Fault-tolerant actuator control method and apparatus for electric vehicle, storage medium, and vehicle

Abstract

The present application relates to the technical field of electric vehicles, and discloses a fault-tolerant actuator control method and apparatus for an electric vehicle, a storage medium, and a vehicle. The method comprises: acquiring vehicle-mounted sensor information and a reference trajectory of a distributed drive electric vehicle; using an MPC algorithm to perform trajectory tracking on the basis of the vehicle-mounted sensor information and the reference trajectory to obtain an expected front wheel steering angle; when at least one steering actuator is determined to be faulty on the basis of the vehicle-mounted sensor information, calculating a differential power moment on the basis of the expected front wheel steering angle and an actual front wheel steering angle corresponding to a previous moment by using a sliding mode control (SMC) algorithm; when at least one driving actuator is determined to be faulty on the basis of the vehicle-mounted sensor information, calculating a torque corresponding to the current moment on the basis of the differential power moment by using a distribution optimization algorithm based on minimum tire adhesion utilization; and controlling steering of the distributed drive electric vehicle on the basis of the torque corresponding to the current moment. The present application implements reliable control of the vehicle in a case where a steering actuator and a driving actuator fail at the same time.

Description

Electric vehicle actuator fault-tolerant control method, device, storage medium and vehicle Technical Field

[0001] The present application relates to the technical field of electric vehicles, and in particular to a fault-tolerant control method, device, storage medium, and vehicle for an actuator of an electric vehicle. Background Art

[0002] With growing awareness of environmental protection and the increasingly severe energy crisis, electric vehicles (EVs) have attracted widespread attention and attention as clean and efficient means of transportation. Distributed drive electric vehicles (DDEVs) offer a short drive chain, high transmission efficiency, fast motor response, and precise torque control, making them an ideal vehicle for electric vehicles. However, as an overdriven system, DDEVs are characterized by high system complexity, strong coupling between subsystems, and a relatively large number of actuators and sensors, significantly increasing the probability of failure in each subsystem. Therefore, research on fault-tolerant control of DDEVs is crucial for improving vehicle safety and driving stability.

[0003] Depending on the actuators in distributed electric vehicles, fault-tolerant control can be divided into fault-tolerant control for steer-by-wire failure and fault-tolerant control for drive motor failure. To address this issue, a hierarchical collaborative fault-tolerant control method combining differential steering and yaw torque has been proposed. While the upper-level controller calculates the desired front wheel angle and additional yaw torque, the lower-level controller optimizes the tire load factor using the active set method to distribute the output torque.

[0004] Fault-tolerant control for drive motor failure primarily refers to a control method that, when one or more drive motors in a drive-by-wire chassis system fail, appropriately redistributes drive to the remaining drive motors, ensuring that the vehicle maintains driving stability and safety. Fault-tolerant control for drive motor failure can generally be categorized as passive and active. Passive fault-tolerant control primarily focuses on controlling drive motor failures and typically targets specific failure modes. Specifically, when a corresponding vehicle failure occurs, it eliminates the need to know the system fault information and instead relies on pre-defined logic rules to ensure robustness to the failure. Prior art studies have investigated the path tracking problem of an electric vehicle with four electromechanical wheel systems under normal and fault conditions. Taking into account wheel slip constraints and certain actuator failures, a passive fault-tolerant controller based on variable structure control has been developed to maintain system stability and ensure acceptable tracking performance. However, for overdriven systems with multiple fault sources, passive fault-tolerant control strategies can address fewer fault types, making optimal fault-tolerant control difficult to achieve. Active fault-tolerant control, however, can adopt more appropriate control strategies based on different fault conditions, adjusting fault-tolerant controller parameters in real time. This proactively eliminates or mitigates motor faults, minimizing their impact on vehicle driving conditions and ensuring vehicle stability and safety. Existing technologies address the tracking control of four-wheel independent drive vehicles under actuator faults and disturbances by designing a composite observer to simultaneously estimate the drive system state and disturbances, reconstruct the fault efficiency factor, and adaptively adjust controller parameters, improving the robustness of tracking performance.

[0005] The above research primarily targets vehicle dynamics and stability, addressing fault-tolerant control for distributed electric vehicles (EVs) facing sensor failure, steering actuator failure, and drive actuator failure. However, existing research primarily considers single-actuator failures, lacking research on the coupled mechanisms and fault-tolerant control of multiple actuator failures. When multiple actuators fail simultaneously, fault-tolerant control applied only to a single actuator can lead to failure due to the coupling between actuators, severely threatening driving safety.

[0006] Summary of the Invention

[0007] This application provides a method, device, storage medium, and vehicle for fault-tolerant control of actuators in an electric vehicle, which are used to solve the problem of being unable to perform fault-tolerant control when multiple actuators fail simultaneously, threatening driving safety. The technical solution is as follows:

[0008] In a first aspect of the present application, a fault-tolerant control method for an actuator of an electric vehicle is provided, which is used in a distributed drive electric vehicle using front-wheel drive steering. The method comprises:

[0009] Acquiring onboard sensor information and a reference trajectory of the distributed drive electric vehicle;

[0010] Using a model predictive control (MPC) algorithm to track the vehicle-mounted sensor information and the reference trajectory to obtain a desired front wheel steering angle;

[0011] When at least one steering actuator fault is determined based on the vehicle-mounted sensor information, a sliding mode control (SMC) algorithm is used to calculate the desired front wheel steering angle and the actual front wheel steering angle at the previous moment to obtain a differential torque;

[0012] When at least one drive actuator fault is determined based on the vehicle-mounted sensor information, the differential torque is calculated based on an optimization allocation algorithm that minimizes tire adhesion utilization to obtain a current moment torque of each wheel;

[0013] The steering of the distributed drive electric vehicle is controlled according to the current moment torque.

[0014] In one possible implementation, the using an MPC algorithm to track the vehicle-mounted sensor information and the reference trajectory to obtain a desired front wheel steering angle includes:

[0015] Constructing an MPC trajectory tracking optimization objective function based on the vehicle-mounted sensor information and the reference trajectory;

[0016] Converting the MPC trajectory tracking optimization objective function into a quadratic function;

[0017] Solving the quadratic function using a quadratic programming method and a first constraint condition to obtain an optimal control increment sequence in a control time domain;

[0018] The first n control variables in the optimal control increment sequence are determined as the desired front wheel steering angle, where n is a positive integer.

[0019] In one possible implementation, the creating an MPC trajectory tracking optimization objective function based on the vehicle-mounted sensor information and the reference trajectory includes:

[0020] A state space expression is obtained by combining a vehicle dynamics model equation and a trajectory tracking model equation according to the vehicle-mounted sensor information and the reference trajectory;

[0021] Discretizing the parameters in the state space expression to obtain a discretized gain matrix equation;

[0022] Substituting the discretized gain matrix equation into the state space expression, the MPC trajectory tracking optimization objective function is obtained.

[0023] In one possible implementation, converting the MPC trajectory tracking optimization objective function into a quadratic function includes:

[0024] Predicting future states in a time domain based on the MPC trajectory tracking optimization objective function;

[0025] A quadratic function is generated according to the future state in the time domain.

[0026] In one possible implementation, the calculating of the desired front wheel steering angle and the actual front wheel steering angle at the previous moment by using the SMC algorithm to obtain the differential torque includes:

[0027] Constructing a dynamic model of the steering actuator, wherein the dynamic model includes an unknown differential torque;

[0028] constructing a sliding surface according to the desired front wheel steering angle and the actual front wheel steering angle;

[0029] Performing first-order differentiation on the sliding surface to obtain a differential equation;

[0030] The differential torque is calculated according to the dynamic model and the differential equation.

[0031] In one possible implementation, the optimization allocation algorithm based on minimizing tire adhesion utilization calculates the differential torque to obtain the current moment torque of each wheel, including:

[0032] Acquiring fault information of the drive actuator and constructing a failure mode according to the fault information;

[0033] Constructing an optimization objective function based on minimizing tire adhesion utilization according to the failure mode;

[0034] The optimization objective function is solved using a quadratic programming method and a second constraint condition to obtain the current moment torque of each wheel.

[0035] In a possible implementation, the second constraint condition is created based on the failure mode, the total driving torque requirement, the differential torque requirement, and the requirement that no additional yaw torque occurs in the effective solution.

[0036] In a second aspect of the present application, a fault-tolerant control device for an actuator of an electric vehicle is provided, which is used in a distributed drive electric vehicle using front-wheel drive steering, and the device comprises:

[0037] An acquisition module, configured to acquire on-board sensor information and a reference trajectory of the distributed drive electric vehicle;

[0038] A tracking module, configured to track the vehicle-mounted sensor information and the reference trajectory using a model predictive control (MPC) algorithm to obtain a desired front wheel steering angle;

[0039] a calculation module, configured to calculate the desired front wheel steering angle and the actual front wheel steering angle at a previous moment using a sliding mode control (SMC) algorithm to obtain a differential torque when at least one steering actuator fault is determined based on the vehicle-mounted sensor information;

[0040] The calculation module is further configured to calculate the differential torque based on an optimization allocation algorithm that minimizes tire adhesion utilization when at least one drive actuator fault is determined based on the vehicle-mounted sensor information, to obtain the current moment torque of each wheel;

[0041] A control module is used to control the steering of the distributed drive electric vehicle according to the torque at the current moment.

[0042] In a third aspect of the present application, a computer-readable storage medium is provided, wherein the storage medium stores at least one instruction, and the at least one instruction is loaded and executed by a processor to implement the above-mentioned actuator fault-tolerant control method for an electric vehicle.

[0043] In a fourth aspect of the present application, a distributed drive electric vehicle is provided, comprising a processor and a memory, wherein the memory stores at least one instruction, and the instruction is loaded and executed by the processor to implement the actuator fault-tolerant control method of the electric vehicle as described above.

[0044] The beneficial effects of the technical solution provided by this application include at least:

[0045] When at least one steering actuator fails, causing the vehicle to completely or partially lose its steering ability, the differential torque can be calculated based on the actual front wheel angle and the calculated expected front wheel angle. When at least one drive actuator fails, the drive torque of each wheel can be reasonably distributed based on the differential torque, so that the distributed drive electric vehicle has better tracking performance. The fault-tolerant control method with a hierarchical architecture can effectively improve the fault tolerance and robustness of the steering system, realize reliable control of the vehicle when the steering actuator and drive actuator fail at the same time, and improve driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0047] FIG1 is a flow chart of a fault-tolerant control method for an actuator of an electric vehicle provided by one embodiment of the present application;

[0048] FIG2 is a flow chart of a fault-tolerant control method for an actuator of an electric vehicle provided by one embodiment of the present application;

[0049] FIG3 is a schematic diagram of the planar motion of a vehicle provided by one embodiment of the present application;

[0050] FIG4 is a schematic diagram of a differential steering principle provided by an embodiment of the present application;

[0051] FIG5 is a schematic diagram of a fault-tolerant control method for an actuator of a distributed drive electric vehicle provided by one embodiment of the present application;

[0052] FIG6 is a trajectory diagram under uniform acceleration linear working conditions provided by one embodiment of the present application;

[0053] FIG7 is a longitudinal velocity diagram under uniform acceleration linear working conditions provided by one embodiment of the present application;

[0054] FIG8 is a diagram showing experimental results under a step-steering condition provided by one embodiment of the present application;

[0055] FIG9 is a diagram showing experimental results under a double lane shifting condition provided by an embodiment of the present application;

[0056] FIG10 is a torque distribution diagram provided by one embodiment of the present application;

[0057] FIG11 is a structural block diagram of an actuator fault-tolerant control device for an electric vehicle provided by one embodiment of the present application. DETAILED DESCRIPTION

[0058] In order to make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0059] As shown in FIG1 , it shows a method flow chart of a method for fault-tolerant control of an electric vehicle actuator provided by an embodiment of the present application. The method for fault-tolerant control of an electric vehicle actuator can be applied to a distributed drive electric vehicle using front-wheel drive steering. The method for fault-tolerant control of an electric vehicle actuator can include:

[0060] Step 101: Obtain onboard sensor information and a reference trajectory of a distributed drive electric vehicle.

[0061] Distributed drive electric vehicles are equipped with multiple on-board sensors, which collect information to obtain on-board sensor information.

[0062] The vehicle sensor information includes but is not limited to the following information: vehicle longitudinal speed, vehicle lateral speed, vehicle yaw rate, lateral stiffness of the vehicle's front and rear axles, front wheel angle, vehicle center of mass sideslip angle, vehicle moment of inertia, lateral error between the vehicle's center of mass and the reference trajectory, and heading angle error between the vehicle's heading angle and the trajectory centerline.

[0063] The reference trajectory is a pre-set trajectory, and the distributed drive electric vehicle needs to travel according to the reference trajectory.

[0064] Step 102: Use the MPC algorithm to track the vehicle-mounted sensor information and the reference trajectory to obtain the desired front wheel steering angle.

[0065] The MPC (Model-based Predictive Control) algorithm works by obtaining state information from the external environment at each computational step and predicting possible future states using a state update function. Simultaneously, under a given reference state, the algorithm is optimized to minimize a predefined performance metric, thereby obtaining the optimal sequence of control increments. The first one or several control increments are typically selected as the control output. This optimization process uses a rolling optimization approach, continuously refining the algorithm until the entire algorithm task is completed.

[0066] Step 103 : When it is determined based on the vehicle-mounted sensor information that at least one steering actuator is faulty, the SMC algorithm is used to calculate the desired front wheel steering angle and the actual front wheel steering angle at the previous moment to obtain the differential torque.

[0067] The core idea of ​​the SMC (Sliding Mode Control) algorithm is to establish a sliding surface, pull the system onto the sliding surface, and make the system move along the sliding surface.

[0068] The actual front wheel turning angle at the previous moment can be obtained from the vehicle sensor information.

[0069] Step 104 : When at least one driving actuator is determined to be faulty based on the vehicle-mounted sensor information, the differential torque is calculated based on an optimization allocation algorithm that minimizes tire adhesion utilization to obtain the current moment torque of each wheel.

[0070] Since the vehicle's handling stability is better when the sum of the tire adhesion utilization rates is minimized, we can set an optimization allocation algorithm based on the goal of minimizing the tire adhesion utilization rates, and use the optimization allocation algorithm to reasonably distribute the driving torque of each wheel.

[0071] Step 105 : Control the steering of the distributed drive electric vehicle according to the current torque.

[0072] In summary, the actuator fault-tolerant control method of the electric vehicle provided in the embodiment of the present application can calculate the differential torque based on the actual front wheel angle and the calculated expected front wheel angle when at least one steering actuator fails and causes the vehicle to completely or partially lose steering ability. When at least one drive actuator fails, the driving torque of each wheel can be reasonably distributed based on the differential torque, so that the distributed drive electric vehicle has better tracking performance. The fault-tolerant control method with a hierarchical architecture can effectively improve the fault tolerance and robustness of the steering system, and realize reliable control of the vehicle when the steering actuator and the drive actuator fail at the same time, thereby improving driving safety.

[0073] As shown in FIG2 , it shows a flow chart of a method for controlling an actuator of an electric vehicle provided by an embodiment of the present application. The method for controlling an actuator of an electric vehicle can be applied to a distributed drive electric vehicle using front-wheel drive steering. The method for controlling an actuator of an electric vehicle can include:

[0074] Step 201: Obtain onboard sensor information and a reference trajectory of a distributed drive electric vehicle.

[0075] Distributed drive electric vehicles are equipped with multiple on-board sensors, which collect information to obtain on-board sensor information.

[0076] The vehicle sensor information includes but is not limited to the following information: vehicle longitudinal speed, vehicle lateral speed, vehicle yaw rate, lateral stiffness of the vehicle's front and rear axles, front wheel angle, vehicle center of mass sideslip angle, vehicle moment of inertia, lateral error between the vehicle's center of mass and the reference trajectory, and heading angle error between the vehicle's heading angle and the trajectory centerline.

[0077] The reference trajectory is a pre-set trajectory, and the distributed drive electric vehicle needs to travel according to the reference trajectory.

[0078] Step 202: Construct an MPC trajectory tracking optimization objective function based on the vehicle-mounted sensor information and the reference trajectory; convert the MPC trajectory tracking optimization objective function into a quadratic function; solve the quadratic function using a quadratic programming method and the first constraint condition to obtain an optimal control increment sequence in the control time domain; determine the first n control variables in the optimal control increment sequence as the desired front wheel steering angle, where n is a positive integer.

[0079] Specifically, creating an MPC trajectory tracking optimization objective function based on vehicle sensor information and reference trajectory can include the following steps:

[0080] (1) Based on the on-board sensor information and the reference trajectory, the vehicle dynamics model equation and the trajectory tracking model equation are jointly established to obtain the state space expression.

[0081] FIG3 shows a schematic diagram of the planar motion of a vehicle. A vehicle dynamics model and a trajectory tracking model can be established based on the parameters shown in FIG3 .

[0082] A. Establishing vehicle dynamics model

[0083] To solve the trajectory tracking problem of fault-tolerant control of multiple actuators in distributed drive electric vehicles, it is necessary to construct a control-oriented vehicle dynamics model that ignores pitch and roll, considers only front wheel steering, and has hub motors installed in each wheel hub:

[0084] Where m is the vehicle mass, v x is the vehicle longitudinal velocity, v y is the vehicle's lateral velocity, γ is the vehicle's yaw rate, C f and C r is the cornering stiffness of the front and rear axles of the vehicle, δ f is the front wheel turning angle of the vehicle, β is the side slip angle of the vehicle's center of mass, l f and l r is the distance from the front and rear axles of the vehicle to the center of mass, I z is the vehicle's moment of inertia. These parameters can be obtained from the on-board sensor information.

[0085] B. Establishing trajectory tracking model

[0086] Among them, e y is the lateral error between the vehicle center of mass and the target trajectory, ψ e is the heading angle error between the vehicle heading angle and the trajectory centerline, and ρ is the curvature of the reference trajectory. Let ψ e The angles of cos(ψ e )≈1, sin(ψ e )≈ψ e And tan(β)≈β. So based on β≈v y / v x The trajectory tracking model can be obtained.

[0087] C. Establish state space expression

[0088] To achieve excellent control results, the MPC algorithm is used in this embodiment to design the upper-level trajectory tracking controller. The principle of the MPC algorithm is that in each calculation step, the system will obtain state information from the external environment and predict the possible future states through the state update function. At the same time, under a given reference state, the algorithm is optimized to minimize the pre-set performance indicators, thereby obtaining the optimal control increment sequence. Generally, the first or first few control increments are selected as the control output. This optimization adopts the form of rolling optimization, continuously optimizing the algorithm until the entire algorithm task is completed.

[0089] By combining the vehicle dynamics model and the trajectory tracking model, the state space expression can be obtained as follows:

[0090] in,

[0091] Select are the state quantities of the vehicle system, representing the vehicle's sideslip angle, vehicle yaw rate, lateral error between the vehicle's center of mass and the target trajectory, and heading angle error between the vehicle's heading angle and the trajectory centerline. is the control variable, representing the front wheel steering angle of the vehicle, w=ρv x For external interference.

[0092] (2) Discretize the parameters in the state space expression to obtain the discretized gain matrix equation.

[0093] Using the zero-order hold technique for discretization, the discretized gain matrix equation can be obtained:

[0094] Among them, A d , B d , D d Denote the discretized gain matrices of matrices A, B, and D respectively, and T is the discretization step size.

[0095] (3) Substitute the discretized gain matrix equation into the state space expression to obtain the MPC trajectory tracking optimization objective function.

[0096] Specifically, converting the MPC trajectory tracking optimization objective function into a quadratic function can include the following steps:

[0097] (1) Predict the future state in the time domain based on the MPC trajectory tracking optimization objective function.

[0098] According to the state space expression equation, the future state in the time domain can be predicted as follows: Y = S x x(t)+S uu(t-1)+S Δ ΔU+S w W (5)

[0099] in, Np is the prediction time domain step size of the trajectory tracking controller, and Nc is the pre-control time domain step size of the trajectory tracking controller.

[0100] n is the dimension of the state quantity in the state space, and m is the dimension of the control quantity in the state space.

[0101] (2) Generate a quadratic function based on the future state in the time domain.

[0102] After obtaining the future state Y within the predicted time domain, the system's current control increment ΔU (the desired front wheel angle) can be determined by solving the constrained optimization objective function. To track the reference trajectory in real time, the optimization objective is to minimize the error between the system output and the desired value. Considering the mechanical limitations of the vehicle's steering actuator and the risk of vehicle loss of control due to excessive front wheel angle changes, the control increment ΔU should be as small as possible.

[0103] In order to meet the constraints of mechanical structure and motion conditions, it is necessary to impose constraints on the optimization objective function and some states of the system to achieve the best control effect. In other words, Δδ f , δ f , β, γ, α f , α r The corresponding constraints must be met to achieve the lateral motion control of the vehicle and the reliability of the linear model. Among them, the first constraint conditions of β and γ can be obtained according to the steady-state conditions:

[0104] It is assumed that μ in formula (6) is known.

[0105] In summary, the performance index J of the trajectory tracking controller can be mpc Defined as:

[0106] in, is the system error (Y r (t)-Y(t)) weight matrix, In order to control the weight matrix of the increment (ΔU(t)), a relaxation factor ε is added to avoid the situation where the problem cannot be solved. ρ is the weight coefficient of the relaxation factor. is the reference system output, which is a time-varying variable determined by the reference trajectory and system state at each moment. r Zhonge y Reference value e yr and ψe The reference value ψ er Should be 0, the reference value of β r and the reference value of γ r for:

[0107] in,

[0108] When the current state variable y(t) and the control variable u(t) at the previous moment are known, the quadratic function can be solved by quadratic programming optimization within the control cycle, that is, Formula 7, to obtain a series of optimal control increment sequences in the control time domain:

[0109] Then, the first n control increments in the control increment sequence are used as the actual control input increments to act on the system, and the desired front wheel steering angle δ at the current moment can be obtained. f , n is a positive integer. In one embodiment, we can use the first control increment in the control increment sequence as the desired front wheel steering angle.

[0110] During normal driving, if the steering actuators of a distributed drive electric vehicle are functioning properly, the desired front wheel angle can be directly applied to the steering actuator to control it to achieve the desired front wheel angle, thereby achieving trajectory tracking. However, if the steering actuator fails, the current differential torque must be determined based on the desired front wheel angle, and torque distribution is based on this differential torque.

[0111] Step 203 : When it is determined based on the vehicle-mounted sensor information that at least one steering actuator is faulty, the SMC algorithm is used to calculate the desired front wheel steering angle and the actual front wheel steering angle at the previous moment to obtain the differential torque.

[0112] The principle of differential steering is shown in Figure 4. The longitudinal driving force of the left and right wheels of the front axle will generate a torque τ around the kingpin. σ and τ r When the torque τ l and τ r When there is a difference, a differential torque will be generated, which can be used to control the return torque τ of the left and right wheels. α To achieve differential steering, the differential torque can be expressed as:

[0113] in, The kingpin caster angle is, and σ is the kingpin inclination angle.

[0114] The sliding mode front wheel angle tracking control strategy is used below to determine the current differential torque.

[0115] Specifically, the SMC algorithm is used to calculate the desired front wheel angle and the actual front wheel angle at the previous moment to obtain the differential torque, which may include the following steps:

[0116] (1) Construct a dynamic model of the steering actuator, which includes the unknown differential torque.

[0117] Considering that the friction torque of the steering actuator and the second-order derivative of the front wheel angle are usually small and can be ignored as external disturbances, the dynamic model of the steering actuator can be simplified as follows:

[0118] Among them, b e is the equivalent damping of the steering actuator, τ α It is the sum of the aligning moments of the left and right wheels around their respective kingpins. α The self-aligning torque generated by the lateral force of the tire and the tire's own self-aligning torque And the self-aligning torque generated by the vertical load on the tire Among them, F y is the tire lateral force, M z is the tire's self-aligning torque, F z is the vertical load on the tire.

[0119] (2) Construct a sliding surface based on the expected front wheel angle and the actual front wheel angle.

[0120] In order to obtain the front wheel steering angle command δ fd Perform effective tracking and construct the following sliding surface: s=δ fd -δ f (11)

[0121] (3) Perform first-order differentiation on the sliding surface to obtain the differential equation.

[0122] Taking the first-order differential of the synovial surface, we get:

[0123] (4) Calculate the differential torque based on the dynamic model and differential equation.

[0124] According to formula (12), we can get: ΔM f =b e u-τ α (13)

[0125] in, Setting error value x i is the damping ratio, Δ is the thickness of the sliding surface, wn =|1 / x i Δ| is the natural frequency.

[0126] Step 204 : When at least one driving actuator is determined to be faulty based on the vehicle-mounted sensor information, the differential torque is calculated based on an optimization allocation algorithm that minimizes tire adhesion utilization to obtain the current moment torque of each wheel.

[0127] Since the vehicle's handling stability is better when the sum of the tire adhesion utilization rates is minimized, we can set an optimization allocation algorithm based on the goal of minimizing the tire adhesion utilization rates, and use the optimization allocation algorithm to reasonably distribute the driving torque of each wheel.

[0128] After obtaining the differential torque, a lower-level torque distribution controller can be established to perform torque distribution.

[0129] Specifically, the differential torque is calculated based on the optimization allocation algorithm that minimizes tire adhesion utilization to obtain the current moment torque of each wheel, which may include the following steps:

[0130] (1) Obtain the fault information of the driving actuator and construct the failure mode based on the fault information.

[0131] The failure mode of the drive actuator is defined as: T' i =m i T i

[0132] Among them, T' i is the actual output torque of the driving motor, T i is the expected output torque of the driving motor, m i The fault diagnosis factor is output by the drive system fault diagnostic device.

[0133] (2) Construct an optimization objective function based on minimizing tire adhesion utilization according to the failure mode.

[0134] Establish an optimization objective function based on minimizing tire adhesion utilization:

[0135] Among them, F xi is the longitudinal force of each wheel of the vehicle, F zi is the vertical load on each wheel of the vehicle.

[0136] (3) The quadratic programming method and the second constraint condition are used to solve the optimization objective function to obtain the current torque of each wheel.

[0137] During the optimization allocation algorithm solution process, it is necessary not only to ensure that the effective solution can meet the requirements of the total longitudinal drive torque and differential torque, but also to ensure that the effective solution does not produce additional yaw torque. The influence of factors such as the maximum output torque of the drive motor and fault information must also be considered. In other words, the second constraint is created based on the failure mode, the total drive torque requirement, the differential torque requirement, and the requirement that the effective solution does not produce additional yaw torque. Therefore, the constraint is:

[0138] The quadratic programming method is used to solve the driving torque of each wheel, and the performance indicators obtained are:

[0139] in, And ζ=diag{ζ1,ζ2,ζ3} is the weight matrix that satisfies the equality conditions, ζ1 is the weight of the differential torque ΔM f ζ2 is the required longitudinal torque T tot ζ3 is the weight of the additional yaw moment generated.

[0140] u=[T fl T fr T rl T rr ] is the optimized vector of the solver, representing the output torque of the hub motors of the four wheels of the vehicle. v=[ΔM f T tot 0] T is the differential torque ΔM f , Required longitudinal torque T tot and the expected value of the additional yaw moment (to avoid the additional yaw moment affecting the trajectory tracking process, it will be set to 0), K is the gain matrix,

[0141] Step 205 : Control the steering of the distributed drive electric vehicle according to the current torque.

[0142] In summary, the actuator fault-tolerant control method of the electric vehicle provided in the embodiment of the present application can calculate the differential torque based on the actual front wheel angle and the calculated expected front wheel angle when at least one steering actuator fails and causes the vehicle to completely or partially lose steering ability. When at least one drive actuator fails, the driving torque of each wheel can be reasonably distributed based on the differential torque, so that the distributed drive electric vehicle has better tracking performance. The fault-tolerant control method with a hierarchical architecture can effectively improve the fault tolerance and robustness of the steering system, and realize reliable control of the vehicle when the steering actuator and the drive actuator fail at the same time, thereby improving driving safety.

[0143] In this embodiment, the distributed drive electric vehicle is provided with a trajectory tracking controller, a steering failure controller, a steering system fault detector, a torque distribution controller, a drive system fault detector, and a vehicle speed tracker, as shown in FIG5 . The following describes the process of the fault-tolerant control method in conjunction with the structure of the distributed drive electric vehicle.

[0144] 1. The trajectory tracking controller generates the desired front wheel angle based on the vehicle sensor information and the reference trajectory;

[0145] 2. The steering system fault detector detects whether there is a fault in the steering actuator based on information from onboard sensors. If there is a fault in the steering actuator, the steering failure controller is triggered to operate;

[0146] 3. The steering failure controller generates a differential torque based on the desired front wheel angle and the actual front wheel angle at the previous moment;

[0147] 4. The drive system fault detector detects whether the drive actuator is faulty based on the information from the on-board sensors. If the drive actuator is faulty, it triggers the torque distribution controller to operate and sends the fault parameters to the torque distribution controller.

[0148] 5. The speed tracker generates a longitudinal demand torque based on the target speed and actual speed;

[0149] 6. The torque distribution controller generates the current moment torque of each wheel based on the differential torque, fault parameters and longitudinal demand torque, and controls the steering of the distributed drive electric vehicle according to the current moment torque.

[0150] The following is a simulation experiment analysis of the above fault-tolerant control method:

[0151] To verify the effectiveness of the aforementioned fault-tolerant control scheme, we conducted experiments under three operating conditions: uniform acceleration in a straight line, a step-turn steering operation, and a double-lane change operation. The experimental platform was jointly built using MATLAB / Simulink and CarSim. The vehicle parameters used in the experiments are shown in Table 1.

[0152] 1. Uniform acceleration straight line working condition

[0153] A uniformly accelerated linear simulation experiment was conducted with vehicle speeds ranging from 30 km / h to 60 km / h. The experiment was conducted with failure rates of the vehicle's left front wheel drive motor set at 100%, 75%, 50%, and 25%. As shown in the trajectory plot in Figure 6, as the failure rate increases, the vehicle without fault-tolerant control deviates further from its trajectory, while the vehicle with a fault-tolerant controller maintains a straight line. Figure 7 shows the longitudinal velocity results, showing that as the failure rate increases, the vehicle without fault-tolerant control exhibits poorer speed tracking, while the vehicle with a fault-tolerant controller consistently maintains good speed tracking.

[0154] 2. Step steering condition

[0155] A step steering simulation experiment was conducted, generating a 0.1 rad step angle at 1 second and simulating a steering actuator failure at 3 seconds. The experimental results are shown in Figure 8. As can be seen, when the steering actuator fails at 3 seconds, the vehicle without fault-tolerant control loses steering capability, the yaw rate drops to zero, the yaw angle remains unchanged, and the vehicle continues to travel along the current heading angle. However, the experimental results of the vehicle with a fault-tolerant controller after the failure are essentially the same as those before the failure. This result demonstrates that the proposed steering failure fault-tolerant controller has good fault-tolerant performance.

[0156] 3. Double lane shifting conditions

[0157] A dual-lane-change experiment was conducted under conditions of a vehicle speed of 60 km / h and a road adhesion coefficient of 0.85. Three types of vehicles were tested and equipped with the proposed fault-tolerant control method: vehicle A was a normal vehicle, vehicle B experienced only a steering actuator failure, and vehicle C experienced a complete failure of both the steering actuator and the drive motor. The experimental results, shown in Figure 9, show that vehicles B and C lost steering capability due to steering actuator failure, but all three vehicles maintained good trajectory tracking accuracy and stability, demonstrating the effectiveness of the proposed fault-tolerant control method for steering failures. The torque distribution diagram in Figure 10 shows that vehicle B experienced a different torque distribution, requiring the controller to generate differential torque to compensate for the steering actuator failure. Vehicle C experienced failures in both the steering actuator and the left front wheel drive motor, but the lower-level torque distribution controller was able to immediately allocate torque to the remaining three drive motors to meet vehicle trajectory tracking control requirements. Overall, the experimental results demonstrate that the proposed multi-actuator failure-tolerant control method exhibits excellent fault-tolerant performance and can improve the safety and reliability of distributed drive electric vehicles.

[0158] Table 1 Vehicle parameters

[0159] As shown in Figure 11, it shows a structural block diagram of an actuator fault-tolerant control device for an electric vehicle provided by one embodiment of the present application. The actuator fault-tolerant control device for an electric vehicle can be applied to a distributed drive electric vehicle using front-wheel drive steering. The actuator fault-tolerant control device for an electric vehicle can include:

[0160] An acquisition module 1110 is configured to acquire onboard sensor information and a reference trajectory of a distributed drive electric vehicle;

[0161] A tracking module 1120 is configured to track the vehicle sensor information and the reference trajectory using a model predictive control (MPC) algorithm to obtain a desired front wheel steering angle.

[0162] a calculation module 1130 for calculating a desired front wheel angle and an actual front wheel angle at a previous moment using a sliding mode control (SMC) algorithm to obtain a differential torque when at least one steering actuator fault is determined based on vehicle-mounted sensor information;

[0163] The calculation module 1130 is further configured to calculate the differential torque based on an optimization allocation algorithm that minimizes tire adhesion utilization when at least one drive actuator fault is determined based on vehicle-mounted sensor information, to obtain the current moment torque of each wheel;

[0164] The control module 1140 is used to control the steering of the distributed drive electric vehicle according to the torque at the current moment.

[0165] In an optional embodiment, the tracking module 1120 is further configured to:

[0166] Construct the MPC trajectory tracking optimization objective function based on the vehicle sensor information and the reference trajectory;

[0167] Convert the MPC trajectory tracking optimization objective function into a quadratic function;

[0168] The quadratic function is solved using the quadratic programming method and the first constraint condition to obtain the optimal control increment sequence in the control time domain;

[0169] The first n control variables in the optimal control increment sequence are determined as the desired front wheel steering angle, where n is a positive integer.

[0170] In an optional embodiment, the tracking module 1120 is further configured to:

[0171] According to the vehicle sensor information and the reference trajectory, the vehicle dynamics model equation and the trajectory tracking model equation are jointly established to obtain the state space expression;

[0172] Discretize the parameters in the state space expression to obtain the discretized gain matrix equation;

[0173] Substituting the discretized gain matrix equation into the state space expression, the MPC trajectory tracking optimization objective function is obtained.

[0174] In an optional embodiment, the tracking module 1120 is further configured to:

[0175] Predict future states in the time domain based on the MPC trajectory tracking optimization objective function;

[0176] Generates a quadratic function based on future states in the time domain.

[0177] In an optional embodiment, the calculation module 1130 is further configured to:

[0178] Construct a dynamic model of the steering actuator, which includes the unknown differential torque;

[0179] Constructing a sliding surface based on the desired front wheel steering angle and the actual front wheel steering angle;

[0180] Perform first-order differentiation on the sliding surface to obtain the differential equation;

[0181] The differential torque is calculated based on the dynamic model and differential equations.

[0182] In an optional embodiment, the calculation module 1130 is further configured to:

[0183] Obtain the fault information of the drive actuator and construct the failure mode based on the fault information;

[0184] According to the failure mode, an optimization objective function based on minimizing tire adhesion utilization is constructed;

[0185] The quadratic programming method and the second constraint condition are used to solve the optimization objective function to obtain the current moment torque of each wheel.

[0186] In an optional embodiment, the second constraint condition is created based on the failure mode, the total driving torque requirement, the differential torque requirement, and the requirement that the effective solution does not produce additional yaw torque.

[0187] In summary, the actuator fault-tolerant control device of the electric vehicle provided in the embodiment of the present application can calculate the differential torque based on the actual front wheel angle and the calculated expected front wheel angle when at least one steering actuator fails and causes the vehicle to completely or partially lose its steering ability. When at least one drive actuator fails, the driving torque of each wheel can be reasonably distributed based on the differential torque, so that the distributed drive electric vehicle has better tracking performance. The fault-tolerant control method of the hierarchical architecture can effectively improve the fault tolerance and robustness of the steering system, and realize reliable control of the vehicle when the steering actuator and the drive actuator fail at the same time, thereby improving driving safety.

[0188] One embodiment of the present application provides a computer-readable storage medium, wherein the storage medium stores at least one instruction, and the at least one instruction is loaded and executed by a processor to implement the above-mentioned actuator fault-tolerant control method for an electric vehicle.

[0189] An embodiment of the present application provides a distributed drive electric vehicle, which includes a processor and a memory, wherein the memory stores at least one instruction, and the instruction is loaded and executed by the processor to implement the above-mentioned electric vehicle actuator fault-tolerant control method.

[0190] It should be noted that the above-described embodiments of the electric vehicle actuator fault-tolerant control device, when performing fault-tolerant control of the electric vehicle actuator, are merely exemplified by the division of the aforementioned functional modules. In actual applications, the aforementioned functions can be distributed among different functional modules as needed, i.e., the internal structure of the electric vehicle actuator fault-tolerant control device can be divided into different functional modules to perform all or part of the functions described above. Furthermore, the electric vehicle actuator fault-tolerant control device and the electric vehicle actuator fault-tolerant control method embodiment provided in the above-described embodiments share the same concept. The specific implementation process is detailed in the method embodiment and will not be further elaborated here.

[0191] Those skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware, or by a program to instruct the relevant hardware, and the program may be stored in a computer-readable storage medium, which may be a read-only memory, a disk, or an optical disk, etc.

[0192] The above description is not intended to limit the embodiments of the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the scope of protection of the embodiments of the present application.

Claims

1. A fault-tolerant control method for an actuator of an electric vehicle, characterized in that: For use in a distributed drive electric vehicle using front wheel drive steering, the method comprises: Acquiring on-board sensor information and reference trajectory of the distributed drive electric vehicle; Using a model predictive control (MPC) algorithm to track the vehicle-mounted sensor information and the reference trajectory to obtain a desired front wheel steering angle; When at least one steering actuator failure is determined according to the vehicle-mounted sensor information, a sliding mode control SMC algorithm is used to calculate the desired front wheel steering angle and the actual front wheel steering angle at the previous moment to obtain a differential torque; When at least one driving actuator failure is determined according to the vehicle-mounted sensor information, the differential torque is calculated based on an optimization allocation algorithm that minimizes tire adhesion utilization to obtain the current moment torque of each wheel; The steering of the distributed drive electric vehicle is controlled according to the torque at the current moment.

2. The electric vehicle actuator fault-tolerant control method according to claim 1, characterized in that: The adopting the MPC algorithm to track the vehicle-mounted sensor information and the reference trajectory to obtain the expected front wheel steering angle includes: Constructing an MPC trajectory tracking optimization objective function according to the vehicle-mounted sensor information and the reference trajectory; Converting the MPC trajectory tracking optimization objective function into a quadratic function; The quadratic function is solved by using a quadratic programming method and a first constraint condition to obtain an optimal control increment sequence in a control time domain; The first n control quantities in the optimal control increment sequence are determined as the desired front wheel steering angle, where n is a positive integer.

3. The electric vehicle actuator fault-tolerant control method according to claim 2, characterized in that: The step of creating an MPC trajectory tracking optimization objective function according to the vehicle-mounted sensor information and the reference trajectory includes: According to the vehicle sensor information and the reference trajectory, a vehicle dynamics model equation and a trajectory tracking model equation are combined to obtain a state space expression; Discretizing the parameters in the state space expression to obtain a discretized gain matrix equation; Substituting the discretized gain matrix equation into the state space expression, the MPC trajectory tracking optimization objective function is obtained.

4. The electric vehicle actuator fault-tolerant control method according to claim 3, characterized in that: The step of converting the MPC trajectory tracking optimization objective function into a quadratic function comprises: Predicting future states in the time domain according to the MPC trajectory tracking optimization objective function; A quadratic function is generated according to the future state in the time domain.

5. The electric vehicle actuator fault-tolerant control method according to claim 1, characterized in that: The method of using the SMC algorithm to calculate the desired front wheel steering angle and the actual front wheel steering angle at the previous moment to obtain the differential torque includes: Constructing a dynamic model of the steering actuator, wherein the dynamic model includes an unknown differential torque; constructing a sliding surface according to the desired front wheel steering angle and the actual front wheel steering angle; Performing first-order differentiation on the sliding surface to obtain a differential equation; The differential torque is calculated according to the dynamic model and the differential equation.

6. The electric vehicle actuator fault-tolerant control method according to claim 1, characterized in that: The optimization allocation algorithm based on minimization of tire adhesion utilization calculates the differential torque to obtain the current moment torque of each wheel, including: Acquiring fault information of the drive actuator, and constructing a failure mode according to the fault information; Constructing an optimization objective function based on minimizing tire adhesion utilization according to the failure form; The optimization objective function is solved by using a quadratic programming method and a second constraint condition to obtain the current moment torque of each wheel.

7. The electric vehicle actuator fault-tolerant control method according to claim 6, characterized in that: The second constraint condition is created according to the failure mode, the total driving torque requirement, the differential torque requirement, and the requirement that no additional yaw torque occurs in the effective solution.

8. An actuator fault-tolerant control device for an electric vehicle, characterized in that: Used in a distributed drive electric vehicle using front wheel drive steering, the device comprises: An acquisition module, used to acquire on-board sensor information and a reference trajectory of the distributed drive electric vehicle; A tracking module, used for tracking the vehicle-mounted sensor information and the reference trajectory using a model predictive control (MPC) algorithm to obtain a desired front wheel steering angle; a calculation module, configured to calculate the desired front wheel steering angle and the actual front wheel steering angle at the previous moment by using a sliding mode control SMC algorithm to obtain a differential torque when at least one steering actuator fault is determined according to the vehicle-mounted sensor information; The calculation module is further configured to calculate the differential torque based on an optimization allocation algorithm for minimizing tire adhesion utilization to obtain the current moment torque of each wheel when at least one drive actuator fault is determined according to the vehicle-mounted sensor information; A control module is used to control the steering of the distributed drive electric vehicle according to the torque at the current moment.

9. A computer-readable storage medium, characterized in that: The storage medium stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the actuator fault-tolerant control method of the electric vehicle as described in any one of claims 1 to 7.

10. A distributed drive electric vehicle, characterized in that: The distributed drive electric vehicle comprises a processor and a memory, wherein the memory stores at least one instruction, and the instruction is loaded and executed by the processor to implement the actuator fault-tolerant control method of the electric vehicle as claimed in any one of claims 1 to 7.

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