Automobile stability hierarchical control method considering motor energy consumption optimization and fault state

Abstract

The invention provides an automobile stability hierarchical control method based on motor energy consumption optimization and a fault state, which comprises the following steps of: firstly, calculating energy consumption loss through motor loss power integration; secondly, an upper-layer control algorithm takes a linear two-degree-of-freedom vehicle model as a reference, a sequential quadratic programming (SQP) algorithm is adopted to calculate required total driving torque and additional yawing torque, and effective tracking of the vehicle to an expected motion state is achieved; and finally, distributing the total driving torque and the additional yawing torque to each wheel by a lower-layer control algorithm, and solving a motor torque control law by adopting a gradient descent method by taking tire stability margin maximization as a target when a wheel side motor has no fault. When the motor breaks down, torque correction distribution is carried out on the remaining normal motors according to the fault state, and it is ensured that the output driving torque and the additional yawing torque meet the requirement of an upper-layer control algorithm. According to the method, energy consumption optimization, stability control and fault tolerance are fused into automobile hierarchical control, and the method has the advantages of being high in optimization precision and high in adaptability.

Description

Technical Field

[0001] The present invention belongs to the technical field of automobile electric stability control, and specifically is a vehicle stability hierarchical control method taking into account motor energy consumption optimization and fault status. Background Art

[0002] With the continuous development of hybrid vehicle technology, vehicle energy consumption optimization and stability control have become the focus of research. As the core driving component, the energy efficiency and reliability of the motor directly affect the performance and user experience of the vehicle. However, in the existing technology, the motor may suffer energy loss during operation, and in the event of a fault, the vehicle's stability control ability will be seriously affected.

[0003] Some existing patents, such as the invention patent with patent number CN119511895A, designed a multi-mode redundant integrated control system for a car-by-wire chassis, providing ideal execution parameters for the car-by-wire chassis execution controller, and the car-by-wire chassis execution controller provides execution actions for the car-by-wire chassis execution subsystem, but the patent lacks research on the impact of motor energy consumption and stability. The invention patent with patent number CN118672251A proposes an online variable time domain model predictive control method based on lateral stability, which tracks the trajectory of the controlled vehicle and performs rolling optimization based on the feedback of the state quantity of the controlled vehicle, and classifies the lateral stability of the vehicle to characterize the danger level of the vehicle, but lacks effective fault tolerance for motor failure.

[0004] Existing vehicle stability control systems usually adopt a single control strategy, which makes it difficult to take into account both energy consumption optimization and fault tolerance at the same time. When the motor fails, the existing control method may not be able to effectively distribute the driving torque, resulting in reduced vehicle stability. In particular, in the coordinated design of torque correction distribution and energy consumption optimization under motor failure conditions, the existing technology has obvious deficiencies. Therefore, how to improve the stability and fault tolerance of the vehicle under fault conditions while optimizing energy consumption is a technical problem that needs to be solved urgently. Summary of the invention

[0005] The present invention proposes a hierarchical control method for automobile stability that takes into account motor energy consumption optimization and fault conditions. The method aims to solve the problem in the prior art that it is difficult to balance motor energy consumption optimization and vehicle stability control. By designing a hierarchical control architecture, the upper-level control algorithm calculates the required total driving torque and additional yaw torque based on a linear two-degree-of-freedom vehicle model; the lower-level control algorithm is responsible for distributing the driving torque to each wheel and performing torque correction distribution when the motor fails. Through this collaborative control strategy, the present invention achieves coordinated control of energy consumption optimization and vehicle stability, while significantly improving the vehicle's fault tolerance in a fault state.

[0006] The object of the present invention can be achieved by the following technical solutions:

[0007] A hierarchical control method for vehicle stability considering motor energy consumption optimization and fault states, wherein the drive system consists of a power battery, a battery energy management system, in-wheel motors, in-wheel motor controllers, and wheels. The in-wheel motor controllers control the in-wheel motors to drive the wheels, and the motors are powered by the power battery driven by the battery energy management system. The control method includes:

[0008] S1: Calculate the motor energy consumption loss

[0009] The loss of energy consumption can be obtained by integrating the motor loss power:

[0010]

[0011] where E motor is the energy consumed by the four in-wheel motors, P lossi is the motor loss power, t is the current time, and t0, t f are the initial and termination times.

[0012] S2: The upper-layer control algorithm takes the linear two-degree-of-freedom vehicle model as a reference, and uses the sequential quadratic programming algorithm to calculate the required total driving torque and additional yaw torque, so as to effectively track the desired motion state of the vehicle;

[0013] S3: The lower-layer control algorithm distributes the total driving torque and additional yaw torque requirements to each wheel. When there is no fault in the four wheel-side motors, with the maximization of the tire stability margin as the control target, the gradient descent method is used to solve the nominal distribution motor torque control laws T lf 、T rf 、T lr 、T rr (left front wheel, right front wheel, left rear wheel, right rear wheel);

[0014] S4: When one or more wheel-side motors fail, according to the corresponding fault states, combined with the nominal distribution control law, the torque of the remaining normal wheel-side drive motors is corrected and distributed, so that the total output driving torque and additional yaw torque can meet the control requirements calculated by the upper-layer control algorithm.

[0015] Further, step S2 is specifically:

[0016] The upper-layer control algorithm takes the linear two-degree-of-freedom vehicle model as the reference model, and takes the reference yaw angular velocity and the sideslip angle of the center of mass as the state variables x(t) = [β γ] T , and takes the sum of the front wheel steering angle δ a and the additional front wheel steering angle δ d , as well as the additional yaw torque as the control variables The state - space equation is established as follows:

[0017]

[0018] In the formula, a and b are the distances between the centroid and the front and rear axles respectively, k1 and k2 are the cornering stiffnesses of the front and rear axles, m is the vehicle mass, v is the vehicle speed, and I z is the moment of inertia of the vehicle about the z - axis.

[0019] The motor loss energy consumption E motor is incorporated into the performance index of the controller objective function, and the sequence quadratic programming (SQP) algorithm is used to calculate the required total driving torque T d and the additional yaw moment requirement ΔM, so that the established vehicle model can effectively track the desired motion state;

[0020] Furthermore, the specific process of the upper - layer control algorithm in step S2 is as follows:

[0021] S21: Incorporate the motor loss energy consumption into the objective function:

[0022]

[0023] In the formula, is the objective function of the electric drive system energy consumption (the integral of the motor power P motor ), is the objective function of the yaw rate tracking error (the sum of the squares of the error ω act_i between the actual yaw rate ω and the desired yaw rate ω ref_i ), and k1 and k2 are the weight coefficients of the objective function.

[0024] S22: Convert the optimal control problem in formula (3) into the standard form of a nonlinear programming problem:

[0025]

[0026] In the formula, is the set of state variables and control variables.

[0027] S23: Use the sequence quadratic programming method to solve the nonlinear programming problem in formula (4), and apply the first element of the optimal control input sequence to the system.

[0028] Furthermore, the specific process of the nominal torque distribution algorithm in the lower - layer control algorithm in step S3 is as follows:

[0029] S31: With the minimum sum of the squares of the longitudinal utilization rates of each wheel as the optimization control objective, set the objective function as:

[0030]

[0031] Wherein, μ is the friction coefficient, and F xi is the lateral tire force, and F zi is the vertical tire force.

[0032] S32: The upper-layer dynamic constraint is:

[0033]

[0034] Wherein, d is the track width and r is the wheel radius.

[0035] The adhesion ellipse is restricted as:

[0036]

[0037] Wherein, F i,y is the longitudinal tire force.

[0038] S33: According to the optimization objective function and constraint conditions, convert Equation (5) into a weighted least squares problem:

[0039]

[0040] Wherein, u is the control input, u = [T lf T rf T lr T rr T , W u is the control quantity weight matrix, and W v is the distribution weight matrix.

[0041] Solve using the gradient descent method to obtain the driving torque of each motor as the nominal control output.

[0042] Furthermore, the specific lower-layer control algorithm in step S4 is:

[0043] S41: Distinguish five cases of single-wheel motor failure, two motors on the same side of the different axes failure, two motors on the different sides of the same axis failure, two motors on the same side failure, and more than two motors failure. The flag bits F fault are 1, 2, 3, 4, and 5 respectively;

[0044] S42: Perform stability coordination fault tolerance control based on the fault state. The specific method is:

[0045] (1) When F fault = 1, that is, in the case of single-wheel motor failure (taking the motor at the right front wheel as an example), the control input in Equation (8) is updated to u = [T lf T lr T rr T ​​, considering the upper-layer dynamic constraints and the adhesion ellipse limitation, the corrected torque is solved based on the gradient descent method;

[0046] (2) When F fault = 2 and F fault = 3, that is, when two motors on the opposite sides fail, the control input of Equation (8) is updated to the torques of the remaining two motors. Considering the upper-layer dynamic constraints, the corrected torque is solved based on the gradient descent method;

[0047] (3) When F fault = 4, that is, when two motors on the same side fail (taking the motors at the left front wheel and the right front wheel as an example), the control input of Equation (8) is updated to u = [T lr T rr , T , and only the upper-layer dynamic constraint T d = T lf + T rf + T lr + T rr needs to be satisfied;

[0048] (4) When F fault = 5, that is, when more than two motors fail, the safety-first strategy is executed for emergency braking control.

[0049] Compared with the prior art, the advantages of the present invention are as follows:

[0050] 1. A hierarchical control method for vehicle stability considering motor energy consumption optimization and fault states proposed by the present invention solves the problem of vehicle stability control considering motor energy consumption and fault constraints. By using the sequential quadratic programming (SQP) algorithm in the upper-layer control algorithm to calculate the total driving torque and the additional yaw moment, and the lower-layer control algorithm to achieve torque distribution and fault tolerance, a new solution for vehicle stability control is provided.

[0051] 2. The hierarchical control method for vehicle stability considering motor energy consumption optimization and fault states described in the present invention integrates energy consumption optimization, stability control, and fault tolerance into the hierarchical control framework. By calculating the energy consumption loss through the integral of the motor loss power, and taking the maximization of the tire stability margin as the goal when there is no fault, the motor torque control law is solved using the gradient descent method, significantly improving the economy and stability of the vehicle.

[0052] 3. The hierarchical control method for vehicle stability considering motor energy consumption optimization and fault states described in the present invention designs a torque correction and distribution strategy based on the fault state. When a motor fails, the torques of the remaining normal motors are corrected and distributed according to the fault state to ensure that the output driving torque and the additional yaw moment meet the requirements of the upper-layer control algorithm, effectively improving the fault tolerance and driving safety of the vehicle, and providing important guiding significance for vehicle stability control under complex working conditions. Brief Description of the Drawings

[0053] Figure 1 Schematic diagram of the structure of a hub - driven electric vehicle system used in the embodiment;

[0054] Figure 2 Flowchart of the hierarchical control method for vehicle stability based on motor energy consumption and fault status used in the embodiment;

[0055] Figure 3 Flowchart of the solution for the total driving torque and additional yaw moment requirements using the sequential quadratic programming algorithm used in the embodiment;

[0056] Figure 4 Flowchart of the solution for the nominal distributed motor torque control law using the gradient descent method used in the embodiment;

[0057] Figure 5 Flowchart of the correction and distribution of the driving motor torque based on the motor fault status used in the embodiment;

[0058] Markings in the figure: 1. Power battery, 2. Battery energy management system, 3. In - wheel motor, 4. In - wheel motor controller, 5. Wheel. Detailed Embodiment

[0059] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described examples are only a part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0060] Embodiment

[0061] A method for active control of torsional vibration during the engine start - stop process of a power - split hybrid system, wherein the powertrain is as Figure 1 shown, and is composed of a power battery 1, a battery energy management system 2, an in - wheel motor 3, an in - wheel motor controller 4, and a wheel 5. The in - wheel motor controller 4 controls the in - wheel motor 5 to drive the wheel 6, and the in - wheel motor 3 is powered by the power battery 1 driven by the battery energy management system 2.

[0062] Schematic diagram of the flowchart of the hierarchical control method for vehicle stability considering motor energy consumption optimization and fault status for the in - wheel motor drive system is as Figure 2As shown below. First, calculate the energy consumption loss through the integral of the motor loss power; secondly, the upper-layer control algorithm takes the linear two-degree-of-freedom vehicle model as a reference and uses the sequential quadratic programming algorithm to calculate the required total driving torque and additional yaw moment to effectively track the desired motion state of the vehicle; finally, the lower-layer control algorithm distributes the total driving torque and additional yaw moment to each wheel. When there is no fault in the in-wheel motors, with the goal of maximizing the tire stability margin, the gradient descent method is used to solve the motor torque control law; when a motor fails, the remaining normal motors are corrected and distributed according to the fault state to ensure that the output driving torque and additional yaw moment meet the requirements of the upper-layer control algorithm. The specific steps of this hierarchical stability control method include:

[0063] S1: Calculate the motor energy consumption loss;

[0064] The energy consumption loss can be obtained by integrating the motor loss power:

[0065]

[0066] where E motor is the energy consumed by the four in-wheel motors, P lossi is the motor loss power, t is the current time, and t0, t f are the initial and termination times.

[0067] S2: The upper-layer control algorithm takes the linear two-degree-of-freedom vehicle model as a reference and uses the sequential quadratic programming algorithm to calculate the required total driving torque and additional yaw moment to effectively track the desired motion state of the vehicle;

[0068] The upper-layer control algorithm takes the linear two-degree-of-freedom vehicle model as the reference model, and uses the reference yaw rate and sideslip angle of the center of mass as the state variables x(t) = [β γ] T , and takes the sum of the front wheel steering angle δ a and the additional front wheel steering angle δ d , as well as the additional yaw moment as the control variables to establish the state space equation as:

[0069]

[0070] where a and b are the distances between the center of mass and the front and rear axles respectively, k1 and k2 are the cornering stiffnesses of the front and rear axles, m is the vehicle mass, v is the vehicle speed, and I z is the moment of inertia of the vehicle about the z-axis.

[0071] Incorporate the motor energy consumption loss E motor into the performance index of the controller objective function, and use the sequential quadratic programming (Sequence Quadratic Program, SQP) algorithm to calculate the required total driving torque Td With the additional yaw moment requirement ΔM, the established vehicle model can effectively track the desired motion state; the specific process is as follows:

[0072] S21: Incorporate the motor loss energy consumption into the objective function:

[0073]

[0074] In the formula, is the energy consumption of the electric drive system (the integral of the motor power P motor ), the objective function, is the yaw rate tracking error objective function (the sum of the squares of the error ω act_i between the actual yaw rate ω and the desired yaw rate ω ref_i ), and k1, k2 are the weight coefficients of the objective function.

[0075] S22: Convert the optimal control problem in Equation (3) into the standard form of a nonlinear programming problem:

[0076]

[0077] In the formula, is the set of state variables and control variables.

[0078] S23: Use the sequential quadratic programming method to solve the nonlinear programming problem in Equation (4), and apply the first element of the optimal control input sequence to the system.

[0079] S3: Use the lower-level control algorithm to distribute the total driving torque and the additional yaw moment requirement to each wheel. When all four in-wheel motors are fault-free, with the maximization of the tire stability margin as the control objective, use the gradient descent method to solve the nominal distributed motor torque control law T lf 、T rf 、T lr 、T rr (left front wheel, right front wheel, left rear wheel, right rear wheel); the specific process is as follows:

[0080] S31: With the minimization of the sum of the squares of the longitudinal utilization rates of each wheel as the optimization control objective, set the objective function as:

[0081]

[0082] In the formula, μ is the friction coefficient, F xi is the lateral tire force, and F zi is the vertical tire force.

[0083] S32: The upper-level dynamic constraints are:

[0084]

[0085] In the formula, d is the track width and r is the wheel radius.

[0086] The adhesion ellipse is restricted as:

[0087]

[0088] In the formula, F i,y is the longitudinal tire force.

[0089] S33: According to the optimization objective function and constraint conditions, transform Equation (5) into a weighted least squares problem:

[0090]

[0091] In the formula, u is the control input, u = [T lf T rf T lr T rr T , W u is the control quantity weight matrix, and W v is the distribution weight matrix.

[0092] Use the gradient descent method to solve for the driving torque of each motor as the nominal control output.

[0093] S4: When one or more wheel-side motors fail, according to the corresponding fault status, combine the nominal distribution control law to perform torque correction distribution on the remaining normal wheel-side drive motors, so that the total output driving torque and the additional yaw moment can meet the control requirements calculated by the upper-layer control algorithm. The specific process is as follows:

[0094] S41: Distinguish five cases of single-wheel motor failure, two motors on the different sides of the same axis failure, two motors on the different sides of different axes failure, two motors on the same side failure, and more than two motors failure. The flag bit F fault is 1, 2, 3, 4, 5 respectively;

[0095] S42: Perform stability coordination fault tolerance control based on the fault status. The specific method is as follows:

[0096] (1) F fault = 1, that is, when a single-wheel motor fails (taking the motor at the right front wheel as an example), the control input of Equation (8) is updated to u = [T lf T lr T rr T , considering the upper-layer dynamic constraints and adhesion ellipse restrictions, solve for the correction torque based on the gradient descent method;

[0097] (2) F fault = 2 and F fault ​​When \(n = 3\), that is, when two motors on the opposite sides fail, the control input of Equation (8) is updated to the torques of the remaining two motors. Considering the upper-layer dynamic constraints, the corrected torque is solved based on the gradient descent method.

[0098] (3)F fault When \(n = 4\), that is, when two motors on the same side fail (taking the motors at the left front wheel and the right front wheel as an example), the control input of Equation (8) is updated to \(u = [T lr T rr T , and only the upper-layer dynamic constraint \(T d = T lf + T rf + T lr + T rr needs to be satisfied.

[0099] (4)F fault When \(n = 5\), that is, when more than two motors fail, the safety-first strategy is executed for emergency braking control.

[0100] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention. Any equivalent structural transformation made under the inventive concept of the present invention by using the content of the specification and drawings of the present invention, or any direct / indirect application in other related technical fields, is included in the patent protection scope of the present invention.​

Claims

1. A vehicle stability hierarchical control method considering motor energy consumption optimization and fault status, characterized in that: The steps include: S1: Calculate the motor energy loss; S2: The upper control algorithm uses the linear two-degree-of-freedom vehicle model as a reference and uses a sequential quadratic programming algorithm to calculate the required total driving torque and additional yaw torque to achieve effective tracking of the vehicle to the desired motion state; S3: The total driving torque and additional yaw torque requirements are distributed to each wheel using the lower control algorithm. When the motors on the four wheel sides are not faulty, the tire stability margin is maximized as the control target, and the nominal distribution motor torque control law is solved by the gradient descent method. lf , right front wheel T rf , Left rear wheel T lr , right rear wheel T rr ; S4: When one or more wheel-side motors fail, torque correction distribution is performed on the remaining normal wheel-side drive motors according to the corresponding fault status and the nominal distribution control law, so that the total output drive torque and additional yaw torque can meet the control requirements calculated by the upper-level control algorithm.

Images