A distributed drive electric vehicle fault limp maneuvering enhancement control method and system

Abstract

The embodiments of the present invention provide a method and system for enhancing control of fault limp maneuvering of a distributed drive electric vehicle, and relate to the field of data processing technology. The method comprises: obtaining current vehicle parameters and wheel hub motor fault information; calculating the current vehicle parameters based on a preset desired state estimation model to obtain a vehicle state control target; calculating the current vehicle parameters and the vehicle state control target based on a preset optimization control model to obtain a direct yaw moment and an active front wheel angle; determining an initial torque adjustment parameter corresponding to the wheel hub motor fault information; determining torque compensation allocation information based on the wheel hub motor fault information and the direct yaw moment; integrating the initial torque adjustment parameter and the torque compensation allocation information to obtain a target torque adjustment information; determining the target torque adjustment information and the active front wheel angle as the current control information of the distributed drive electric vehicle. The present invention can improve the driving stability of the vehicle.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of data processing technology. More specifically, the embodiments of the present invention relate to a method and system for enhancing control of fault limp homeostasis of a distributed drive electric vehicle. Background Art

[0002] Since the four-wheel torque of distributed independently driven electric vehicles can be independently and accurately controlled, it has gradually become the development direction of electric vehicle drive forms, and the wire-controlled chassis technology also provides distributed independently driven electric vehicles with precise and effective dynamic control means.

[0003] However, it is found in practice that the control targets of the independent control of four-wheel torque and the wire-controlled chassis technology are both the hub motors, which leads to a redundant number of controllers for controlling the hub motors. When a distributed drive electric vehicle fails and is in a limp state, complex failure situations will occur. The existing control methods cannot accurately control different hub motors, resulting in unstable driving conditions of the vehicle. Summary of the invention

[0004] In this context, embodiments of the present invention are intended to provide a method and system for enhanced control of fault limp homeostasis of a distributed drive electric vehicle.

[0005] In a first aspect of an embodiment of the present invention, a method for enhancing control of fault limp homeostasis of a distributed drive electric vehicle is provided, comprising:

[0006] When it is detected that the longitudinal speed of the distributed drive electric vehicle is less than a preset speed, the current vehicle parameters of the distributed drive electric vehicle and the hub motor fault information are obtained; wherein the hub motor fault information includes the fault type, fault level and the position of the faulty hub motor;

[0007] The current vehicle parameters are calculated based on a preset desired state estimation model to obtain a vehicle state control target; wherein the vehicle state control target includes a desired yaw angle and a desired sideslip angle of the center of mass;

[0008] Calculating the current vehicle parameters and the vehicle state control target based on a preset optimization control model to obtain a direct yaw moment and an active front wheel steering angle;

[0009] Determining an initial torque adjustment parameter corresponding to the hub motor fault information;

[0010] Based on the wheel hub motor fault information and the direct yaw moment, torque compensation allocation information is determined; wherein the torque compensation allocation information includes a target compensation wheel hub motor and a torque compensation parameter of the target compensation wheel hub motor;

[0011] Integrate the initial torque adjustment parameter and the torque compensation allocation information to obtain target torque adjustment information; wherein the target torque adjustment information includes the target compensation hub motor, the torque compensation parameter of the target compensation hub motor, the target adjustment hub motor and the torque adjustment parameter of the target adjustment hub motor; the target compensation hub motor and the target adjustment hub motor are different;

[0012] The target torque adjustment information and the active front wheel steering angle are determined as current control information of the distributed drive electric vehicle.

[0013] In one embodiment of this implementation, the current vehicle parameters are calculated based on a preset desired state estimation model to obtain a vehicle state control target, including:

[0014] Obtaining the current longitudinal speed of the vehicle from the current vehicle parameters;

[0015] Calculating a yaw angle error weight based on the current longitudinal speed of the vehicle;

[0016] Calculate an initial desired yaw angle and a maximum yaw angle based on the current vehicle parameters;

[0017] Calculating a desired yaw angle based on the initial desired yaw angle, the maximum yaw angle and current vehicle parameters;

[0018] The desired yaw angle and a preset desired sideslip angle of the center of mass are determined as vehicle state control targets.

[0019] In one embodiment of this implementation, the formula for the yaw angle error weight is:

[0020]

[0021] in, represents the yaw angle error weight, Indicates the current longitudinal speed of the vehicle.

[0022] In one embodiment of this implementation manner, the calculating the initial desired yaw angle and the maximum yaw angle based on the current vehicle parameters includes:

[0023] Calculating an initial desired yaw angle based on the current longitudinal speed of the vehicle and the vehicle wheelbase, design stability factor, and front wheel turning angle among the current vehicle parameters;

[0024] The maximum yaw angle value is calculated based on the current longitudinal speed of the vehicle and the lateral acceleration in the current vehicle parameters.

[0025] In one embodiment of this implementation, the calculation formula for calculating the initial expected yaw angle based on the current vehicle longitudinal speed and the distance in the current vehicle parameter is:

[0026]

[0027] in, represents the initial desired yaw angle, represents the current longitudinal speed of the vehicle, L represents the wheelbase of the vehicle, represents the design stability factor, represents the front wheel turning angle.

[0028] In one embodiment of this implementation manner, the calculation formula for calculating the maximum yaw angle based on the current vehicle longitudinal speed and the acceleration in the current vehicle parameters is:

[0029]

[0030] in, represents the maximum yaw angle, represents the current longitudinal speed of the vehicle, represents the lateral acceleration.

[0031] In one embodiment of this implementation, the calculation formula for calculating the desired yaw angle based on the initial desired yaw angle, the maximum yaw angle and the current vehicle parameters is:

[0032]

[0033] in, represents the desired yaw angle, represents the initial desired yaw angle, represents the maximum value of the yaw angle, and sign() represents the sign function.

[0034] In an example of this implementation, the fault types include single motor fault, coaxial dual motor fault, same-side dual motor fault, and different-axis different-side dual motor fault.

[0035] In one embodiment of this implementation manner, the determining of torque compensation allocation information based on the wheel hub motor fault information and the direct yaw moment includes:

[0036] Determining the fault type in the hub motor fault information;

[0037] If the fault type is the single motor fault, the motor coaxial with the faulty wheel hub motor and the motor on the same side as the faulty wheel hub motor are determined as the target compensation wheel hub motor, and the direct yaw moment is evenly distributed to the target compensation wheel hub motor to obtain the torque compensation parameter of the target compensation wheel hub motor;

[0038] If the fault type is a dual-motor fault on different sides of different shafts, a normal motor is determined as a target compensation hub motor, and the direct yaw moment is evenly distributed to the target compensation hub motor to obtain a torque compensation parameter of the target compensation hub motor;

[0039] If the fault type is the coaxial dual motor fault or the same-side dual motor fault, the target compensating hub motor is determined according to the fault level of the faulty hub motor, and the direct yaw torque is evenly distributed to the target compensating hub motor to obtain the torque compensation parameter of the target compensating hub motor.

[0040] In a second aspect of the embodiments of the present invention, a distributed drive electric vehicle fault limp homeostasis enhancement control system is provided, comprising:

[0041] an acquisition unit, configured to acquire current vehicle parameters and hub motor fault information of the distributed drive electric vehicle when it is detected that the longitudinal speed of the distributed drive electric vehicle is less than a preset speed; wherein the hub motor fault information includes a fault type, a fault level, and a position of the faulty hub motor;

[0042] A first calculation unit is used to calculate the current vehicle parameters based on a preset expected state estimation model to obtain a vehicle state control target; wherein the vehicle state control target includes an expected yaw angle and an expected sideslip angle of the center of mass;

[0043] A second calculation unit, configured to calculate the current vehicle parameters and the vehicle state control target based on a preset optimization control model to obtain a direct yaw moment and an active front wheel steering angle;

[0044] A first determining unit, configured to determine an initial torque adjustment parameter corresponding to the hub motor fault information;

[0045] a second determining unit, configured to determine torque compensation allocation information based on the wheel hub motor fault information and the direct yaw moment; wherein the torque compensation allocation information includes a target compensation wheel hub and a torque compensation parameter of the target compensation wheel hub;

[0046] an integration unit, configured to integrate the initial torque adjustment parameter and the torque compensation allocation information to obtain target torque adjustment information; wherein the target torque adjustment information includes the target compensation hub, the torque compensation parameter of the target compensation hub, the target adjustment hub, and the torque adjustment parameter of the target adjustment hub; the target compensation hub and the target adjustment hub are different;

[0047] The third determining unit is used to determine the target torque adjustment information and the active front wheel steering angle as current control information of the distributed drive electric vehicle.

[0048] In a third aspect of an embodiment of the present invention, a computing device is provided, comprising: at least one processor, a memory, and an input-output unit; wherein the memory is used to store a computer program, and the processor is used to call the computer program stored in the memory to execute any one of the methods described in the first aspect.

[0049] In a fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided, which includes instructions, and when the instructions are executed on a computer, the computer executes the method described in any one of the first aspects.

[0050] In a fifth aspect of the embodiments of the present invention, a computer program product is provided, comprising a computer program, which implements any one of the methods in the first aspect when executed by a processor.

[0051] According to the distributed drive electric vehicle fault limp maneuver enhancement control method and system according to the embodiment of the present invention, when it is detected that the longitudinal speed of the vehicle is less than the preset speed, the current vehicle parameters and the hub motor fault information can be obtained, so that the hub motor can be controlled in time, and by calculating the current vehicle parameters, the direct yaw torque for the vehicle hub and the active front wheel angle for adjusting the front wheels of the vehicle can be obtained, and then the torque compensation allocation information can be obtained according to the hub motor fault information and the direct yaw torque. In addition, the initial torque adjustment parameters corresponding to the hub motor fault information can be determined, and the torque compensation allocation information and the initial torque adjustment parameters can be integrated to obtain the target torque adjustment information. The target torque adjustment information can take into account the impact of the torque adjustment on the vehicle driving in a more comprehensive and systematic manner, thereby improving the vehicle driving stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, in which:

[0053] Figure 1 A schematic flow chart of a method for enhancing control of a fault limp homeostasis of a distributed drive electric vehicle provided by an embodiment of the present invention;

[0054] Figure 2 A schematic diagram of a limited time domain rolling optimization strategy provided by an embodiment of the present invention;

[0055] Figure 3 A schematic diagram of the state quantity error weight adjustment effect provided by an embodiment of the present invention;

[0056] Figure 4 A schematic diagram of a method for handling a single motor failure and a dual motor failure on different sides of different shafts provided in an embodiment of the present invention;

[0057] Figure 5 A schematic diagram of a processing method when a coaxial dual motor fails and at least one motor is in a primary fault according to an embodiment of the present invention;

[0058] Figure 6 A schematic diagram of a processing method when a coaxial dual motor fails and both failed motors are in a secondary failure state provided by an embodiment of the present invention;

[0059] Figure 7 A schematic diagram of a processing method when dual motors on the same side fail and at least one motor is in a primary failure, provided by an embodiment of the present invention;

[0060] Figure 8 A schematic diagram of a dual motor failure on the same side and both faulty motors are in a secondary fault state provided by an embodiment of the present invention;

[0061] Fig. 9 A schematic diagram of the control result of AMPC with feedback under FL fault provided by one embodiment of the present invention;

[0062] Fig.10 A schematic diagram of torque request after a FL hub motor failure provided by an embodiment of the present invention;

[0063] Fig.11 A schematic diagram of the yaw angle of a double lane change test under FL / RL drive failure provided by an embodiment of the present invention;

[0064] Fig.12 A schematic diagram of torque compensation for double lane shifting test under FL / RL drive failure provided by an embodiment of the present invention;

[0065] Fig.13 A schematic diagram of the structure of a distributed drive electric vehicle fault limp home maneuver enhancement control system provided by an embodiment of the present invention;

[0066] Fig.14 A schematic diagram of the structure of a medium according to an embodiment of the present invention is schematically shown;

[0067] Fig.15 A schematic diagram of the structure of a computing device according to an embodiment of the present invention is schematically shown.

[0068] In the drawings, the same or corresponding reference numerals represent the same or corresponding parts. DETAILED DESCRIPTION

[0069] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0070] Those skilled in the art know that the embodiments of the present invention can be implemented as a system, device, apparatus, method or computer program product. Therefore, the present disclosure can be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0071] According to an embodiment of the present invention, a method and system for enhanced control of fault limp homeostasis of a distributed drive electric vehicle are proposed.

[0072] It should be noted that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction and does not have any limiting meaning.

[0073] The principle and spirit of the present invention are explained in detail below with reference to several representative embodiments of the present invention.

[0074] Exemplary Methods

[0075] Reference below Figure 1 , Figure 1 This is a flow chart of a method for enhancing control of a fault limp homeostasis of a distributed drive electric vehicle provided by an embodiment of the present invention. It should be noted that the embodiments of the present invention can be applied to any applicable scenario.

[0076] Figure 1 The process of the distributed drive electric vehicle fault limp maneuver enhancement control method provided by one embodiment of the present invention includes:

[0077] Step S101, when it is detected that the longitudinal speed of the distributed drive electric vehicle is less than a preset speed, current vehicle parameters of the distributed drive electric vehicle and hub motor fault information are obtained.

[0078] In the embodiment of the present invention, the hub motor fault information includes the fault type, fault level and fault hub motor position; the fault type includes single motor fault Single_MotorFTC, coaxial dual motor fault Dbl2w_MototFTC, same side dual motor fault Dbli_MototFTC, different shafts and different sides dual motor fault DblX_MototFTC. In addition, the fault type may also include three or four motor faults Stop. The fault level may be divided into primary fault, secondary fault, etc.

[0079] In the embodiment of the present invention, the preset speed may be 100 kph. Determining the vehicle parameters may include the actual longitudinal speed of the vehicle and the vehicle parameters.

[0080] Step S102, calculating the current vehicle parameters based on a preset desired state estimation model to obtain a vehicle state control target.

[0081] In the embodiment of the present invention, the vehicle state control target includes a desired yaw angle and a desired sideslip angle of the center of mass.

[0082] As an optional implementation, step S102 calculates the current vehicle parameters based on a preset desired state estimation model, and the method of obtaining the vehicle state control target may include:

[0083] Obtaining the current longitudinal speed of the vehicle from the current vehicle parameters;

[0084] Calculating a yaw angle error weight based on the current longitudinal speed of the vehicle;

[0085] Calculate an initial desired yaw angle and a maximum yaw angle based on the current vehicle parameters;

[0086] Calculating a desired yaw angle based on the initial desired yaw angle, the maximum yaw angle and current vehicle parameters;

[0087] The desired yaw angle and a preset desired sideslip angle of the center of mass are determined as vehicle state control targets.

[0088] Among them, by implementing this embodiment, the expected yaw angle can be adjusted according to the actual driving state of the vehicle, so that the obtained expected yaw angle and expected sideslip angle of the center of mass better match the driving state of the vehicle.

[0089] In the embodiment of the present invention, the formula for the yaw angle error weight is:

[0090]

[0091] in, represents the yaw angle error weight, Indicates the current longitudinal speed of the vehicle.

[0092] Center of mass sideslip angle error weight The calculation formula can be:

[0093]

[0094] In the embodiment of the present invention, based on the initial desired yaw angle, the maximum yaw angle and the current vehicle parameters, the calculation formula for calculating the desired yaw angle is:

[0095]

[0096] in, represents the desired yaw angle, represents the initial desired yaw angle, represents the maximum value of the yaw angle, and sign() represents the sign function.

[0097] And the method of calculating the initial expected yaw angle and the maximum yaw angle based on the current vehicle parameters may include:

[0098] Calculating an initial desired yaw angle based on the current longitudinal speed of the vehicle and the vehicle wheelbase, design stability factor, and front wheel turning angle among the current vehicle parameters;

[0099] The maximum yaw angle value is calculated based on the current longitudinal speed of the vehicle and the lateral acceleration in the current vehicle parameters.

[0100] In the embodiment of the present invention, the initial desired yaw angle and the maximum yaw angle The calculation formula can be:

[0101]

[0102]

[0103] in, represents the current longitudinal speed of the vehicle, L represents the wheelbase of the vehicle, represents the design stability factor, represents the front wheel turning angle, represents the lateral acceleration.

[0104] In the embodiment of the present invention, the expected sideslip angle of the center of mass is limited in its observation method, the subsequent optimization control model has poor tracking accuracy, and the vehicle emphasizes stable driving when it fails, so the expected sideslip angle of the center of mass can be set to 0.

[0105] Step S103, calculating the current vehicle parameters and the vehicle state control target based on a preset optimization control model to obtain a direct yaw moment and an active front wheel steering angle.

[0106] In the embodiment of the present invention, the preset optimization control model is applicable to the multi-objective optimization problem of discrete systems. Figure 2 The sampling time of the optimization control model is T, and the control time domain N=n×T is defined, and the prediction time domain P=p×T. Within P, the system state at the starting time is used to establish a time-invariant discretized optimization control model. The system state quantity can be expressed by the result obtained by calculating the incremental sequence of the system control variables through the instantaneous time-invariant discretized optimization control model.

[0107] When N is equal to P, there are n unknown increment sequences. When n is less than p, the increment sequence consists of n different constant sequences with (pn) values ​​equal to the nth increment. The control target and system state are converted into the standard quadratic programming cost function form, and the system state is represented by p groups of sequences. The standard quadratic programming form is combined with equality and inequality constraints to solve, and the first element in the calculated increment sequence is superimposed with the control amount of the previous moment to form the control amount output at this moment. The next control moment is deduced in this way.

[0108] The optimized control model is improved by using a simplified three-degree-of-freedom vehicle dynamics model, adding direct yaw moment based on the front wheel angle As the vehicle dynamics control quantity, the state quantity is the vehicle center of mass side slip angle β and the vehicle yaw angle γ. The prediction model state space equation is shown as follows:

[0109]

[0110] in, , is the vehicle front and rear wheel cornering stiffness; , is the front and rear wheelbase; m is the vehicle mass, is the vehicle yaw moment of inertia; is the longitudinal speed of the vehicle.

[0111] In the embodiment of the present invention, in order to solve the problem of enhancing the limp maneuverability under various vehicle operating conditions, an optimization control adaptive method is adopted to improve the control performance. The vehicle optimization control model is updated in real time before each optimization calculation starts according to the signal of the vehicle longitudinal speed, and the updated optimization control model is discretized into a differential state space equation in combination with the sampling time Ts through the forward Euler method as shown in the following formula. The differential state space equation is recursively deduced in Np steps, and the state evolution in the entire prediction time domain can be expressed as a function of the current state and the future control sequence.

[0112]

[0113]

[0114]

[0115] And, define the cost function J, whose goal is to minimize the state deviation and control increment in the prediction time domain. The form of the cost function J is:

[0116]

[0117] in, is the state variable; The vehicle state control target calculated by the driver's expected state estimation layer; is the increment of the control quantity.

[0118] The cost function J is expressed as a standard quadratic programming problem. The state evolution in the prediction domain is expressed in matrix form:

[0119]

[0120] in, is the state sequence at future moments; is the control sequence for the future moment (i.e., direct yaw torque and active front wheel steering angle); F and G are the state matrices of the system and the input matrix The derived prediction matrix.

[0121] Among them, the matrix F is used to represent the impact of the initial state on the future state sequence. Assume that the prediction time domain length is , then the matrix F is in the following form:

[0122]

[0123] The matrix G is used to represent the influence of the control sequence on the future state sequence. The influence of the control sequence can be recursively accumulated to the future state, so the structure of the matrix G is a lower triangular matrix, and each row represents the contribution of a certain control quantity at the current moment to the future state, in the following form:

[0124]

[0125] Therefore, the cost function J can be rewritten as:

[0126]

[0127] in, is the state deviation weight matrix in the prediction time domain, is the weight matrix for the control amount increment, It is the target state at a future moment.

[0128] Finally, the finite horizon regressive optimization control problem can be transformed into a standard quadratic programming problem:

[0129]

[0130] st

[0131] in, is the coefficient matrix of the quadratic terms; is the coefficient of the first-order term. C and d are constraints. Under different fault forms, the available control range of the control system changes, and the coefficient matrix and constraints need to be dynamically adjusted according to the fault form. The adjustment effect is as follows: Figure 3 shown.

[0132] Step S104, determining an initial torque adjustment parameter corresponding to the hub motor fault information.

[0133] In the embodiment of the present invention, different initial torque adjustment parameters may be determined for different fault types.

[0134] Specifically, when a single motor fails, the coaxial opposite motor is used for torque fault-tolerant control, and its output torque is the lowest value of the two-wheel torque as the initial torque adjustment parameter (i.e., torque low selection).

[0135] When a coaxial dual-motor fault occurs, the faulty axis performs torque low selection to obtain the initial torque adjustment parameters, and the normal axis does not adopt fault-tolerant control.

[0136] When a dual motor failure occurs on the same side, the coaxial torque is low selected to obtain the initial torque adjustment parameters.

[0137] When dual motor failures occur on different sides of different shafts, the torque demand of the control module is downgraded to obtain the initial torque adjustment parameters, and active fault-tolerant torque compensation can be performed through torque arbitration afterwards.

[0138] When three or four motors fail, a global torque low selection is adopted to obtain the initial torque adjustment parameters, and subsequent torque compensation or direct limp stop is performed.

[0139] Step S105: determining torque compensation distribution information based on the hub motor fault information and the direct yaw moment.

[0140] In the embodiment of the present invention, the torque compensation allocation information includes a target compensation hub motor and a torque compensation parameter of the target compensation hub motor.

[0141] As an optional implementation manner, in step S105, the method of determining the torque compensation distribution information based on the hub motor fault information and the direct yaw moment may include:

[0142] Determining the fault type in the hub motor fault information;

[0143] If the fault type is the single motor fault, the motor coaxial with the faulty wheel hub motor and the motor on the same side as the faulty wheel hub motor are determined as the target compensation wheel hub motor, and the direct yaw moment is evenly distributed to the target compensation wheel hub motor to obtain the torque compensation parameter of the target compensation wheel hub motor;

[0144] If the fault type is a dual-motor fault on different sides of different shafts, a normal motor is determined as a target compensation hub motor, and the direct yaw moment is evenly distributed to the target compensation hub motor to obtain a torque compensation parameter of the target compensation hub motor;

[0145] If the fault type is the coaxial dual motor fault or the same-side dual motor fault, the target compensating hub motor is determined according to the fault level of the faulty hub motor, and the direct yaw torque is evenly distributed to the target compensating hub motor to obtain the torque compensation parameter of the target compensating hub motor.

[0146] Among them, by implementing this implementation method, different calculation methods can be adopted according to different fault types to obtain the target compensation hub motor and the target compensation hub motor's torque compensation parameters, so that the obtained target compensation hub motor and the target compensation hub motor's torque compensation parameters can better control the vehicle's driving state.

[0147] Please also read Figure 4~Figure 8 , where the diagonal line box represents the faulty motor; the gray block represents the target compensation hub motor; the white block is the normal motor, which inherits the original torque demand (i.e., the initial torque adjustment parameter). FL represents the left front motor, FR represents the right front motor, RL represents the left rear motor, and RR represents the right rear motor. DrgLv1 represents a primary fault, and DrgLv2 represents a secondary fault.

[0148] Figure 4 It represents the handling method of single motor failure and dual motor failure on different sides of different shafts. The torque compensation parameters are applied to the target compensated hub motor and the initial torque adjustment parameters are applied to the normal motor.

[0149] For coaxial dual motor failure and same-side dual motor failure, when the vehicle is in the above two types of failures, it needs to be controlled separately according to the fault level.

[0150] Figure 5Indicates the processing method when the coaxial dual motors fail and at least one motor is in a first-level fault. If FL fails in the first level, the torque compensation parameters will be applied to the FL and RR motors. The same applies to RL and RR failures and at least one motor is in a first-level fault.

[0151] Figure 6 Indicates the processing method when the coaxial dual motors are faulty and both faulty motors are in the second-level fault state, and the torque compensation parameters are applied to RL and RR. The same applies when both RL and RR have a second-level fault.

[0152] Figure 7 Indicates the handling method when two motors on the same side fail and at least one motor is in the first-level failure. If the FL motor fails in the first level, the torque compensation parameters will be applied to the FL and RR motors. The same applies when FR and RR fail at the same time and the FR motor fails in the first level.

[0153] Figure 8 Indicates that the two motors on the same side are faulty and both faulty motors are in secondary fault. In this case, no torque compensation parameters are applied and the torque compensation is reduced. Active front wheel steering weight in the matrix.

[0154] In the embodiment of the present invention, when three or four motors fail, the torque compensation parameter is not applied, the torque demand is calculated according to the rule-based fault-tolerant control, and the active front wheel steering is still turned on.

[0155] Step S106: Integrate the initial torque adjustment parameter and the torque compensation allocation information to obtain target torque adjustment information.

[0156] In an embodiment of the present invention, the target torque adjustment information includes the target compensation hub motor, the torque compensation parameter of the target compensation hub motor, the target adjustment hub motor and the torque adjustment parameter of the target adjustment hub motor; the target compensation hub motor and the target adjustment hub motor are different.

[0157] Step S107: determining the target torque adjustment information and the active front wheel steering angle as current control information of the distributed drive electric vehicle.

[0158] In the embodiment of the present invention, the active front wheel steering angle directly superimposes the steering request on the original steering demand of the wire control steering system through the VCU.

[0159] Please also read Figure 9~Figure 12 This solution is built and debugged using the dSPACE MicroAutoBox II rapid prototyping controller platform. Two typical working conditions are used to test the effectiveness of the solution.

[0160] Test 1: Under straight-line driving conditions, a single motor fault Single_MotorFTC occurs, and the faulty motor is the left front FL. The vehicle yaw angle without the control strategy of this solution is as follows Fig. 9 As shown in the orange line, the conventional model predictive control is turned on. Fig. 9 As shown by the purple line. When the adaptive variable weight finite horizon rolling optimization strategy proposed in this scheme is used, Fig. 9 As shown by the red line, under the straight line condition, the expected yaw angle is 0, such as Fig. 9 As shown by the blue line. It can be seen that when a fault occurs, if no measures are taken, the vehicle yaw angle increases greatly and the vehicle deviates. If conventional model predictive control is used, the vehicle yaw angle begins to approach the target value after 2 seconds, but there is a steady-state error, which effectively suppresses the deviation, but the deviation still exists. When the strategy of this scheme is used, the vehicle yaw angle converges to the target value quickly, and the steady-state error is negligible. The differential torque compensation calculated by this scheme is shown in Fig.10 As shown, FR and RR motors are used for differential torque compensation.

[0161] Test 2: 70kph double lane change condition, dual motor failure Dbl2w_MototFTC on the same side, the faulty motors are the left front FL and the left rear RL. The vehicle's yaw angle control results are as follows: Fig.11 As shown in the figure, it can be obtained that after the vehicle fails, the driver's desired yaw angle can still be tracked efficiently and quickly. The differential torque compensation result is as follows: Fig.12 shown.

[0162] Compared with traditional fault-tolerant control strategies, this solution utilizes the multi-actuator characteristics of distributed drive electric vehicles to compensate for the front wheel angle under fault conditions, and decouples degradation control from torque compensation. When the motor is in a fault, differential torque can still be output through the residual torque capacity of the fault, so as to achieve multi-objective optimization control of the yaw angle and the sideslip angle of the center of mass, and maximize the handling stability of such vehicles. Traditional fault-tolerant control strategies can only achieve non-slip conditions in straight-line driving by degrading and limiting the motor torque, and even give up the advantage of active steering control of wire-controlled vehicles.

[0163] Compared with the traditional model predictive controller, the adaptive variable weight finite horizon rolling optimization strategy proposed in this scheme adaptively adjusts the system prediction model and effectively solves the control steady-state error problem under vehicle drive fault through the weight of the fault form decision cost function, further improving the control performance of this scheme.

[0164] This new fault-tolerant control scheme introduces a higher-performance control scheme for drive failures in distributed drive electric vehicles, which can effectively solve practical problems. Even when optimal control is not available, the risk of vehicle drive failure instability can still be reduced through the integrated rule-based fault-tolerant control strategy.

[0165] The present invention can obtain the current vehicle parameters and wheel hub motor fault information when detecting that the longitudinal speed of the vehicle is less than the preset speed, so that the wheel hub motor can be controlled in time, and by calculating the current vehicle parameters, the direct yaw moment for the vehicle wheel hub and the active front wheel angle adjusted for the front wheel of the vehicle can be obtained, and then the torque compensation distribution information can be obtained according to the wheel hub motor fault information and the direct yaw moment. In addition, the initial torque adjustment parameter corresponding to the wheel hub motor fault information can be determined, and the torque compensation distribution information and the initial torque adjustment parameter are integrated to obtain the target torque adjustment information, which can more comprehensively and systematically consider the impact of torque adjustment on vehicle driving, thereby improving the stability of vehicle driving. In addition, the present invention can also make the obtained expected yaw angle and expected center of mass sideslip angle more matched with the driving state of the vehicle. In addition, the present invention can also make the obtained target compensation wheel hub motor and the torque compensation parameters of the target compensation wheel hub motor better control the driving state of the vehicle.

[0166] Exemplary Systems

[0167] After introducing the method of the exemplary embodiment of the present invention, next, refer to Fig.13 A distributed drive electric vehicle fault limp home maneuvering enhancement control system according to an exemplary embodiment of the present invention is described, and the system includes:

[0168] The acquisition unit 1301 is used to acquire the current vehicle parameters and hub motor fault information of the distributed drive electric vehicle when it is detected that the longitudinal speed of the distributed drive electric vehicle is less than a preset speed; wherein the hub motor fault information includes the fault type, fault level and fault hub motor position; the fault types include single motor fault, coaxial dual motor fault, same-side dual motor fault and dual motor fault on different sides of different shafts.

[0169] The first calculation unit 1302 is used to calculate the current vehicle parameters based on a preset desired state estimation model to obtain a vehicle state control target; wherein the vehicle state control target includes a desired yaw angle and a desired sideslip angle of the center of mass;

[0170] A second calculation unit 1303 is used to calculate the current vehicle parameters and the vehicle state control target based on a preset optimization control model to obtain a direct yaw moment and an active front wheel steering angle;

[0171] A first determining unit 1304 is used to determine an initial torque adjustment parameter corresponding to the hub motor fault information;

[0172] A second determining unit 1305 is configured to determine torque compensation allocation information based on the wheel hub motor fault information and the direct yaw moment; wherein the torque compensation allocation information includes a target compensation wheel hub and a torque compensation parameter of the target compensation wheel hub;

[0173] an integration unit 1306, configured to integrate the initial torque adjustment parameter and the torque compensation allocation information to obtain target torque adjustment information; wherein the target torque adjustment information includes the target compensation hub, the torque compensation parameter of the target compensation hub, the target adjustment hub, and the torque adjustment parameter of the target adjustment hub; the target compensation hub and the target adjustment hub are different;

[0174] The third determining unit 1307 is used to determine the target torque adjustment information and the active front wheel steering angle as current control information of the distributed drive electric vehicle.

[0175] As an optional implementation, the first calculation unit 1302 calculates the current vehicle parameters based on a preset desired state estimation model, and the vehicle state control target may be obtained in the following manner:

[0176] Obtaining the current longitudinal speed of the vehicle from the current vehicle parameters;

[0177] Calculating a yaw angle error weight based on the current longitudinal speed of the vehicle;

[0178] Calculate an initial desired yaw angle and a maximum yaw angle based on the current vehicle parameters;

[0179] Calculating a desired yaw angle based on the initial desired yaw angle, the maximum yaw angle and current vehicle parameters;

[0180] The desired yaw angle and a preset desired sideslip angle of the center of mass are determined as vehicle state control targets.

[0181] Among them, by implementing this embodiment, the expected yaw angle can be adjusted according to the actual driving state of the vehicle, so that the obtained expected yaw angle and expected sideslip angle of the center of mass better match the driving state of the vehicle.

[0182] In the embodiment of the present invention, the formula for the yaw angle error weight is:

[0183]

[0184] in, represents the yaw angle error weight, Indicates the current longitudinal speed of the vehicle.

[0185] In the embodiment of the present invention, the first calculation unit 1302 calculates the initial expected yaw angle and the maximum yaw angle based on the current vehicle parameters in the following manner:

[0186] Calculating an initial desired yaw angle based on the current longitudinal speed of the vehicle and the vehicle wheelbase, design stability factor, and front wheel turning angle among the current vehicle parameters;

[0187] The maximum yaw angle value is calculated based on the current longitudinal speed of the vehicle and the lateral acceleration in the current vehicle parameters.

[0188] In the embodiment of the present invention, the calculation formula of the initial desired yaw angle is:

[0189]

[0190] in, represents the initial desired yaw angle, represents the current longitudinal speed of the vehicle, L represents the wheelbase of the vehicle, represents the design stability factor, represents the front wheel turning angle.

[0191] In the embodiment of the present invention, the calculation formula of the maximum yaw angle is:

[0192]

[0193] in, represents the maximum yaw angle, represents the current longitudinal speed of the vehicle, represents the lateral acceleration.

[0194] In the embodiment of the present invention, based on the initial desired yaw angle, the maximum yaw angle and the current vehicle parameters, the calculation formula for calculating the desired yaw angle is:

[0195]

[0196] in, represents the desired yaw angle, represents the initial desired yaw angle, represents the maximum value of the yaw angle, and sign() represents the sign function.

[0197] As an optional implementation manner, the second determining unit 1305 determines the torque compensation distribution information based on the hub motor fault information and the direct yaw moment in the following manner:

[0198] Determining the fault type in the hub motor fault information;

[0199] If the fault type is the single motor fault, the motor coaxial with the faulty wheel hub motor and the motor on the same side as the faulty wheel hub motor are determined as the target compensation wheel hub motor, and the direct yaw moment is evenly distributed to the target compensation wheel hub motor to obtain the torque compensation parameter of the target compensation wheel hub motor;

[0200] If the fault type is a dual-motor fault on different sides of different shafts, a normal motor is determined as a target compensation hub motor, and the direct yaw moment is evenly distributed to the target compensation hub motor to obtain a torque compensation parameter of the target compensation hub motor;

[0201] If the fault type is the coaxial dual motor fault or the same-side dual motor fault, the target compensating hub motor is determined according to the fault level of the faulty hub motor, and the direct yaw torque is evenly distributed to the target compensating hub motor to obtain the torque compensation parameter of the target compensating hub motor.

[0202] Among them, by implementing this implementation method, different calculation methods can be adopted according to different fault types to obtain the target compensation hub motor and the target compensation hub motor's torque compensation parameters, so that the obtained target compensation hub motor and the target compensation hub motor's torque compensation parameters can better control the vehicle's driving state.

[0203] The present invention can obtain the current vehicle parameters and wheel hub motor fault information when detecting that the longitudinal speed of the vehicle is less than the preset speed, so that the wheel hub motor can be controlled in time, and by calculating the current vehicle parameters, the direct yaw moment for the vehicle wheel hub and the active front wheel angle adjusted for the front wheel of the vehicle can be obtained, and then the torque compensation distribution information can be obtained according to the wheel hub motor fault information and the direct yaw moment. In addition, the initial torque adjustment parameter corresponding to the wheel hub motor fault information can be determined, and the torque compensation distribution information and the initial torque adjustment parameter are integrated to obtain the target torque adjustment information, which can more comprehensively and systematically consider the impact of torque adjustment on vehicle driving, thereby improving the stability of vehicle driving. In addition, the present invention can also make the obtained expected yaw angle and expected center of mass sideslip angle more matched with the driving state of the vehicle. In addition, the present invention can also make the obtained target compensation wheel hub motor and the torque compensation parameters of the target compensation wheel hub motor better control the driving state of the vehicle.

[0204] Exemplary Media

[0205] After introducing the method and system of the exemplary embodiment of the present invention, next, reference is made to Fig.14 For a description of a computer-readable storage medium according to an exemplary embodiment of the present invention, please refer to Fig.14 , the computer-readable storage medium shown is a CD 140, on which a computer program (i.e., a program product) is stored. When the computer program is executed by the processor, each step recorded in the above method implementation is implemented. For example, when it is detected that the longitudinal speed of the distributed drive electric vehicle is less than a preset speed, the current vehicle parameters and the hub motor fault information of the distributed drive electric vehicle are obtained; wherein the hub motor fault information includes the fault type, the fault level, and the position of the faulty hub motor; the current vehicle parameters are calculated based on a preset expected state estimation model to obtain a vehicle state control target; wherein the vehicle state control target includes an expected yaw angle and an expected sideslip angle of the center of mass; the current vehicle parameters and the vehicle state control target are calculated based on a preset optimization control model to obtain a direct yaw moment and an active front wheel steering angle; and the vehicle state control target is determined to be related to the vehicle state. The initial torque adjustment parameter corresponding to the hub motor fault information; based on the hub motor fault information and the direct yaw moment, determine the torque compensation allocation information; wherein the torque compensation allocation information includes the target compensation hub motor and the torque compensation parameter of the target compensation hub motor; integrate the initial torque adjustment parameter and the torque compensation allocation information to obtain the target torque adjustment information; wherein the target torque adjustment information includes the target compensation hub motor, the torque compensation parameter of the target compensation hub motor, the target adjustment hub motor and the torque adjustment parameter of the target adjustment hub motor; the target compensation hub motor and the target adjustment hub motor are different; determine the target torque adjustment information and the active front wheel angle as the current control information of the distributed drive electric vehicle; the specific implementation method of each step will not be repeated here.

[0206] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which are not listed here one by one.

[0207] Exemplary Computing Devices

[0208] After introducing the method, system and medium of the exemplary embodiment of the present invention, next, reference is made to Fig.15 A computing device for enhanced control of fault limp home maneuvers of a distributed drive electric vehicle according to an exemplary embodiment of the present invention.

[0209] Fig.15A block diagram of an exemplary computing device 150 suitable for implementing embodiments of the present invention is shown, and the computing device 150 may be a computer system or a server. Fig.15 The computing device 150 shown is merely an example and should not bring any limitation to the functionality and scope of use of embodiments of the present invention.

[0210] like Fig.15 As shown, the components of computing device 150 may include, but are not limited to: one or more processors or processing units 1501 , a system memory 1502 , and a bus 1503 connecting various system components (including system memory 1502 and processing unit 1501 ).

[0211] Computing device 150 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by computing device 150, including volatile and nonvolatile media, removable and non-removable media.

[0212] System memory 1502 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 15021 and / or cache memory 15022. Computing device 150 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, ROM 15023 may be used to read and write non-removable, non-volatile magnetic media ( Fig.15 is not shown in the Fig.15 As shown in FIG. 15 , a disk drive for reading and writing a removable non-volatile disk (e.g., a “floppy disk”) and an optical disk drive for reading and writing a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 1503 via one or more data medium interfaces. System memory 1502 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of various embodiments of the present invention.

[0213] A program / utility 15025 having a set (at least one) of program modules 15024 may be stored, for example, in system memory 1502, and such program modules 15024 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. Program modules 15024 generally perform the functions and / or methods of the embodiments described herein.

[0214] The computing device 150 may also communicate with one or more external devices 1504 (e.g., a keyboard, a pointing device, a display, etc.). Such communication may be performed through an input / output (I / O) interface 1505. In addition, the computing device 150 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) through a network adapter 1506. Fig.15 As shown, the network adapter 1506 communicates with other modules (such as the processing unit 1501, etc.) of the computing device 150 via the bus 1503. It should be understood that although Fig.15 Not shown, other hardware and / or software modules may be used in conjunction with computing device 150 .

[0215] The processing unit 1501 executes various functional applications and data processing by running the program stored in the system memory 1502. For example, when it is detected that the longitudinal speed of the distributed drive electric vehicle is less than a preset speed, the current vehicle parameters and hub motor fault information of the distributed drive electric vehicle are obtained; wherein the hub motor fault information includes the fault type, fault level and fault hub motor position; the current vehicle parameters are calculated based on a preset expected state estimation model to obtain a vehicle state control target; wherein the vehicle state control target includes an expected yaw angle and an expected center of mass sideslip angle; the current vehicle parameters and the vehicle state control target are calculated based on a preset optimization control model to obtain a direct yaw moment and an active front wheel turning angle; and the wheel hub motor is determined. The initial torque adjustment parameter corresponding to the motor fault information; based on the hub motor fault information and the direct yaw moment, determine the torque compensation allocation information; wherein the torque compensation allocation information includes the target compensation hub motor and the torque compensation parameter of the target compensation hub motor; integrate the initial torque adjustment parameter and the torque compensation allocation information to obtain the target torque adjustment information; wherein the target torque adjustment information includes the target compensation hub motor, the torque compensation parameter of the target compensation hub motor, the target adjustment hub motor and the torque adjustment parameter of the target adjustment hub motor; the target compensation hub motor and the target adjustment hub motor are different; determine the target torque adjustment information and the active front wheel angle as the current control information of the distributed drive electric vehicle. The specific implementation of each step is not repeated here. It should be noted that although several units / modules or sub-units / sub-modules of the distributed drive electric vehicle fault limp maneuver enhancement control system are mentioned in the above detailed description, this division is only exemplary and not mandatory. In fact, according to an embodiment of the present invention, the features and functions of two or more units / modules described above can be concretized in one unit / module. On the contrary, the features and functions of one unit / module described above may be further divided into multiple units / modules to be embodied.

[0216] In the description of the present invention, it should be noted that the terms “first”, “second” and “third” are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0217] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0218] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. The system embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of the system or unit can be electrical, mechanical or other forms.

[0219] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0220] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0221] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that can be executed by a processor. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., various media that can store program codes.

[0222] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

[0223] In addition, although the operations of the method of the present invention are described in a specific order in the drawings, this does not require or imply that the operations must be performed in this specific order, or that all the operations shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0224] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

Claims

1. A distributed drive electric vehicle fault limp maneuvering enhancement control method, characterized in that: The method comprises: When it is detected that the longitudinal speed of the distributed drive electric vehicle is less than a preset speed, the current vehicle parameters of the distributed drive electric vehicle and the hub motor fault information are obtained; wherein the hub motor fault information includes the fault type, fault level and the position of the faulty hub motor; The current vehicle parameters are calculated based on a preset desired state estimation model to obtain a vehicle state control target; wherein the vehicle state control target includes a desired yaw angle and a desired sideslip angle of the center of mass; Calculating the current vehicle parameters and the vehicle state control target based on a preset optimization control model to obtain a direct yaw moment and an active front wheel steering angle; Determining an initial torque adjustment parameter corresponding to the hub motor fault information; Based on the wheel hub motor fault information and the direct yaw moment, torque compensation allocation information is determined; wherein the torque compensation allocation information includes a target compensation wheel hub motor and a torque compensation parameter of the target compensation wheel hub motor; Integrate the initial torque adjustment parameter and the torque compensation allocation information to obtain target torque adjustment information; wherein the target torque adjustment information includes the target compensation hub motor, the torque compensation parameter of the target compensation hub motor, the target adjustment hub motor and the torque adjustment parameter of the target adjustment hub motor; the target compensation hub motor and the target adjustment hub motor are different; Determining the target torque adjustment information and the active front wheel steering angle as current control information of the distributed drive electric vehicle; The current vehicle parameters are calculated based on a preset desired state estimation model to obtain a vehicle state control target, including: Obtaining the current longitudinal speed of the vehicle from the current vehicle parameters; Calculating a yaw angle error weight based on the current longitudinal speed of the vehicle; Calculate an initial desired yaw angle and a maximum yaw angle based on the current vehicle parameters; Calculating a desired yaw angle based on the initial desired yaw angle, the maximum yaw angle and current vehicle parameters; The desired yaw angle and a preset desired sideslip angle of the center of mass are determined as vehicle state control targets.

2. The method for enhancing control of fault limp homeostasis of a distributed drive electric vehicle according to claim 1, characterized in that: The formula of the yaw angle error weight is: ； in, represents the yaw angle error weight, Indicates the current longitudinal speed of the vehicle.

3. The method for enhancing control of fault limp homeostasis of a distributed drive electric vehicle according to claim 2, characterized in that: The calculating and obtaining the initial desired yaw angle and the maximum yaw angle based on the current vehicle parameters includes: Calculating an initial desired yaw angle based on the current longitudinal speed of the vehicle and the vehicle wheelbase, design stability factor, and front wheel turning angle among the current vehicle parameters; The maximum yaw angle value is calculated based on the current longitudinal speed of the vehicle and the lateral acceleration in the current vehicle parameters.

4. The method for enhancing control of fault limp homeostasis of a distributed drive electric vehicle according to claim 3, characterized in that: The calculation formula for calculating the initial expected yaw angle based on the current vehicle longitudinal speed and the distance in the current vehicle parameters is: ； in, represents the initial desired yaw angle, represents the current longitudinal speed of the vehicle, L represents the wheelbase of the vehicle, represents the design stability factor, represents the front wheel turning angle.

5. The method for enhancing control of fault limp homeostasis of a distributed drive electric vehicle according to claim 4, characterized in that: The calculation formula for calculating the maximum yaw angle based on the current vehicle longitudinal speed and the acceleration in the current vehicle parameters is: ； in, represents the maximum yaw angle, represents the current longitudinal speed of the vehicle, represents the lateral acceleration.

6. The method for enhancing control of fault limp homeostasis of a distributed drive electric vehicle according to claim 5, characterized in that: The calculation formula for calculating the expected yaw angle based on the initial expected yaw angle, the maximum yaw angle and the current vehicle parameters is: ； in, represents the desired yaw angle, represents the initial desired yaw angle, represents the maximum value of the yaw angle, and sign() represents the sign function.

7. The method for enhancing control of fault limp homeostasis of a distributed drive electric vehicle according to any one of claims 1 to 6, characterized in that: The fault types include single motor fault, coaxial dual motor fault, same-side dual motor fault, and different-axis different-side dual motor fault.

8. The method for enhancing control of fault limp homeostasis of a distributed drive electric vehicle according to claim 7, characterized in that: The determining of torque compensation distribution information based on the wheel hub motor fault information and the direct yaw moment includes: Determining the fault type in the hub motor fault information; If the fault type is the single motor fault, the motor coaxial with the faulty wheel hub motor and the motor on the same side as the faulty wheel hub motor are determined as the target compensation wheel hub motor, and the direct yaw moment is evenly distributed to the target compensation wheel hub motor to obtain the torque compensation parameter of the target compensation wheel hub motor; If the fault type is a dual-motor fault on different sides of different shafts, a normal motor is determined as a target compensation hub motor, and the direct yaw moment is evenly distributed to the target compensation hub motor to obtain a torque compensation parameter of the target compensation hub motor; If the fault type is the coaxial dual motor fault or the same-side dual motor fault, the target compensating hub motor is determined according to the fault level of the faulty hub motor, and the direct yaw torque is evenly distributed to the target compensating hub motor to obtain the torque compensation parameter of the target compensating hub motor.

9. A distributed drive electric vehicle fault limp maneuvering enhancement control system, characterized in that: The system comprises: an acquisition unit, configured to acquire current vehicle parameters and hub motor fault information of the distributed drive electric vehicle when it is detected that the longitudinal speed of the distributed drive electric vehicle is less than a preset speed; wherein the hub motor fault information includes a fault type, a fault level, and a position of the faulty hub motor; A first calculation unit is used to calculate the current vehicle parameters based on a preset expected state estimation model to obtain a vehicle state control target; wherein the vehicle state control target includes an expected yaw angle and an expected sideslip angle of the center of mass; A second calculation unit, configured to calculate the current vehicle parameters and the vehicle state control target based on a preset optimization control model to obtain a direct yaw moment and an active front wheel steering angle; A first determining unit, configured to determine an initial torque adjustment parameter corresponding to the hub motor fault information; a second determining unit, configured to determine torque compensation allocation information based on the wheel hub motor fault information and the direct yaw moment; wherein the torque compensation allocation information includes a target compensation wheel hub and a torque compensation parameter of the target compensation wheel hub; an integration unit, configured to integrate the initial torque adjustment parameter and the torque compensation allocation information to obtain target torque adjustment information; wherein the target torque adjustment information includes the target compensation hub, the torque compensation parameter of the target compensation hub, the target adjustment hub, and the torque adjustment parameter of the target adjustment hub; the target compensation hub and the target adjustment hub are different; a third determining unit, configured to determine the target torque adjustment information and the active front wheel steering angle as current control information of the distributed drive electric vehicle; The current vehicle parameters are calculated based on a preset desired state estimation model to obtain a vehicle state control target, including: Obtaining the current longitudinal speed of the vehicle from the current vehicle parameters; Calculating a yaw angle error weight based on the current longitudinal speed of the vehicle; Calculate an initial desired yaw angle and a maximum yaw angle based on the current vehicle parameters; Calculating a desired yaw angle based on the initial desired yaw angle, the maximum yaw angle and current vehicle parameters; The desired yaw angle and a preset desired sideslip angle of the center of mass are determined as vehicle state control targets.

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