step 2): calculating a desired centroid yaw angle and a desired yaw rate from the vehicle longitudinal velocity and the front wheel steering angle;
step 3): constructing a novel sliding mode function of which the mass center side slip angle and the yaw angular velocity simultaneously converge to expected values, and calculating the expected yaw moment of the sliding mode;
step 4): carrying out self-adaptive compensation on the sliding mode expected yaw moment, and calculating to obtain the expected yaw moment;
step 5): aiming at the yaw moment, solving the expected torques of the four wheels by adopting an optimal distribution method considering the tire utilization rate;
step 6): and correcting the expected wheel torque aiming at the problem that the wheel is easy to slip when the vehicle starts.
Further, the vehicle state space equation in step 1) is specifically:
s21: the vehicle state space equation is as follows:
Figure BDA0003716849290000021
wherein x is 1 ＝β；x 2 ＝ω rc ；
Figure BDA0003716849290000022
Figure BDA0003716849290000023
Kappa is the interference term due to uncertainty of the parameters, the upper bound being
Figure BDA0003716849290000025
Δ m is the unknown additional bearer quality; c f Representing the front tire cornering stiffness; c r Representing the rear tire cornering stiffness; beta represents the centroid slip angle; omega rc Representing a yaw rate; m represents a yaw moment about the center of mass; m represents a vehicle mass; delta f Indicating a front wheel turning angle; i is z Representing the yaw moment of inertia; a and b represent the distance from the center of mass of the vehicle to the front and rear axles, respectively; v. of x Representing the vehicle longitudinal speed.
Further, in step 2), the desired centroid slip angle and the desired yaw rate specifically include:
s31: the desired centroid slip angle is calculated as follows:
Figure BDA0003716849290000024
wherein, the meaning of the parameter in the formula is consistent with the meaning represented in the formulas S21 and S22, and beta d Representing a desired centroid slip angle; k represents a stability factor; μ represents a wheel base; μ represents a road surface adhesion coefficient; g represents the gravitational acceleration;
s32: the desired yaw rate is calculated as follows:
Figure BDA0003716849290000031
wherein, the meaning of the parameter in the formula is consistent with the meaning represented in the formulas S21 and S31, and omega rcd Indicating the desired yaw rate.
Further, in step 3), the desired yaw moment of the sliding form is specifically:
s41: the centroid slip angle tracking error is calculated as follows:
e 1 ＝x 1 -x 1d
wherein e is 1 Representing centroid slip angle tracking error, x 1 Representing vehicle mass center slip angle, x 1d Representing the system state quantity x 1 Desired value of (1), i.e. desired centroid slip angle β d ；
S42: the 2 nd tracking error is calculated as follows:
Figure BDA0003716849290000032
wherein, the meaning of the parameter in the formula is consistent with the meaning represented in the formulas S22 and S41, and x 2 Representing a virtual input quantity; x is the number of 2d Representing a desire for a virtual input quantity; zeta 1 Representing sliding mode parameters which are normal numbers;
s43: the sliding mode function is as follows:
s＝ζ 2 e 1 +e 2
wherein, the meaning of the parameter in the formula is consistent with the meaning represented in the formulas S41 and S42, and S represents a sliding mode function; zeta 2 Representing sliding mode parameters which are normal numbers;
s44: the approach law is as follows:
Figure BDA0003716849290000034
wherein the meaning of the parameter in the formula is identical to that of the formula S43, and k is 1 、k 2 Represents a controller parameter and satisfies
Figure BDA0003716849290000033
S45: the desired yaw moment for the sliding mode is calculated as follows:
Figure BDA0003716849290000041
wherein, the meaning of the parameter in the formula is consistent with the meaning represented in the formulas S22, S44, S43, S42 and S41;
further, in step 4), the adaptive dynamic sliding film desired yaw moment specifically includes:
s51: the neural network inputs are as follows:
Figure BDA0003716849290000042
wherein, the meaning of the parameter in the formula is consistent with the meaning represented in the formulas S43 and S44;
s52: the hidden layer neuron output matrix is as follows:
Figure BDA0003716849290000043
wherein the parameters in the formula represent meanings as in the formula S51Meaning coincidence, i denotes the number of inputs to the network, j denotes the number of nodes of the hidden layer, b ij Representing the neuron width value of the jth hidden node corresponding to the ith input, c ij Representing the j neuron center parameter value corresponding to the ith input;
s53: the estimates of the interference terms are as follows:
Figure BDA0003716849290000044
wherein, the meaning of the parameter in the formula is consistent with the meaning represented in the formulas S51 and S52, and omega j Is a network weight matrix;
s54: the adaptive dynamic synovial expected yaw moment M is calculated as follows:
Figure BDA0003716849290000045
wherein, the meaning of the parameter in the formula is consistent with the meaning represented in the formulas S22, S44, S43, S42, S41 and S53, and k is 1 、k 2 Represents a controller parameter and satisfies
Figure BDA0003716849290000046
S55: the update rate of the network weight matrix is as follows:
Figure BDA0003716849290000051
wherein, the meaning of the parameter in the formula is consistent with the meaning represented in the formulas S21, S52 and S43, eta represents the update rate coefficient and is a normal number
S56：b ij And c ij The update rule of (2) is calculated as follows:
Figure BDA0003716849290000052
wherein the meaning of the parameter in the formula is consistent with the meaning represented in the formulas S21, S51, S52, S53, S43 and S44, gamma epsilon (0,1) represents the learning rate,
Figure BDA0003716849290000053
representing the momentum factor.
Further, in step 5), the desired torques of the four wheels are specifically:
s61: and optimizing torque distribution by adopting quadratic programming, taking the minimum tire utilization rate as an objective function, and solving the output torque of each wheel as follows:
Figure BDA0003716849290000054
wherein the meaning of the parameter in the formula is identical to the meaning represented in the formulas S21 and S54, r represents the rolling radius of the tire, d represents the wheel track, T represents the wheel track i Representing motor output torque, F zi Indicates the tire vertical force, i is 1,2,3, 4.
Further, the step 6) of correcting the wheel desired torque by using a PID control method specifically includes:
s71: the target slip ratio is calculated as follows:
S goal ＝(S min +S max )/2
wherein S is goal Representing a target slip ratio; s min And S max Respectively representing the maximum value and the minimum value of the target slip rate;
s72: slip difference values were calculated as follows:
λ i ＝S goal -S i-real ,i＝1,2,3,4
wherein the meaning of the parameter in the formula is identical to the meaning of the parameter in the formula S71, S i_real Representing the actual slip ratio of the ith wheel;
s73: the PID-based torque correction is calculated as follows:
Figure BDA0003716849290000061
wherein the meaning of the parameter in the formula is identical to that of the formula S72, and k is P Denotes the proportional gain, k I Representing the integral gain, k D Represents a differential gain;
s74: the desired torques for the four wheels are calculated as follows:
T i d ＝T i +ΔT i ,i＝1,2,3,4
wherein the meaning of the parameter in the formula is identical to the meaning of the parameter in the formula S73.
Has the advantages that: the distributed driving electric vehicle torque distribution control method provided by the invention aims at the influence of the load parameters on the distributed electric driving vehicle, and the vehicle based on the distributed driving electric vehicle torque distribution control method has better adaptability to the uncertainty of the overall vehicle quality and has the vehicle posture holding capability and the motion tracking capability. The influence of uncertain load on system control can be eliminated, and the method has better stability and traceability.
Drawings
FIG. 1 is a block diagram of a control strategy of the present invention;
FIG. 2 is a schematic diagram of a two degree-of-freedom vehicle dynamics model;
FIG. 3 is a schematic view of an RBF neural network;
FIG. 4 is a schematic diagram of the motion trajectory of an uncontrolled vehicle of test scenario 1;
FIG. 5 is a schematic diagram of the vehicle speed tracking response of the uncontrolled vehicle of test scenario 1;
FIG. 6 is a comparative schematic diagram of the vehicle centroid slip angles for test scenario 1;
FIG. 7 is a comparative schematic of vehicle yaw rate for test scenario 1;
FIG. 8 is a schematic diagram of the movement trajectory of an uncontrolled vehicle of test field 2;
FIG. 9 is a schematic of the vehicle speed tracking response of the uncontrolled vehicle of test scenario 2;
FIG. 10 is a comparative schematic of vehicle centroid slip angles for test scenario 2;
fig. 11 is a comparative schematic diagram of the vehicle yaw rate in test scenario 2.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments thereof which are illustrated in the appended drawings. In addition, the embodiments and features of the embodiments of the present application may be combined with each other without conflict. In the following description, numerous specific details are set forth to provide a thorough understanding of the present invention, and the described embodiments are merely a subset of the embodiments of the present invention, rather than a complete embodiment. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
Referring to fig. 1 in detail, the present embodiment provides a torque distribution control method for a distributed intelligent electric vehicle, which is characterized in that: adopts a layered structure: wherein the upper layer is a yaw moment decision layer, and the lower layer is a torque distribution layer; calculating the expected yaw moment of the sliding mode at the upper layer of the control system and carrying out self-adaptive compensation on the expected yaw moment; the method comprises the following steps of solving expected torques of four wheels at the lower layer of a control system and correcting wheel torques, namely eliminating the influence of uncertain load on system control and improving the safety and stability of the distributed electric drive vehicle, and comprises the following specific steps:
step 1): establishing a vehicle state space equation;
step 2): calculating a desired centroid yaw angle and a desired yaw rate from the vehicle longitudinal velocity and the front wheel steering angle;
step 3): constructing a novel sliding mode function with the mass center side drift angle and the yaw angular velocity simultaneously converging to the expected value based on a back stepping method, and calculating the expected yaw moment of the sliding mode;
step 4): based on RBF neural network, carrying out self-adaptive compensation on the sliding mode expected yaw moment, and calculating to obtain the expected yaw moment;
step 5): aiming at the yaw moment, solving the expected torques of the four wheels by adopting an optimal distribution method considering the tire utilization rate;
step 6): and aiming at the problem that the wheel is easy to slip when the vehicle starts, a PID control method is adopted to correct the expected torque of the wheel.
The preferable scheme of this embodiment is that the vehicle state space equation in step 1) specifically includes:
s21: the vehicle state space equation is as follows:
Figure BDA0003716849290000081
wherein x is 1 ＝β；x 2 ＝ω rc ；
Figure BDA0003716849290000082
Figure BDA0003716849290000083
Kappa is the interference term due to uncertainty of the parameters, the upper bound being
Figure BDA0003716849290000084
Δ m is the unknown additional bearer quality; c f Representing the front tire cornering stiffness; c r Representing the rear tire cornering stiffness; beta represents the centroid slip angle; omega rc Representing a yaw rate; m represents a yaw moment about the center of mass; m represents a vehicle mass; delta f Indicating a front wheel turning angle; i is z Representing the yaw moment of inertia; a and b represent the distance from the center of mass of the vehicle to the front and rear axles, respectively; v. of x Representing the vehicle longitudinal speed.
In a preferred embodiment of this embodiment, in step 2), the desired centroid slip angle and the desired yaw rate specifically include:
s31: the desired centroid slip angle is calculated as follows:
Figure BDA0003716849290000085
wherein, the meaning of the parameter in the formula is consistent with the meaning represented in the formulas S21 and S22, and beta d Representing a desired centroid slip angle; k represents a stability factor; μ represents a wheel base; μ represents a road surface adhesion coefficient; g represents the gravitational acceleration;
s32: the desired yaw rate is calculated as follows:
Figure BDA0003716849290000086
wherein, the meaning of the parameter in the formula is consistent with the meaning represented in the formulas S21 and S31, and omega rcd Indicating the desired yaw rate.
The preferable scheme of this embodiment is that, in step 3), the desired yaw moment of the sliding form specifically includes:
s41: the centroid slip angle tracking error is calculated as follows:
e 1 ＝x 1 -x 1d
wherein e is 1 Representing centroid slip angle tracking error, x 1 Representing vehicle mass center slip angle, x 1d Representing the system state quantity x 1 Desired value of (1), i.e. desired centroid slip angle β d ；
S42: the 2 nd tracking error is calculated as follows:
Figure BDA0003716849290000091
wherein, the meaning of the parameter in the formula is consistent with the meaning represented in the formulas S22 and S41, and x 2 Representing a virtual input quantity; x is the number of 2d Representing a desire for a virtual input quantity; zeta 1 Representing sliding mode parameters which are normal numbers;
s43: the sliding mode function is as follows:
s＝ζ 2 e 1 +e 2
wherein in the formulaThe meaning of the parameter is consistent with the meaning represented in formulas S41 and S42, and S represents a sliding mode function; zeta 2 Representing sliding mode parameters which are normal numbers;
s44: the approach law is as follows:
Figure BDA0003716849290000092
wherein the meaning of the parameter in the formula is identical to that of the formula S43, and k is 1 、k 2 Represents a controller parameter and satisfies
Figure BDA0003716849290000093
S45: the desired yaw moment for the sliding mode is calculated as follows:
Figure BDA0003716849290000094
wherein, the meaning of the parameter in the formula is consistent with the meaning represented in the formulas S22, S44, S43, S42 and S41;
in a preferred embodiment of this embodiment, in step 4), the adaptive dynamic sliding film is specifically configured to:
s51: the neural network inputs are as follows:
Figure BDA0003716849290000095
wherein, the meaning of the parameter in the formula is consistent with the meaning represented in the formulas S43 and S44;
s52: the hidden layer neuron output matrix is as follows:
Figure BDA0003716849290000101
wherein the meaning of the parameter in the formula is the same as that in the formula S51, i represents the number of network inputs, and j represents the hidden layerNumber of nodes, b ij Representing the neuron width value of the jth hidden node corresponding to the ith input, c ij Representing the j neuron center parameter value corresponding to the ith input;
s53: the estimates of the interference terms are as follows:
Figure BDA0003716849290000102
wherein, the meaning of the parameter in the formula is consistent with the meaning represented in the formulas S51 and S52, and omega j Is a network weight matrix;
s54: the adaptive dynamic synovial expected yaw moment M is calculated as follows:
Figure BDA0003716849290000103
wherein, the meaning of the parameter in the formula is consistent with the meaning represented in the formulas S22, S44, S43, S42, S41 and S53, and k is 1 、k 2 Represents a controller parameter and satisfies
Figure BDA0003716849290000104
S55: the update rate of the network weight matrix is as follows:
Figure BDA0003716849290000105
wherein, the meaning of the parameter in the formula is consistent with the meaning represented in the formulas S21, S52 and S43, eta represents the update rate coefficient and is a normal number
S56：b ij And c ij The update rule of (2) is calculated as follows:
Figure BDA0003716849290000106
wherein the parameters in the formula represent meanings of S21, S51, S52, S53, S43 and S44, γ ∈ (0,1) denotes the learning rate,
Figure BDA0003716849290000107
representing the momentum factor.
In a preferable scheme of this embodiment, in step 5), the expected torques of the four wheels specifically are:
s61: and optimizing torque distribution by adopting quadratic programming, taking the minimum tire utilization rate as an objective function, and solving the output torque of each wheel as follows:
Figure BDA0003716849290000111
wherein the meaning of the parameter in the formula is identical to the meaning represented in the formulas S21 and S54, r represents the rolling radius of the tire, d represents the wheel track, T represents the wheel track i Representing motor output torque, F zi Indicates the tire vertical force, i is 1,2,3, 4.
The preferable scheme of this embodiment is that the step 6) of correcting the wheel desired torque by using a PID control method specifically includes:
s71: the target slip ratio is calculated as follows:
S goal ＝(S min +S max )/2
wherein S is goal Representing a target slip ratio; s min And S max Respectively representing the maximum value and the minimum value of the target slip rate;
s72: slip difference values were calculated as follows:
λ i ＝S goal -S i-real ,i＝1,2,3,4
wherein the meaning of the parameter in the formula is identical to the meaning of the parameter in the formula S71, S i_real Representing the actual slip ratio of the ith wheel;
s73: the PID-based torque correction is calculated as follows:
Figure BDA0003716849290000112
wherein the meaning of the parameter in the formula is identical to that of the formula S72, and k is P Denotes the proportional gain, k I Representing the integral gain, k D Represents a differential gain;
s74: the desired torques for the four wheels are calculated as follows:
T i d ＝T i +ΔT i ,i＝1,2,3,4
wherein the meaning of the parameter in the formula is identical to the meaning of the parameter in the formula S73.
Examples
The simulation experiment under the working condition of double shifting lines is designed based on a Carsim/Simulink software combined simulation platform, and the comparison experiment verification based on the traditional first-order slip film control and no control is carried out.
The invention adopts a layered structure: the upper layer is a yaw moment decision layer, and the lower layer is a torque distribution layer. On the upper layer of the control system, a novel sliding mode function with the mass center side drift angle and the yaw velocity simultaneously converging to expected values is constructed based on a backstepping method, and the unknown interference amount in the dynamic system is subjected to self-adaptive compensation by using an RBF neural network to obtain the yaw moment output. At the lower layer of the control system, solving the moments of four wheels by adopting an optimal distribution method considering the tire utilization rate; aiming at the problem that the wheel slip is easy to occur during vehicle starting, a PID control method is adopted to correct the wheel torque.
Vehicle model parameters and simulation scenarios
(1) The vehicle model parameters are as follows:
Figure BDA0003716849290000121
(2) two test scenes are set, which are respectively as follows:
1) the pavement adhesion coefficient is 0.4 at the speed of 65km/h, and the extra bearing mass is 0;
2) the speed is 65km/h, the adhesion is 0.4, and the mass of the whole vehicle is increased by 200 kg;
second, test results
(1) The results of the scenario 1 test are shown in fig. 4-7. As can be seen from FIG. 4, the uncontrolled vehicle experiences a large sideslip, and the deviation of the driving trajectory from the desired trajectory is large; the vehicle based on the traditional first-order slip film control can basically ensure safe driving, but a large overshoot occurs at a turning position; the accuracy of the expected track tracking of the vehicle based on the invention is obviously improved, the vehicle has stronger over-bending posture correction capability, and the condition of overlarge sideslip is basically avoided. As can be seen from fig. 5, the speed tracking response of the uncontrolled vehicle is slow and a certain fluctuation occurs; the vehicle speed tracking based on the traditional first-order slip film control and the vehicle speed tracking based on the invention has faster response, but the vehicle based on the traditional first-order slip film control has certain overshoot. Fig. 6 and 7 show a comparison of the vehicle's centroid slip angle and yaw rate for three control modes, where both the conventional first order slip film control and the vehicle's actual centroid slip angle and yaw rate based on the present invention can accurately and quickly track the desired values as compared to no control.
(2) The results of the scenario 2 test are shown in fig. 8-11. As can be seen from fig. 8, the vehicle without control and the vehicle based on the conventional first-order slip film control have large sideslip, and the vehicle running track deviates greatly from the expected track at the longitudinal coordinate of 280 m; the vehicle based on the invention can track the expected track although the vehicle has a certain lateral displacement. As can be seen from fig. 9, the vehicle speed tracking response of the uncontrolled vehicle and the vehicle speed tracking response based on the conventional first-order slip film control are both reduced compared with the first scenario, and a certain buffeting occurs; the vehicle speed tracking response based on the invention is obviously improved, and the buffeting is also obviously improved. As can be seen from fig. 10 and 11, the deviation of the yaw rate and the centroid slip angle of the vehicle without control is the largest, the vehicle stability is poor, and after the control of the traditional first-order slip film, the deviation of the yaw rate and the centroid slip angle is obviously reduced, but certain fluctuation still exists; the deviation of the yaw angular velocity and the centroid slip angle of the vehicle is minimum, and the change trend is gentle, so that the vehicle based on the invention has better adaptability to the uncertainty of the overall vehicle quality, and has the attitude keeping capability and the motion tracking capability of the vehicle.
The above detailed description of the torque distribution control method for a distributed intelligent electric vehicle with reference to the embodiments is illustrative and not restrictive, and several embodiments may be enumerated within the scope of limitations thereof, so that changes and modifications may be made without departing from the spirit of the present invention.

Claims (7)

1. A torque distribution control method of a distributed intelligent electric vehicle is characterized by comprising the following steps: a layered structure is adopted, wherein the upper layer is a yaw moment decision layer, and the lower layer is a torque distribution layer; calculating the expected yaw moment of the sliding mode at the upper layer of the control system and carrying out self-adaptive compensation on the expected yaw moment; the method is characterized in that expected torques of four wheels are solved at the lower layer of a control system, so that the influence of uncertain load on system control can be eliminated, and the safety and stability of the distributed electric drive vehicle are improved, and the method specifically comprises the following steps:
step 1) establishing a vehicle state space equation;
step 2) calculating an expected mass center slip angle and an expected yaw rate according to the longitudinal speed of the vehicle and the front wheel rotation angle;
step 3) constructing a sliding mode function of which the mass center side drift angle and the yaw angular velocity are converged to expected values at the same time, and calculating the expected yaw moment of the sliding mode;
step 4) carrying out self-adaptive compensation on the sliding mode expected yaw moment, and calculating to obtain the expected yaw moment;
step 5) aiming at the yaw moment, adopting an optimal distribution method to solve the expected torques of the four wheels;
and 6) correcting the expected wheel torque aiming at the problem that the wheel is easy to slip when the vehicle starts.
2. The distributed intelligent electric vehicle torque distribution control method according to claim 1, wherein: the vehicle state space equation in the step 1) is specifically as follows:
s21: the vehicle state space equation is as follows:
Figure FDA0003716849280000011
wherein x is 1 ＝β；x 2 ＝ω rc ；
Figure FDA0003716849280000012
Figure FDA0003716849280000013
Kappa is the interference term due to uncertainty of the parameters, the upper bound being
Figure FDA0003716849280000014
Δ m is the unknown additional bearer quality; c f Representing the front tire cornering stiffness; c r Representing the rear tire cornering stiffness; beta represents the centroid slip angle; omega rc Representing a yaw rate; m represents a yaw moment about the center of mass; m represents a vehicle mass; delta f Indicating a front wheel turning angle; i is z Representing the yaw moment of inertia; a and b represent the distance from the center of mass of the vehicle to the front and rear axles, respectively; v. of x Representing the vehicle longitudinal speed.
3. The distributed intelligent electric vehicle torque distribution control method according to claim 1, wherein: in step 2), the desired centroid slip angle and the desired yaw rate specifically include:
s31: the desired centroid slip angle is calculated as follows:
Figure FDA0003716849280000021
wherein, the meaning of the parameter in the formula is consistent with the meaning represented in the formulas S21 and S22, and beta d Representing a desired centroid slip angle; k represents a stability factor; μ represents a wheel base; μ represents a road surface adhesion coefficient; g represents the gravitational acceleration;
s32: the desired yaw rate is calculated as follows:
Figure FDA0003716849280000022
wherein, the meaning of the parameter in the formula is consistent with the meaning represented in the formulas S21 and S31, and omega rcd Indicating the desired yaw rate.
4. The distributed intelligent electric vehicle torque distribution control method according to claim 1, wherein: in step 3), the desired yaw moment of the sliding form is specifically as follows:
s41: the centroid slip angle tracking error is calculated as follows:
e 1 ＝x 1 -x 1d
wherein e is 1 Representing centroid slip angle tracking error, x 1 Representing vehicle mass center slip angle, x 1d Representing the system state quantity x 1 Desired value of (1), i.e. desired centroid slip angle β d ；
S42: the 2 nd tracking error is calculated as follows:
Figure FDA0003716849280000023
wherein, the meaning of the parameter in the formula is consistent with the meaning represented in the formulas S22 and S41, and x 2 Representing a virtual input quantity; x is the number of 2d Representing a desire for a virtual input quantity; zeta 1 Representing sliding mode parameters which are normal numbers;
s43: the sliding mode function is as follows:
s＝ζ 2 e 1 +e 2
wherein, the meaning of the parameter in the formula is consistent with the meaning represented in the formulas S41 and S42, and S represents a sliding mode function; zeta 2 Representing sliding mode parameters which are normal numbers;
s44: the approach law is as follows:
Figure FDA0003716849280000031
whereinThe meaning of the parameter in the formula is identical to that of the formula S43, k 1 、k 2 Represents a controller parameter and satisfies
Figure FDA0003716849280000032
S45: the desired yaw moment for the sliding mode is calculated as follows:
Figure FDA0003716849280000033
wherein, the meaning of the parameter in the formula is consistent with the meaning represented in the formulas S22, S44, S43, S42 and S41.
5. The distributed intelligent electric vehicle torque distribution control method according to claim 1, wherein: in the step 4), the desired yaw moment of the self-adaptive dynamic sliding film specifically comprises the following steps:
s51: the neural network inputs are as follows:
Figure FDA0003716849280000034
wherein, the meaning of the parameter in the formula is consistent with the meaning represented in the formulas S43 and S44;
s52: the hidden layer neuron output matrix is as follows:
Figure FDA0003716849280000035
wherein the meaning of the parameter in the formula is identical to the meaning represented in the formula S51, i represents the number of network inputs, j represents the number of nodes of the hidden layer, b ij Representing the neuron width value of the jth hidden node corresponding to the ith input, c ij Representing the j neuron center parameter value corresponding to the ith input;
s53: the estimates of the interference terms are as follows:
Figure FDA0003716849280000036
wherein, the meaning of the parameter in the formula is consistent with the meaning represented in the formulas S51 and S52, and omega j Is a network weight matrix;
s54: the adaptive dynamic synovial expected yaw moment M is calculated as follows:
Figure FDA0003716849280000041
wherein, the meaning of the parameter in the formula is consistent with the meaning represented in the formulas S22, S44, S43, S42, S41 and S53, and k is 1 、k 2 Represents a controller parameter and satisfies
Figure FDA0003716849280000042
S55: the update rate of the network weight matrix is as follows:
Figure FDA0003716849280000043
wherein, the meaning of the parameter in the formula is consistent with the meaning represented in the formulas S21, S52 and S43, eta represents the update rate coefficient and is a normal number
S56：b ij And c ij The update rule of (2) is calculated as follows:
Figure FDA0003716849280000044
wherein the meaning of the parameter in the formula is consistent with the meaning represented in the formulas S21, S51, S52, S53, S43 and S44, gamma epsilon (0,1) represents the learning rate,
Figure FDA0003716849280000045
representing the momentum factor.
6. The distributed intelligent electric vehicle torque distribution control method according to claim 1, wherein: in step 5), the expected torques of the four wheels are specifically:
s61: and optimizing torque distribution by adopting quadratic programming, taking the minimum tire utilization rate as an objective function, and solving the output torque of each wheel as follows:
Figure FDA0003716849280000051
wherein the meaning of the parameter in the formula is identical to the meaning represented in the formulas S21 and S54, r represents the rolling radius of the tire, d represents the wheel track, T represents the wheel track i Representing motor output torque, F zi Indicates the tire vertical force, i is 1,2,3, 4.
7. The distributed intelligent electric vehicle torque distribution control method according to claim 1, wherein: and 6) correcting the expected wheel torque by adopting a PID control method, which specifically comprises the following steps:
s71: the target slip ratio is calculated as follows:
S goal ＝(S min +S max )/2
wherein S is goal Representing a target slip ratio; s min And S max Respectively representing the maximum value and the minimum value of the target slip rate;
s72: slip difference values were calculated as follows:
λ i ＝S goal -S i-real ,i＝1,2,3,4
wherein the meaning of the parameter in the formula is identical to the meaning of the parameter in the formula S71, S ireal Representing the actual slip rate of the ith wheel;
s73: the PID-based torque correction is calculated as follows:
Figure FDA0003716849280000052
wherein the meaning of the parameter in the formula is identical to that of the formula S72, and k is P Denotes the proportional gain, k I Representing the integral gain, k D Represents a differential gain;
s74: the desired torques for the four wheels are calculated as follows:
T i d ＝T i +ΔT i ,i＝1,2,3,4
wherein the meaning of the parameter in the formula is identical to the meaning of the parameter in the formula S73.
CN202210738853.XA 2022-06-28 2022-06-28 Torque distribution control method of distributed intelligent electric vehicle Pending CN114889450A (en)

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