Driving and braking suspension cooperative control method, device, equipment, medium and product

Abstract

The invention discloses a driving and braking suspension cooperative control method, device and equipment, a medium and a product, and relates to the field of vehicle active safety control. The method comprises the steps that a driving demand analysis and decision module determines driver demand torque, a control mode and a neutral steering target according to the vehicle state, the steering angle of a steering wheel of a driver and input information of a driving / braking pedal; the hybrid coordination control module determines front and rear motor torques according to the torque required by a driver and the vehicle state under the conventional working condition, and determines four-wheel cylinder brake pressure by using sliding mode control according to the vehicle state; the torque vector optimization distribution control and Smith predictor control method is adopted to determine the torque of a front motor, the torque of a rear motor and the brake pressure of a four-wheel cylinder according to the torque required by a driver, the vehicle state and a neutral steering target under the limiting working condition, and the weighted pseudo-inverse distribution vehicle body posture coordination control is adopted to determine the damping force of the semi-active suspension. The method can achieve the precise tracking of the expected neutral steering target, and gives full play to the lateral movement limit of the vehicle.

Description

Technical Field

[0001] The present application relates to the field of vehicle active safety control, and in particular to a drive-brake-suspension coordinated control method, device, equipment, medium and product. Background Art

[0002] Safety is the primary objective of vehicle operation, and active safety control is key to ensuring this. When operating in extreme conditions such as emergency obstacle avoidance and high-speed steering, excessive speed or drastic steering changes can easily lead to severe understeer or skidding, potentially resulting in accidents. Utilizing the vehicle's lateral motion limits can improve safety during emergency obstacle avoidance and is crucial for enhancing safety and stability during extreme maneuvers. Dual-motor four-wheel-drive vehicles (DMVs) each have a motor driving their front and rear axles. Their chassis features a four-wheel independent braking system and active / semi-active suspension. Coordinated control of these multiple subsystems can maximize the vehicle's extreme performance. Therefore, research on coordinated chassis control for DMVs is crucial for improving safety and stability during high-speed steering. A method is urgently needed to address the issue of low tracking accuracy for vehicle control targets, which hinders the full utilization of the vehicle's motion limits. Summary of the Invention

[0003] The purpose of this application is to provide a drive-brake-suspension coordinated control method, device, equipment, medium and product, which can achieve accurate tracking of the desired steering target and fully utilize the lateral motion limit of the vehicle.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In a first aspect, the present application provides a drive-brake-suspension coordinated control method, comprising:

[0006] The driving demand analysis and decision module obtains vehicle status, driver's steering wheel angle and drive / brake pedal input information;

[0007] The driving demand analysis and decision module determines a driver's required torque, a control mode, and a neutral steering target based on the vehicle state, the driver's steering wheel angle, and the drive / brake pedal input information, and sends the driver's required torque, the control mode, and the neutral steering target to the hybrid coordination control module; the control mode is a normal operating condition or an extreme operating condition; and the neutral steering target is equal front and rear wheel slip angles.

[0008] Under normal working conditions, the hybrid coordinated control module determines the front and rear motor torques according to the driver's required torque and vehicle state, and determines the four-wheel cylinder brake pressure using sliding mode control according to the vehicle state; under extreme working conditions, the hybrid coordinated control module determines the front and rear motor torques and four-wheel cylinder brake pressure according to the driver's required torque, vehicle state and neutral steering target using torque vector optimization distribution control and Smith predictor control method, determines the semi-active suspension damping force using weighted pseudo-inverse distribution body posture coordinated control, and sends the front and rear motor torques, the four-wheel cylinder brake pressure and the semi-active suspension damping force to the vehicle actuator.

[0009] In a second aspect, the present application provides a drive-brake-suspension coordinated control device, comprising:

[0010] a driving demand analysis and decision module, which is used to determine the driver's required torque, control mode, and neutral steering target based on the vehicle state, the driver's steering wheel angle, and the drive / brake pedal input information, and send the driver's required torque, control mode, and neutral steering target to the hybrid coordination control module; the control mode is a normal operating condition or an extreme operating condition; and the neutral steering target is equal front and rear wheel slip angles;

[0011] a hybrid coordinated control module, the hybrid coordinated control module being configured to determine the front and rear motor torques based on the driver's required torque and vehicle state under normal operating conditions, and to determine the four-wheel cylinder brake pressures based on the vehicle state using sliding mode control; and, under extreme operating conditions, the hybrid coordinated control module being configured to determine the front and rear motor torques and the four-wheel cylinder brake pressures based on the driver's required torque, vehicle state, and neutral steering target using torque vector optimization distribution control and Smith predictor control methods, and to determine the semi-active suspension damping force using weighted pseudo-inverse distribution vehicle posture coordinated control, and to transmit the front and rear motor torques, the four-wheel cylinder brake pressures, and the semi-active suspension damping force to vehicle actuators;

[0012] The vehicle actuator is used to control the front and rear axle motors, the hydraulic braking system and the semi-active suspension system according to the front and rear motor torques, the four-wheel cylinder braking pressure and the damping force.

[0013] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned drive-brake-suspension coordinated control method.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned drive-brake-suspension coordinated control method.

[0015] In a fifth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements the above-mentioned drive-brake-suspension coordinated control method.

[0016] According to the specific embodiments provided in this application, this application has the following technical effects:

[0017] This application provides a drive-brake-suspension coordinated control method, device, equipment, medium, and product. The driving demand analysis and decision module sets the front and rear wheel slip angles equal as the neutral steering target, and then determines the driver's required torque, control mode, and neutral steering target based on the vehicle state, the driver's steering wheel angle, and the drive / brake pedal input information. It uses torque vector optimization distribution and weighted pseudo-inverse body posture coordination methods to achieve neutral steering of the vehicle under extreme conditions, fully utilizing the vehicle's lateral motion limit. Taking into account the response characteristic differences between the motor and the hydraulic brake system, the required wheel cylinder brake pressure is calculated based on the Smith predictor, thereby eliminating hydraulic system delays in advance and greatly improving the response speed of the hydraulic brake system. The motor and hydraulic brake system execute synchronously, reducing the deviation between the actual longitudinal force of the wheel and the target longitudinal force, ensuring that the vehicle accurately tracks the neutral steering target. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in 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.

[0019] Figure 1 This is an application environment diagram of a driving, braking, and suspension coordinated control method in one embodiment of the present application;

[0020] Figure 2 This is a framework diagram of related technology 1;

[0021] Figure 3 This is a framework diagram of related technology 2;

[0022] Figure 4 A schematic flow chart of a drive-brake-suspension coordinated control method provided in one embodiment of the present application;

[0023] Figure 5 A schematic diagram of the overall architecture of the control strategy and the logical interaction relationship between modules provided in one embodiment of the present application;

[0024] Figure 6 A logic diagram of triggering the extreme operating condition control mode provided in one embodiment of the present application;

[0025] Figure 7 A schematic diagram of a delay compensation control solution based on a Smith predictor provided in one embodiment of the present application;

[0026] Figure 8 A schematic diagram of a lateral dynamics model provided in one embodiment of the present application;

[0027] Figure 9 A schematic diagram of a tire adhesion ellipse relationship curve provided in one embodiment of the present application;

[0028] Figure 10 A schematic diagram of the relationship between the longitudinal and lateral forces of a tire when the lateral motion limit is maximum provided in one embodiment of the present application;

[0029] Figure 11 A schematic diagram of the structure of a computer device provided in one embodiment of the present application;

[0030] Figure 12 Figure 2. Actual response of the motor, hydraulic brake system, and Smith-based predictor-based hydraulic brake to track the same wheel torque target. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0032] like Figure 2 Related technology 1 shown: A method and system for extending vehicle handling limits based on drive-brake coordinated control. The following steps are involved: ① Calculating a desired additional yaw moment based on the deviation between the current vehicle state and the desired vehicle state; ② Allocating the required longitudinal forces to different wheels based on the additional yaw moment and additional longitudinal force, and their expected values; ③ Correcting the required longitudinal forces of the wheels using the wheel slip ratio; and ④ Coordinating the braking system's braking pressure on the wheels with the engine output torque based on the corrected required longitudinal forces to generate longitudinal forces acting on the wheels.

[0033] like Figure 3Related technology 2 shown: Distributed vehicle model predictive control method considering vehicle posture at handling limits. Steps: ① Propose vehicle control constraints to ensure global force / torque feasibility; ② Establish a variable step-size adaptive linear reference point prediction model based on a three-degree-of-freedom dynamic model considering vehicle posture; ③ Establish a cost function by comprehensively considering path tracking error, vehicle posture error, etc.; ④ Establish multiple constraints for the controller; ⑤ Based on the global forces and torques calculated by the controller in this application, solve the four-wheel longitudinal and lateral forces through the quadratic programming method, and calculate the front and rear axle angles and four-wheel drive force through the tire parameter online update module.

[0034] Regarding Related Technology 1: ① Related Technology 1 generates an additional yaw torque by tracking the desired yaw rate and center of mass slip angle, and then distributes the additional yaw torque to the executable driving torque and braking torque. However, when the vehicle is in extreme operating conditions, the two targets of yaw rate and center of mass slip angle conflict with each other, making it difficult to accurately track the desired yaw rate and center of mass slip angle at the same time, resulting in a weak effect of improving the lateral performance limit. ② The drive-braking coordination method of Related Technology 1 does not take into account the response characteristic differences between the drive system and the braking system. The drive system and the braking system cannot synchronously execute torque commands, resulting in low vehicle control target tracking accuracy.

[0035] Regarding Related Technology 2: ① Related Technology 2 constructs a multi-objective cost function of the global torque increment, the error between the vehicle posture and the reference posture, the vehicle lateral error, and the error between the vehicle speed and the set speed, and ensures vehicle stability by solving the multi-objective optimization function. This solution constructs a cost function containing 5 sub-objectives, and adjusts the importance of different objectives through weight coefficients. However, it is difficult to achieve optimal vehicle performance under extreme working conditions through multi-objective weighting, resulting in insufficient performance of the vehicle's extreme limits. ② The multi-objective optimization function constructed by Related Technology 2 has a high state dimension and many control variables. The model predictive control (MPC) algorithm needs to be run on high-performance equipment, and it is difficult to optimize and solve in real time on the on-board controller, resulting in high solution cost and poor practicality.

[0036] The purpose of this application is to solve the problem that existing methods are difficult to fully utilize the lateral motion limit of the vehicle under extreme working conditions. The desired goal of the above method is to track the desired yaw rate and center of mass sideslip angle. However, under extreme working conditions, the desired yaw rate conflicts with the center of mass sideslip angle, and this goal fundamentally cannot fully utilize the lateral limit of the vehicle. Therefore, this application takes neutral steering (equal side slip angles of the front and rear wheels) as the goal, which can solve the problem of the conflict between the desired yaw rate and the center of mass sideslip angle under extreme working conditions. If the vehicle can achieve neutral steering, it means that the front and rear wheels can maximize the use of road friction and fully utilize the lateral motion limit of the vehicle.

[0037] Regarding option ② of solution 1, because the actual motor response is relatively fast, while the hydraulic brake system has a delayed response characteristic that results in a slow response, for a centralized drive vehicle (this application aims to achieve the effect of independent four-wheel torque control in a distributed drive vehicle), the drive system and brake system must work together to generate different wheel torques on the left and right sides to generate the desired additional yaw moment to change the vehicle's state. However, the response characteristics of the motor and hydraulic brake system differ significantly. If the control system design does not consider these differences, the torque of the four wheels will not generate the required additional yaw moment, making it difficult for the actual vehicle state to track the desired value.

[0038] Regarding option 2 (2), real-time solution of high-dimensional multi-objective optimization is difficult to implement on current mass-produced vehicle controllers. The inability to solve the control quantity in real time makes this option less practical and makes it difficult to fully utilize the vehicle's extreme performance.

[0039] This application implements the torque vectoring control technology typically found in distributed drive vehicles on a dual-motor, four-wheel-drive electric vehicle. A torque vectoring optimization distribution control method for calculating front and rear axle motor torque and four-wheel brake pressure under extreme operating conditions is proposed. A hydraulic brake delay compensation control strategy based on a Smith predictor is established, improving the response speed of the hydraulic brake system and enabling synchronous execution of the motor and hydraulic brakes. Torque vectoring optimization distribution control and hydraulic brake delay compensation control enable all four wheels to generate the longitudinal force required to meet the desired neutral steering target, achieving precise tracking of the desired neutral steering target.

[0040] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0041] The drive-brake-suspension coordinated control method provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the vehicle status, the driver's steering wheel angle and the drive / brake pedal input information to the server 104. After the server 104 receives the vehicle status, the driver's steering wheel angle and the drive / brake pedal input information, the server 104 determines the driver's required torque, control mode, and neutral steering target based on the vehicle status, the driver's steering wheel angle and the drive / brake pedal input information, and sends the driver's required torque, the control mode and the neutral steering target to the hybrid coordination control module; the control mode is a normal working condition or an extreme working condition; the The neutral steering target is equal front and rear wheel slip angles. Under normal operating conditions, the front and rear motor torques are determined based on the driver's requested torque and vehicle state, and the four-wheel cylinder brake pressures are determined using sliding mode control based on the vehicle state. Under extreme operating conditions, the hybrid coordinated control module uses torque vector optimization distribution control and Smith predictor control methods to determine the front and rear motor torques and four-wheel cylinder brake pressures based on the driver's requested torque, vehicle state, and neutral steering target. It also uses weighted pseudo-inverse distribution and vehicle posture coordinated control to determine the semi-active suspension damping force. The front and rear motor torques, four-wheel cylinder brake pressures, and semi-active suspension damping force are then transmitted to the vehicle actuator. Server 104 can provide feedback of the obtained front and rear motor torques, four-wheel cylinder brake pressures, and semi-active suspension damping force to terminal 102. In addition, in some embodiments, the drive-brake-suspension coordinated control method can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly make predictions on the vehicle status, the driver's steering wheel angle, and the drive / brake pedal input information, or the server 104 can obtain the vehicle status, the driver's steering wheel angle, and the drive / brake pedal input information from the data storage system and make predictions on the vehicle status, the driver's steering wheel angle, and the drive / brake pedal input information.

[0042] Terminal 102 may include, but is not limited to, various desktop computers, laptops, smartphones, tablet computers, IoT devices, and portable wearable devices. IoT devices may include smart speakers, smart TVs, smart air conditioners, and smart car devices. Portable wearable devices may include smart watches, smart bracelets, and head-mounted devices. Server 104 may be implemented as a standalone server or a server cluster consisting of multiple servers, or may be a cloud server.

[0043] In an exemplary embodiment, Figure 4As shown, a driving brake suspension coordinated control method is provided. The method is executed by a computer device, specifically a computer device such as a terminal or a server, or a terminal and a server. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in FIG. 1 is used as an example to illustrate the method, which includes the following steps, wherein:

[0044] Step 201: The driving demand analysis and decision module obtains vehicle status, driver's steering wheel angle and drive / brake pedal input information.

[0045] Step 202: The driving demand analysis and decision module determines the driver's required torque, control mode and neutral steering target based on the vehicle state, the driver's steering wheel angle and the drive / brake pedal input information, and sends the driver's required torque, the control mode and the neutral steering target to the hybrid coordination control module; the control mode is a normal operating condition or an extreme operating condition; the neutral steering target is equal front and rear wheel slip angles.

[0046] Step 203: Under normal working conditions, the hybrid coordinated control module determines the front and rear motor torques according to the driver's required torque and the vehicle state, and determines the four-wheel cylinder brake pressure using sliding mode control according to the vehicle state; under extreme working conditions, the hybrid coordinated control module determines the front and rear motor torques and the four-wheel cylinder brake pressure according to the driver's required torque, the vehicle state and the neutral steering target using torque vector optimization distribution control and Smith predictor control methods, determines the semi-active suspension damping force using weighted pseudo-inverse distribution body posture coordinated control, and sends the front and rear motor torques, the four-wheel cylinder brake pressure and the semi-active suspension damping force to the vehicle actuator.

[0047] Implementing steps 201 through 203 above, by setting a neutral steering target, primarily addresses the issue of insufficient lateral motion limits and poor lateral stability during high-speed steering. Torque vector optimization, Smith predictor control, and weighted pseudo-inverse body posture coordination methods calculate the front and rear motor torques, four-wheel cylinder brake pressures, and semi-active suspension damping force under extreme driving conditions, achieving precise tracking of the neutral steering target and fully utilizing the vehicle's lateral motion limits under extreme driving conditions.

[0048] In another exemplary embodiment of the present application, the driving demand analysis and decision module determines the driver's required torque, control mode, and neutral steering target based on the vehicle state, the driver's steering wheel angle, and the drive / brake pedal input information, specifically including:

[0049] The driving demand analysis and decision module determines the driver's required torque based on the drive / brake pedal input information, the road adhesion coefficient of the vehicle state, and the maximum output torque of the motor at the current wheel speed; the driving demand analysis and decision module determines the front and rear wheel slip angle deviations based on the front and rear wheel slip angles of the vehicle state; the front and rear wheel slip angle deviations are used to determine the neutral steering target; the driving demand analysis and decision module determines the control mode based on the front and rear wheel slip angle deviations, the vehicle state, and the driver's steering wheel angle.

[0050] In actual application, the driving demand analysis and decision module determines the control mode according to the front and rear wheel slip angle deviation, the vehicle state and the driver's steering wheel angle, specifically including:

[0051] The driving demand analysis and decision module identifies the vehicle's turning state based on the vehicle state and the driver's steering wheel angle, wherein the turning state is turning or going straight; and the driving demand analysis and decision module determines a control mode based on the front and rear wheel sideslip angle deviation and the turning state.

[0052] In actual application, the driving demand analysis and decision module determines the control mode according to the front and rear wheel slip angle deviation and the turning state, specifically including:

[0053] The driving demand analysis and decision module performs an opening count initialization; the initialization is to control the opening count sampling counter and the trigger flag to be zero.

[0054] The driving demand analysis and decision module determines whether the front and rear wheel sideslip angle deviation exceeds a first deviation threshold, and obtains a first judgment result.

[0055] If the first judgment result is yes, the driving demand analysis and decision module controls the start-up counting sampling counter to start counting and record the number of start-up sampling cycles.

[0056] The driving demand analysis and decision module determines whether the number of start sampling cycles continuously meets the first set number of cycles to obtain a second judgment result.

[0057] If the second judgment result is yes, the driving demand analysis and decision module sets the trigger flag to 1, controls the extreme working condition to be turned on, and initializes the closing count; the initial state of the control mode is the normal working condition; the closing count is initialized to control the closing count sampling counter to zero.

[0058] If the second judgment result is no, the process returns to the step of "the driving demand analysis and decision module determines whether the front and rear wheel sideslip angle deviation exceeds the first deviation threshold value".

[0059] If the first judgment result is no, then return to the step of "driving demand analysis and decision module to initialize the start count".

[0060] The driving demand analysis and decision module determines whether the front and rear wheel slip angle deviation is less than a second deviation threshold and the turning state is straight ahead, and obtains a third judgment result.

[0061] If the third judgment result is yes, the driving demand analysis and decision module controls the closing count sampling counter to start counting and record the closing sampling cycle number.

[0062] The driving demand analysis and decision module determines whether the number of closed sampling cycles continuously meets the second set number of cycles to obtain a fourth judgment result.

[0063] If the fourth judgment result is yes, the driving demand analysis and decision module sets the trigger flag to 0, and controls the normal working condition to be turned on.

[0064] If the fourth judgment result is no, return to the step "the driving demand analysis and decision module determines whether the front and rear wheel sideslip angle deviation is less than the second deviation threshold and the turning state is straight ahead, and obtains the third judgment result".

[0065] If the third judgment result is no, return to step “initialize the closing count”.

[0066] In another exemplary embodiment of the present application, the hybrid coordination control module determines the front and rear motor torques according to the driver's required torque and the vehicle state under normal operating conditions, and determines the four-wheel cylinder brake pressure using sliding mode control according to the vehicle state, specifically including:

[0067] When the control mode is normal working conditions, the hybrid coordinated control module determines the front and rear motor torques under normal working conditions based on the longitudinal acceleration of the vehicle state, the vertical load ratio of the front and rear axles, and the driver's required torque; and uses sliding mode control to calculate the four-wheel cylinder brake pressure under normal working conditions based on the ideal slip rate and actual slip rate of the four wheels of the vehicle state.

[0068] In actual applications, the hybrid coordinated control module uses torque vector optimization distribution control and Smith predictor control methods to determine the front and rear motor torques and four-wheel cylinder brake pressures based on the driver's required torque, vehicle state, and neutral steering target under extreme operating conditions. It also uses weighted pseudo-inverse distribution and vehicle posture coordinated control to determine the semi-active suspension damping force. Specifically, the module includes:

[0069] When the control mode is extreme working condition, the hybrid coordination control module calculates the total longitudinal force requirement of the vehicle when driving according to the driver's required torque;

[0070] The hybrid coordination control module calculates an additional yaw moment required for neutral steering according to the front and rear tire slip angle deviations in the vehicle state;

[0071] The hybrid coordination control module constructs a first objective function according to the total longitudinal force requirement of the vehicle and the additional yaw moment required for neutral steering;

[0072] The hybrid coordination control module constructs a second objective function based on the tire longitudinal force in the vehicle state;

[0073] The hybrid coordinated control module uses the active set method to solve the first and second objective functions to obtain the ideal longitudinal forces of the four wheels under extreme working conditions;

[0074] The hybrid coordination control module calculates the front and rear motor torques and the four-wheel ideal wheel cylinder brake pressures according to the four-wheel ideal longitudinal forces;

[0075] The hybrid coordination control module calculates the four-wheel wheel cylinder brake pressure based on the Smith predictor according to the four-wheel ideal wheel cylinder brake pressure, the four-wheel ideal longitudinal force and the four-wheel actual longitudinal force in the vehicle state;

[0076] The hybrid coordinated control module calculates the semi-active suspension damping force under extreme working conditions using a weighted pseudo-inverse allocation method based on the extreme working condition trigger flag, neutral steering target, vehicle body pitch and vehicle roll attitude.

[0077] In another exemplary embodiment of the present application, in order to address the problem that existing methods are unable to fully utilize the vehicle's motion limit under extreme working conditions, the present application proposes a drive-brake-suspension coordinated control method that improves the vehicle's lateral motion limit. When the vehicle is driving normally, the front and rear torques are distributed in real time according to the front and rear axle load ratio, and the four-wheel brake adopts an electro-hydraulic composite brake control strategy based on sliding mode control to meet the stable driving requirements under normal working conditions. Under extreme working conditions such as emergency obstacle avoidance, a drive-brake coordinated torque vector optimization distribution method is proposed. Considering the differences in the response characteristics of the motor and the hydraulic system, a control method based on the Smith predictor is established to improve the response speed of the hydraulic brake system and realize coordinated control of the drive-brake system; a semi-active suspension control strategy based on weighted pseudo-inverse distribution is established to adjust the vehicle body pitch and roll posture, and ultimately achieve accurate tracking of the desired neutral steering target.

[0078] Controller composition

[0079] The drive-brake-suspension collaborative control method for improving the lateral motion limit of the vehicle mainly consists of two parts: a driving demand analysis and decision module and a hybrid coordination control module. The driving demand analysis and decision module is designed to calculate the driver's required torque, decide the control mode and generate the desired neutral steering target. The module receives various signals from the vehicle's measurement estimation and calculation, the driver's steering wheel angle and the drive / brake pedal input signal, and outputs the driver's required torque, control mode and neutral steering target to the hybrid coordination control module; the hybrid coordination module calculates the front and rear motor torque, four-wheel wheel cylinder brake pressure and the damping force of the semi-active suspension based on the driver's required torque, control mode and neutral steering target, and finally sends the control signals of the motor torque, wheel cylinder brake pressure and damping force to the vehicle actuator. The overall architecture of the controller and the logical interaction between modules are shown as follows. Figure 5 shown.

[0080] 1.1 Driving Demand Analysis and Decision-making

[0081] The proposed driving demand analysis and decision module includes driver input analysis, control mode decision and neutral steering control target calculation, providing the hybrid coordinated control module with the required torque, control mode and desired neutral steering target.

[0082] (1) Driver input analysis

[0083] This section uses the driver's drive / brake pedal opening, the maximum motor output torque at the current wheel speed, and the road adhesion coefficient as inputs to output the driver's required torque, providing the hybrid coordination control module with the driver's required torque (see Formula 1). The driver's required torque is tested and calibrated based on the pedal opening, the maximum output torque of the front and rear motors, and the road conditions. The corresponding relationship can be expressed as:

[0084] T req =f(k ped ,T max ,μ) (1)

[0085] Where T req is the driver's required torque; k ped is the opening of the driving pedal or brake pedal; T max Indicates the maximum torque output by the motor at the current speed; μ is the road adhesion coefficient.

[0086] (2) Determination of neutral steering target value

[0087] The state estimation and solution module estimates the front and rear tire slip angles according to the tire slip angle calculation formula based on the longitudinal speed, lateral speed, yaw rate and front wheel turning angle in the vehicle state. This part takes the front and rear tire slip angles as input, outputs the tire slip angle deviation, and provides the desired reference target for the hybrid coordination control module (see formula 2). A reasonable tracking target is a prerequisite for achieving vehicle control. The current study uses a linear two-degree-of-freedom model to calculate the desired yaw rate for stability control, but the two-degree-of-freedom model is difficult to reflect the dynamic characteristics of the vehicle during extreme operation, and this method requires an accurate estimate of the road adhesion coefficient. This application is based on the neutral steering theory and sets the front wheel slip angle to be equal to the rear wheel slip angle as the target value, which can avoid the influence of inaccurate road adhesion coefficient estimation on the desired yaw rate calculated based on the two-degree-of-freedom model. The neutral steering target can be expressed as

[0088] α f =α r (2)

[0089] Where α f and α r The neutral steering target of this application is to make the front and rear wheel slip angles equal.

[0090] (3) Extreme operating condition triggering logic and turning recognition

[0091] This section uses the front and rear wheel slip angle deviations as input, and the output is the activation or deactivation of the control mode under normal or extreme conditions. Establishing appropriate control trigger conditions is crucial for coordinated control, and the proposed control strategy encompasses both normal and extreme conditions.

[0092] 1) Turning state recognition

[0093] The proposed extreme operating condition control on / off needs to be decided based on the vehicle's turning state. First, the vehicle's turning state needs to be identified. The identification conditions of the turning state are shown in formulas (3) and (4). When both conditions are met at the same time, it is determined to be a turning state.

[0094]

[0095] Where a y and ω z are lateral acceleration and yaw angular velocity respectively; a y0 and ω z0 are the lateral acceleration threshold and yaw angular velocity threshold respectively; Δω and Δω0 are the left and right wheel speed difference and wheel speed difference threshold respectively; flag1, flag2 and flag3 are the sub-flags for turning recognition based on lateral acceleration, yaw angular velocity and left and right wheel speed difference respectively; Flag is the turning recognition flag, Flag=1 represents turning, Flag=2 represents straight driving.

[0096] 2) Extreme working condition control trigger logic

[0097] The trigger logic of extreme working condition control is as follows: Figure 6 As shown in the figure. The initial state is "off", in which parameters are initialized. When the front and rear wheel slip angle deviation is detected to exceed the threshold, the system enters the "on count" state. This state is to avoid false triggering due to parameter fluctuations such as noise. In the figure, i and j are the sampling counters of the on count and off count modules, respectively. Only when the condition that the front and rear wheel slip angle deviation exceeds the threshold is met for n (n is a calibrable parameter) consecutive sampling cycles, the trigger flag is 1, and the extreme condition control strategy is activated. Similarly, the "on" state determines the system exit condition in real time. If the current rear wheel slip angle deviation is less than the threshold and the vehicle is traveling in a straight line, the system enters the "off count" state. If the above condition is met for m (m is a calibrable parameter) consecutive sampling cycles, the trigger flag is 0, and the extreme condition control mode is exited. In the figure, α1 and α2 are the front and rear wheel slip angle deviation thresholds.

[0098] 1.2 Hybrid coordinated control strategy

[0099] The hybrid coordinated control scheme proposed in this application includes two modes: normal working condition control and extreme working condition control. The normal working condition control scheme is designed to meet the driver's normal driving needs, while the extreme working condition control scheme is used to maximize the vehicle's lateral motion limit and improve vehicle driving safety.

[0100] (1) Conventional operating condition control scheme

[0101] This part takes the driver's required torque, longitudinal acceleration, longitudinal vehicle speed, wheel rotation speed, wheel longitudinal force and rolling resistance as input, and outputs the front and rear motor torque and four-wheel cylinder brake pressure.

[0102] 1) Front and rear motor torque distribution scheme

[0103] The front and rear motor torques are distributed based on the ratio of the driver's required torque to the vertical loads on the front and rear axles. This solution has the advantages of being simple to implement and fully utilizing the road adhesion limits of the front and rear axles. The vertical loads on the front and rear axles can be expressed as

[0104]

[0105] Where F zf and F zr are the vertical loads on the front and rear axles respectively; m is the mass of the vehicle; g is the acceleration of gravity; a x is the longitudinal acceleration; h is the height of the vehicle's center of mass; l f and l rare the horizontal distances from the center of mass to the front and rear axles respectively; L is the horizontal distance between the front and rear axles, L = l f +l r .

[0106] According to the vertical load ratio of the front and rear axles, we can get

[0107]

[0108] Where η is the vertical load ratio between the front and rear axles.

[0109] The calculation formula of the front and rear motor torque based on the ratio of the driver's required torque to the vertical load of the front and rear axles is:

[0110]

[0111] Where, T df and T dr are the front axle motor torque and the rear axle motor torque respectively.

[0112] 2) Electro-hydraulic compound brake control

[0113] Electric vehicle braking control can be achieved through both motor braking and hydraulic braking. When the wheel slip ratio exceeds -0.2, to ensure that the vehicle does not lock during braking, a four-wheel independent anti-lock braking control strategy based on sliding mode control is designed.

[0114] Set the desired wheel slip λ during braking d is -0.15, we can get

[0115]

[0116] Where J ω is the wheel moment of inertia; ω i is the rotation speed of the i-th wheel, i∈[fl,fr,rl,rr] represents the left front wheel, right front wheel, left rear wheel and right rear wheel respectively; T di is the driving torque; T bi is the braking torque; R e is the effective radius of the wheel; F xi is the actual longitudinal force of the tire; F fi is the rolling resistance; v xi is the longitudinal speed at the wheel center is the desired wheel speed; = is the derivative of the wheel speed.

[0117] Since sliding mode control (SMC) has the advantages of simplicity and strong robustness, the sliding mode control is used to design the tracking control algorithm. The tracking error of the system is defined as

[0118]

[0119] Where e1 is the tracking error. To minimize the tracking error, the integral sliding surface is selected.

[0120]

[0121] Where s1 is the integral sliding surface, and c1 is a constant greater than 0.

[0122] In order to reduce system chattering and shorten system response time, an improved exponential approach rate is used.

[0123]

[0124] Where ζ1, υ1 and σ1 are the coefficients of the sliding mode controller, and s is the sliding mode surface constructed in SMC; is the derivative of the sliding surface; Ω is the reaching law function.

[0125] The ideal braking torque of the wheel can be expressed as

[0126]

[0127] In the formula is the ideal wheel braking torque.

[0128] When the wheel slip rate exceeds the threshold, according to the low selection principle, the braking torques required by the front and rear axles are

[0129]

[0130] In the formula is the ideal braking torque of the front axle; is the ideal braking torque of the rear axle; They are the ideal braking torque of the left front wheel, the ideal braking torque of the right front wheel, the ideal braking torque of the left rear wheel and the ideal braking torque of the left rear wheel respectively.

[0131] When the required braking torque is less than the maximum braking torque of the motor, the hydraulic braking torque of the four wheels is 0. When the required braking torque exceeds the maximum braking torque of the motor, the motor braking torque and the hydraulic braking torque are respectively

[0132]

[0133] In the formula Motor braking torque during anti-lock control; is the maximum braking torque of the motor; κ∈[f,r]; is the braking torque of the hydraulic brake system of the i-th wheel; χ is the speed ratio of the reducer.

[0134] According to the conversion relationship between the braking torque of the hydraulic brake system and the wheel cylinder braking pressure, we can get

[0135]

[0136] Where P bi is the wheel cylinder brake pressure of the i-th wheel, A b It is the conversion factor between braking torque and wheel cylinder brake pressure.

[0137] (2) Extreme operating condition control scheme

[0138] This section takes the driver's requested torque, the extreme operating condition control trigger flag, the neutral steering target value, and the vehicle state as inputs, and outputs the front and rear motor torques, four-wheel cylinder brake pressures, and semi-active suspension damping force. When the extreme operating condition control trigger flag is 1, the front and rear motor torques and four-wheel cylinder brake pressures switch from conventional operating condition control mode to extreme operating condition control mode, and the semi-active suspension outputs the damping force required for posture adjustment. When the extreme operating condition control trigger flag is 0, the front and rear motor torques and four-wheel cylinder brake pressures switch from extreme operating condition control mode to conventional operating condition control mode, thus this flag serves to switch the control mode output.

[0139] This section will introduce the coordinated control scheme of the dual-motor four-wheel drive vehicle's drive system, braking system, and suspension system under extreme working conditions. This scheme can effectively improve the vehicle's lateral motion limit.

[0140] 1) Additional yaw moment generation

[0141] An additional yaw moment control algorithm based on SMC is designed to ensure the vehicle has neutral steering characteristics.

[0142] The tracking error of the system is defined as

[0143] e2=α f -α r (16)

[0144] Where e2 is the side slip angle deviation of the front and rear wheels.

[0145] In order to minimize the tracking error, the integral sliding surface is selected

[0146]

[0147] Where s2 is the integral sliding surface; c2 is the sliding surface control parameter; and t is time.

[0148] Using the improved exponential approach rate, the additional yaw moment calculation formula is as follows

[0149]

[0150] Where M z is the additional yaw moment; I zis the moment of inertia around the z axis; δ f is the front wheel turning angle; v x is the longitudinal velocity at the center of mass of the vehicle; C f and C r are the front and rear tire cornering stiffnesses respectively; υ2, Ω and ζ2 are the adjustment parameters of the sliding mode controller.

[0151] The total longitudinal force required when the vehicle is driving is obtained according to the driver's required torque. The specific formula can be expressed as

[0152]

[0153] In the formula is the desired longitudinal force; χ is the speed ratio of the reducer

[0154] 2) Neutral steering optimization control

[0155] The essence of vehicle control lies in controlling the tire forces on all four wheels. Using tire forces as the control variable provides a more intuitive representation of the relationship between control input and control variable. This section uses the ideal tire longitudinal force as the control variable and develops an algorithm for optimizing the distribution of the additional yaw moment required for neutral steering and the desired longitudinal force.

[0156] Optimization control needs to meet the following goals:

[0157] (a) The first objective is to track the desired longitudinal force and yaw moment. This objective ensures that the vehicle achieves neutral steering at the driver's desired speed, thereby maximizing the lateral motion limit at the target speed. The control variables are the four tire longitudinal forces, and the relationship between them is

[0158]

[0159] Where B is the wheelbase, F xfl 、F xfr 、F xrl and F xrr The desired longitudinal forces on the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively; B1 is the weight matrix for the four-wheel desired longitudinal forces. The first objective is to track the desired longitudinal force and the additional yaw moment. This objective is of the highest importance. Accurate tracking of the desired longitudinal force means the vehicle can maintain the target speed, and accurate tracking of the additional yaw moment means the vehicle can achieve neutral steering.

[0160] Formula (20) can be solved by constructing an optimization function. The objective function can be expressed as

[0161]

[0162] Where W v represents the weight coefficient matrix; represents the weight coefficient of the desired longitudinal force; represents the weight coefficient of the yaw moment; u and They are the lower and upper bounds of the longitudinal force respectively; the upper bound of the longitudinal force needs to consider the road adhesion limit and the maximum output torque of the motor; the lower bound of the longitudinal force needs to consider the road adhesion limit and the maximum braking capacity of the motor and braking system; the specific relationship can be expressed as

[0163]

[0164] In the formula is the maximum driving torque of the motor; is the maximum braking torque of the hydraulic brake system; F zij is the vertical load of each wheel.

[0165] (b) The second goal is to minimize the longitudinal force of each tire. To improve the lateral stability margin of the vehicle during driving, the optimization goal J2 is constructed by reducing the longitudinal adhesion utilization of the tire to improve lateral stability while preventing excessive wheel slip. This objective function can be expressed as

[0166]

[0167] W u =diag(W u,Fxfl , W u,Fxfr , W u,Fxrl , W u,Fxrr ) (25)

[0168] Where W u is the tire longitudinal force weight coefficient matrix; W u,Fxfl 、W u,Fxfr 、W u,Fxrl 、W u,Fxrr are the weight coefficients of the longitudinal forces of the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively. This coefficient is a nonlinear function of the tire slip rate and increases with the increase of the slip rate, thereby minimizing the tire longitudinal force and suppressing excessive wheel slip.

[0169] The above two optimization goals are typical convex quadratic programming problems. In order to ensure the real-time performance of the optimization algorithm, the effective set method is used to solve this type of convex quadratic programming problem. The basic form of the effective set method is as follows:

[0170]

[0171] Where x is the variable to be optimized; H a is a symmetric matrix; C a is the Jacobian matrix. E={1,2,…m e} is a finite set of equality constraints, P = {m e +1,m e+2,…m} represents a finite set of inequality constraints. Using the weight coefficient to merge the two optimization objectives and transform them into a weighted least squares problem can further improve the computational efficiency. According to the Cauchy-Schwarz inequality, we can get

[0172]

[0173] In the formula is the weight coefficient, which is used to weigh the priority of the two optimization objectives in the objective function. A and b are both parameter sets composed of weight coefficient matrices.

[0174] The effect of the optimization algorithm is to solve the tracking neutral steering target control quantity with high accuracy, fast calculation speed, high real-time performance and strong practicality, which can solve the problems of complex algorithm, low real-time performance and poor practicality reflected in the second solution of using the MPC algorithm.

[0175] 3) Torque vector distribution control

[0176] According to the single-wheel dynamics model, for dual-motor four-wheel drive electric vehicles, controlling the front and rear motor torque and the four-wheel cylinder brake pressure can change the four-wheel longitudinal force. If the actual four-wheel longitudinal force can accurately track the ideal longitudinal force solved by neutral steering optimization, the vehicle can achieve neutral steering and fully utilize the lateral motion limit. Because the motor response is much faster than the hydraulic system response, the execution frequency between the two is out of sync. This can lead to a large difference between the actual tire longitudinal force and the optimized ideal longitudinal force, which can seriously cause vehicle instability. Therefore, the different response characteristics of the motor and hydraulic braking systems need to be considered when allocating actuators.

[0177] This section constructs a hydraulic brake system response prediction model based on Smith predictive control theory. This model predicts the output hydraulic braking force to preemptively eliminate hydraulic system delays, significantly improving the hydraulic brake system's response speed and aligning the motor and hydraulic system response speeds. Synchronizing the drive and brake systems reduces the deviation between the actual and target longitudinal wheel forces, enabling precise tracking of the neutral steering target.

[0178] a) Torque vector distribution between the motor and the hydraulic brake system

[0179] Ignoring the dynamic characteristics of the wheels, the torque of each wheel can be expressed as

[0180] T i =F xi R e +F fi R e (28)

[0181] Where T i is the wheel torque.

[0182] Based on the high selection principle, the front and rear motor torques are calculated. The ideal motor torque can be expressed as

[0183]

[0184] In the formula and are the ideal torques of the front and rear axle motors respectively; T fl 、T fr 、T rl and T rr They are the left front wheel torque, right front wheel torque, left rear wheel torque and right rear wheel torque respectively.

[0185] The ideal braking torque distribution strategy for the left and right wheels is shown in Algorithm 1. Since the front and rear axle distribution strategies are the same, the algorithm only shows the front axle braking torque distribution strategy.

[0186]

[0187]

[0188] The process of torque vector distribution between the motor and the hydraulic brake system is summarized as follows. Since the calculation method for the front and rear axles is the same, the front axle is used as an example:

[0189] 1. Calculate the ideal torque of the left and right wheels: Calculate the ideal torque of each wheel based on the ideal longitudinal force of the left and right wheels output by the neutral steering optimization control and formula (28);

[0190] 2. Calculate the front axle motor torque: According to formula (29), the front axle wheel torque is equal to twice the maximum torque of the left and right vehicle wheels, which can be converted into motor torque according to the speed reducer transmission ratio;

[0191] 3. Calculate the ideal braking torque of one wheel according to Algorithm 1: If the desired torque of the left wheel is greater than that of the right wheel, the left wheel is not braked, and the ideal braking torque of the right wheel is If the desired torque of the left wheel is less than that of the right wheel, the ideal braking torque of the left wheel is The right wheel is not braked.

[0192] b) Wheel cylinder brake pressure delay compensation control based on Smith predictor

[0193] Since the wheel braking torque calculated by Algorithm 1 is an ideal value, the braking force actually generated by the hydraulic system will be delayed and attenuated, which will greatly reduce the tracking accuracy of the desired longitudinal force and affect the vehicle's neutral steering control effect. Therefore, the actual response characteristics of the system need to be considered when designing the control method.

[0194] In order to eliminate the delay of the hydraulic system, improve its response speed, and achieve the response synchronization of the motor and hydraulic system, a delay compensation control scheme based on Smith predictor is proposed. Figure 7 As shown in Figure 2, the overall solution includes a Smith predictor and sliding mode feedback control. The Smith predictor introduces an estimation compensation link to eliminate the effects of time lag, thereby improving system response speed. Sliding mode control (SMC) is used to reduce the deviation between the ideal and actual tire longitudinal force values, thereby improving the tracking accuracy of the ideal tire force.

[0195] exist Figure 7 In the equation, P0(s) describes the characteristics of the hydraulic brake system without time delay; C(s) is the compensation controller; It is an estimation model for the response characteristics of the hydraulic brake system; is the time-lagged estimation model; is the time constant; s is the complex variable in the Laplace transform. To simplify the difficulty of control scheme design, it can be considered that the estimation models in the Smith predictor are all known quantities, that is, The actual estimation model can be obtained through offline data identification or online estimation.

[0196] Smith Predictor:

[0197] Considering SMC as a disturbance, the closed-loop Smith predictive control system is analyzed without disturbance. The closed-loop transfer function of the system can be expressed as

[0198]

[0199] SMC controller:

[0200] The system tracking error is defined as the deviation between the ideal longitudinal force of the wheel calculated by the optimization control and the actual longitudinal force The sliding surface and the convergence rate are as follows.

[0201]

[0202] In the formula is the tire longitudinal force tracking deviation; s3 is the sliding surface formed by the tire longitudinal force deviation.

[0203] The braking torque compensation value is

[0204]

[0205] In the formula is the braking torque compensation value; is the actual torque output by the motor; c3 and ζ3 are the parameters of the sliding mode controller; is the derivative of the tire force deviation sliding surface.

[0206] Figure 12The effects of the motor, hydraulic brake system, and hydraulic braking of Smith predictive control are described when tracking the same target torque. It can be seen that the braking torque response characteristics output by the Smith predictor are basically consistent with the motor response characteristics.

[0207] 4) Vehicle posture coordination control based on weighted pseudo-inverse.

[0208] By adjusting the vehicle body posture, the vertical load distribution on the tires can be changed, thereby changing the vehicle's steering characteristics. This section assumes that the vehicle is traveling on a structured road and does not consider the control of the vertical vibration of the vehicle body.

[0209] a) Anti-pitch and anti-roll moment generation: The damping force output by a semi-active suspension is strongly dependent on the shock absorber compression rate and cannot actively generate actuating forces to counteract changes in vehicle attitude. Therefore, it is not suitable for tracking vehicle attitude angles. This section aims to minimize the rate of change of pitch and roll angles. A semi-active suspension vehicle attitude controller is designed to reduce vertical wheel load variations and ensure that each wheel fully utilizes tire adhesion.

[0210] The system tracking error is defined as the pitch angular velocity and roll angular velocity being 0.

[0211]

[0212] The sliding surface and the convergence rate are as follows

[0213]

[0214] The anti-pitching moment and anti-rolling moment are

[0215]

[0216] In the formula and M φ are the anti-pitch moment and anti-roll moment respectively; φ is the body roll angle; m s is the sprung mass; h rc is the distance from the center of mass to the roll center; g is the acceleration due to gravity; I x is the moment of inertia around the x-axis; M φ is the anti-roll moment generated by the semi-active suspension system; c φ and k φ are the equivalent damping coefficient and equivalent roll stiffness respectively; I y is the moment of inertia around the y-axis; is the vehicle body pitch angle; k sf 、k sr 、c sf and c sr are the front suspension spring stiffness, rear suspension spring stiffness, front suspension shock absorber damping, and rear suspension shock absorber damping; Zsf 、Z sr 、Z uf and Z ur They are the vertical displacement of the vehicle body above the front axle, the vertical displacement of the vehicle body above the rear axle, the vertical displacement of the front wheels, and the vertical displacement of the rear wheels.

[0217] b) Weighted pseudo-inverse distribution of anti-pitch and roll moments: The anti-pitch and roll moments calculated by the sliding mode controller need to be generated by the damping forces of the four shock absorbers. The weighted pseudo-inverse distribution method is as follows.

[0218]

[0219] Where ρ represents the distribution coefficient of the anti-roll moment between the front and rear axles; F dfl 、F dfr 、F drl 、F drr are the damping forces for the left front, right front, left rear, and right rear semi-active suspension, respectively. The anti-roll moment modifies the tire cornering stiffness by affecting the vertical loads on the left and right tires, thereby changing the vehicle's steering characteristics. Given the difficulty in quantifying the relationship between vehicle steering characteristics and the distribution coefficient, a proportional-integral control method is used to distribute the anti-roll moment between the front and rear axles. The specific formula is as follows.

[0220] ρ=K P2 Δα+K I2 ∫Δα (37)

[0221] Where Δα=α f -α r ;K P2 and K I2 For controller parameters.

[0222] (1) For dual-motor four-wheel drive electric vehicles, a torque vector optimization distribution control method that coordinates the front and rear dual motors with a four-wheel hydraulic brake system is proposed. This scheme realizes the application of torque vector control on dual-motor four-wheel drive vehicles, and the control effect of dual-motor four-wheel drive vehicles is basically the same as that of distributed drive vehicles.

[0223] (2) In order to improve the tracking effect of the neutral steering target, considering the difference in response characteristics between the motor and the hydraulic brake system in the actuator, the response speed of the hydraulic brake system is improved based on the Smith predictor, and the synchronous execution of the motor and hydraulic brake is achieved to ensure the accurate tracking of the desired neutral steering target.

[0224] 1. Existing technical solutions improve the stability and safety of the vehicle by tracking the yaw rate and the sideslip angle of the center of mass. However, under extreme working conditions, the yaw rate and the sideslip angle of the center of mass will conflict, making it difficult to fully utilize the vehicle's performance limit. This application utilizes the principle that when the front and rear tires reach the road adhesion limit at the same time during neutral steering, the tire lateral force can be maximized, and proposes a hybrid coordinated control scheme that uses neutral steering characteristics to improve the lateral motion limit of a dual-motor four-wheel drive vehicle. First, with the front and rear wheel sideslip angles being equal as the control target, a neutral steering four-wheel ideal longitudinal force optimization method is designed, and then a torque vector optimization distribution control method that coordinates the motor and hydraulic braking system is established to track the four-wheel ideal longitudinal force, achieve vehicle neutral steering, and improve the vehicle's lateral motion limit. 2. Existing technical solutions usually ignore the response characteristic differences between the motor and the hydraulic braking system. The response characteristic differences between the actuators will result in low control command tracking accuracy, making it difficult to fully utilize the vehicle's performance limit. This application considers the differences in response characteristics between the motor and hydraulic brake system. Based on Smith predictive control theory, a hydraulic brake system response prediction model is constructed. The hydraulic braking force output based on the model's predictions is then used to preemptively eliminate hydraulic system response delays, significantly improving the hydraulic brake system's response speed. Synchronous execution of the motor and hydraulic brake system reduces the deviation between the actual and target longitudinal wheel forces, enabling precise tracking of the neutral steering target.

[0225] This application also provides detailed theoretical proof that neutral steering can fully utilize the vehicle's lateral motion limit.

[0226] Based on the tire adhesion ellipse theory and the analytical solution of the optimization problem, it is proved that the vehicle can achieve the maximum lateral motion limit during neutral steering. The specific proof process is as follows:

[0227] Simplified vehicle model and its force relationship as follows Figure 8 As shown in , when the vehicle is turning at high speed (e.g., in emergency obstacle avoidance), the tire force is at its limit, and the tire longitudinal force, lateral force, and vertical load satisfy the adhesion ellipse relationship. Furthermore, when the vehicle is driving normally, the vertical loads on the front and rear wheels are proportional to each other, and the adhesion ellipses of the front and rear wheels can be expressed in the same coordinate system, as Figure 9 shown.

[0228] Based on the equation of the ellipse, the equations for the longitudinal and lateral forces of the front and rear wheels are established as follows.

[0229]

[0230] Where F x1 and F y1 are the longitudinal force and lateral force of the front wheel respectively; F x2 and F y2are the longitudinal force and lateral force of the rear wheel respectively; a and b are the semi-major axis and semi-minor axis of ellipse 1 respectively; c and d are the semi-major axis and semi-minor axis of ellipse 2 respectively.

[0231] There is a certain proportional relationship between the vertical loads of the front and rear wheels, that is,

[0232]

[0233] In addition, the sum of the longitudinal forces of the front and rear wheels when the vehicle is driving should meet the driving requirements.

[0234] F x1 +F x2 =e (40)

[0235] Where e is the total longitudinal force requirement. Considering vehicle safety and economy, the front and rear axle motors should be driven or braked simultaneously during normal driving. The front and rear axle motors must not operate in opposite states.

[0236] In order to achieve a higher lateral limit for the vehicle during high-speed cornering, it is necessary to maximize the lateral forces of the front and rear tires. Therefore, the above problem can be transformed into an optimization problem, and the conditions for maximizing the lateral forces of the tires can be determined based on the solution of the optimization problem. Combining the established elliptic equation with the known conditions, the mathematical model of the optimization problem can be expressed as

[0237] max F y1 +F y2

[0238]

[0239] Furthermore, according to the optimization goal and the equality constraint, the objective function of the original optimization problem can be simplified to

[0240]

[0241] By taking the derivative of formula (42) and setting the derivative equation equal to 0, the solution to the original optimization problem can be obtained.

[0242] The two solutions obtained are

[0243]

[0244] when hour, At this time, the front and rear axle motors operate in opposite states, with one motor driving and the other braking. This numerical solution does not conform to the actual operating conditions and must be discarded.

[0245] Therefore, the analytical solution of the original optimization problem is At this time, the tire longitudinal force and lateral force satisfy the following relationship

[0246]

[0247] At this time, the relationship between the longitudinal and lateral forces of the front and rear tires is as follows: Figure 10 shown.

[0248] When the sum of the vehicle's lateral forces is maximum, the relationship between the front and rear wheel longitudinal and lateral forces is as shown in formula 44 and Figure 10 As shown, when the front and rear wheel lateral forces simultaneously reach their adhesion limits, the sum of the lateral forces reaches its maximum, and the vehicle's lateral motion limit is highest. Analyzing from the perspective of vehicle steering characteristics, when the vehicle is in neutral steering, the front and rear wheel lateral forces can simultaneously reach their adhesion limits. In summary, the sum of the vehicle's lateral forces is maximum during neutral steering, and the vehicle's lateral motion limit is highest. This completes the proof.

[0249] The present application also provides an application scenario, which applies the above-mentioned drive-brake-suspension coordinated control method. Specifically: The drive-brake-suspension coordinated control method provided in this embodiment can be applied in navigation. The navigation scenario includes the navigation route acquisition link, navigation route planning and driving according to the navigation route link; the vehicle status, the driver's steering wheel angle and the drive / brake pedal input information enter the driving according to the navigation route from the navigation route planning link. The drive-brake-suspension coordinated control method provided in this embodiment belongs to the control turning link in driving according to the navigation route.

[0250] Based on the same inventive concept, embodiments of the present application further provide a device for implementing the aforementioned drive-brake-suspension coordinated control. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the drive-brake-suspension coordinated control device provided below can be found in the aforementioned limitations of the drive-brake-suspension coordinated control method and will not be further elaborated here.

[0251] In an exemplary embodiment, a drive-brake-suspension coordinated control device is provided, comprising:

[0252] The driving demand analysis and decision module is used to obtain vehicle status, driver steering wheel angle and drive / brake pedal input information; determine the driver's required torque, control mode and neutral steering target based on the vehicle status, the driver's steering wheel angle and the drive / brake pedal input information, and send the driver's required torque, the control mode and the neutral steering target to the hybrid coordination control module; the neutral steering target is equal front and rear wheel slip angles.

[0253] The hybrid coordinated control module is used to determine the front and rear motor torques according to the driver's required torque and vehicle status under normal working conditions, and to determine the four-wheel cylinder brake pressure using sliding mode control according to the vehicle status; under extreme working conditions, the front and rear motor torques and four-wheel cylinder brake pressures are determined according to the driver's required torque, vehicle status and neutral steering target using torque vector optimization distribution control and Smith predictor control methods, and the semi-active suspension damping force is determined using weighted pseudo-inverse distribution body posture coordinated control.

[0254] The vehicle actuator is used to control the front and rear axle motors, the hydraulic braking system and the semi-active suspension system according to the front and rear motor torques, the four-wheel cylinder braking pressure and the damping force.

[0255] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 11 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store front and rear motor torque, four-wheel cylinder braking force and damping force data of the semi-active suspension. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a drive brake suspension collaborative control method is implemented.

[0256] Those skilled in the art will understand that Figure 11 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present application and does not constitute a limitation on the computer device to which the solution of the present application is applied. A specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the above-mentioned method embodiments when executing the computer program.

[0257] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the above-mentioned method embodiments when executed by a processor.

[0258] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the above method embodiments are implemented.

[0259] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0260] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0261] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0262] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0263] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A drive-brake-suspension coordinated control method, characterized in that: The driving, braking and suspension coordinated control method includes: The driving demand analysis and decision module obtains vehicle status, driver's steering wheel angle and drive / brake pedal input information; The driving demand analysis and decision module determines a driver's required torque, a control mode, and a neutral steering target based on the vehicle state, the driver's steering wheel angle, and the drive / brake pedal input information, and sends the driver's required torque, the control mode, and the neutral steering target to the hybrid coordination control module; the control mode is a normal operating condition or an extreme operating condition; and the neutral steering target is equal front and rear wheel slip angles. Under normal working conditions, the hybrid coordinated control module determines the front and rear motor torques according to the driver's required torque and vehicle state, and determines the four-wheel cylinder brake pressure using sliding mode control according to the vehicle state; under extreme working conditions, the hybrid coordinated control module determines the front and rear motor torques and four-wheel cylinder brake pressure according to the driver's required torque, vehicle state and neutral steering target using torque vector optimization distribution control and Smith predictor control method, determines the semi-active suspension damping force using weighted pseudo-inverse distribution body posture coordinated control, and sends the front and rear motor torques, the four-wheel cylinder brake pressure and the semi-active suspension damping force to the vehicle actuator.

2. The drive-brake-suspension coordinated control method according to claim 1, characterized in that: The driving demand analysis and decision module determines the driver's required torque, control mode, and neutral steering target based on the vehicle state, the driver's steering wheel angle, and the drive / brake pedal input information, specifically including: The driving demand analysis and decision module determines the driver's required torque according to the driving / brake pedal input information, the road adhesion coefficient of the vehicle state, and the maximum output torque of the motor at the current wheel speed; The driving demand analysis and decision module determines front and rear wheel slip angle deviations according to the front and rear wheel slip angles of the vehicle state; the front and rear wheel slip angle deviations are used to determine a neutral steering target; The driving demand analysis and decision module determines a control mode according to the front and rear wheel slip angle deviations, the vehicle state and the driver's steering wheel angle.

3. The drive-brake-suspension coordinated control method according to claim 2, characterized in that: The driving demand analysis and decision module determines the control mode according to the front and rear wheel slip angle deviations, the vehicle state, and the driver's steering wheel angle, specifically including: The driving demand analysis and decision module identifies the vehicle's turning state based on the vehicle state and the driver's steering wheel angle, and obtains the vehicle's turning state; the turning state is turning or going straight; The driving demand analysis and decision module determines a control mode according to the front and rear wheel sideslip angle deviations and the turning state.

4. The drive-brake-suspension coordinated control method according to claim 3, characterized in that: The driving demand analysis and decision module determines the control mode according to the front and rear wheel slip angle deviation and the turning state, specifically including: The driving demand analysis and decision module performs an opening count initialization; the initialization controls the opening count sampling counter and the trigger flag to zero; The driving demand analysis and decision module determines whether the front and rear wheel slip angle deviation exceeds a first deviation threshold, and obtains a first determination result; If the first judgment result is yes, the driving demand analysis and decision module controls the start-up counting sampling counter to start counting and record the number of start-up sampling cycles; The driving demand analysis and decision module determines whether the number of start sampling cycles continuously meets the first set number of cycles, and obtains a second judgment result; If the second judgment result is yes, the driving demand analysis and decision module sets the trigger flag to 1, controls the extreme working condition to be turned on, and initializes the closing count; the initial state of the control mode is the normal working condition; the closing count is initialized to control the closing count sampling counter to zero; If the second judgment result is no, then return to the step of "the driving demand analysis and decision module determines whether the front and rear wheel sideslip angle deviation exceeds the first deviation threshold"; If the first judgment result is no, return to step "driving demand analysis and decision module starts counting initialization"; The driving demand analysis and decision module determines whether the front and rear wheel slip angle deviation is less than a second deviation threshold and the turning state is straight ahead, thereby obtaining a third determination result; If the third judgment result is yes, the driving demand analysis and decision module controls the closing count sampling counter to start counting and record the closing sampling cycle number; The driving demand analysis and decision module determines whether the number of shutdown sampling cycles continuously meets a second set number of cycles to obtain a fourth determination result; If the fourth judgment result is yes, the driving demand analysis and decision module sets the trigger flag to 0, and controls the normal operating condition to be turned on; If the fourth judgment result is no, returning to step "the driving demand analysis and decision module determines whether the front and rear wheel sideslip angle deviation is less than the second deviation threshold and the turning state is straight ahead, to obtain a third judgment result"; If the third judgment result is no, return to step "initialize the closing count".

5. The drive-brake-suspension coordinated control method according to claim 1, characterized in that: The hybrid coordination control module determines the front and rear motor torques according to the driver's required torque and the vehicle state under normal operating conditions, and determines the four-wheel cylinder brake pressure using sliding mode control according to the vehicle state, specifically including: When the control mode is normal working conditions, the hybrid coordinated control module determines the front and rear motor torques under normal working conditions based on the longitudinal acceleration of the vehicle state, the vertical load ratio of the front and rear axles, and the driver's required torque; and uses sliding mode control to calculate the four-wheel cylinder brake pressure under normal working conditions based on the ideal slip rate and actual slip rate of the four wheels of the vehicle state.

6. The drive-brake-suspension coordinated control method according to claim 1, characterized in that: The hybrid coordinated control module determines the front and rear motor torques and four-wheel cylinder brake pressures using torque vector optimization distribution control and Smith predictor control methods based on the driver's required torque, vehicle state, and neutral steering target under extreme operating conditions, and determines the semi-active suspension damping force using weighted pseudo-inverse distribution vehicle posture coordinated control. Specifically, the control includes: When the control mode is extreme working condition, the hybrid coordination control module calculates the total longitudinal force requirement of the vehicle when driving according to the driver's required torque; The hybrid coordination control module calculates an additional yaw moment required for neutral steering according to the front and rear tire slip angle deviations in the vehicle state; The hybrid coordination control module constructs a first objective function according to the total longitudinal force requirement of the vehicle and the additional yaw moment required for neutral steering; The hybrid coordination control module constructs a second objective function based on the tire longitudinal force in the vehicle state; The hybrid coordinated control module uses the active set method to solve the first and second objective functions to obtain the ideal longitudinal forces of the four wheels under extreme working conditions; The hybrid coordination control module calculates the front and rear motor torques and the four-wheel ideal wheel cylinder brake pressures according to the four-wheel ideal longitudinal forces; The hybrid coordination control module calculates the four-wheel wheel cylinder brake pressure based on the Smith predictor according to the four-wheel ideal wheel cylinder brake pressure, the four-wheel ideal longitudinal force and the four-wheel actual longitudinal force in the vehicle state; The hybrid coordinated control module calculates the semi-active suspension damping force under extreme working conditions using a weighted pseudo-inverse allocation method based on the extreme working condition trigger flag, neutral steering target, vehicle body pitch and vehicle roll attitude.

7. A drive-brake-suspension coordinated control device, characterized in that: The driving, braking and suspension coordinated control device comprises: a driving demand analysis and decision module, which is used to determine the driver's required torque, control mode, and neutral steering target based on the vehicle state, the driver's steering wheel angle, and the drive / brake pedal input information, and send the driver's required torque, control mode, and neutral steering target to the hybrid coordination control module; the control mode is a normal operating condition or an extreme operating condition; and the neutral steering target is equal front and rear wheel slip angles; a hybrid coordinated control module, the hybrid coordinated control module being configured to determine the front and rear motor torques based on the driver's required torque and vehicle state under normal operating conditions, and to determine the four-wheel cylinder brake pressures based on the vehicle state using sliding mode control; and, under extreme operating conditions, the hybrid coordinated control module being configured to determine the front and rear motor torques and the four-wheel cylinder brake pressures based on the driver's required torque, vehicle state, and neutral steering target using torque vector optimization distribution control and Smith predictor control methods, and to determine the semi-active suspension damping force using weighted pseudo-inverse distribution vehicle posture coordinated control, and to transmit the front and rear motor torques, the four-wheel cylinder brake pressures, and the semi-active suspension damping force to vehicle actuators; The vehicle actuator is used to control the front and rear axle motors, the hydraulic braking system and the semi-active suspension system according to the front and rear motor torques, the four-wheel cylinder braking pressure and the damping force.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the drive-brake-suspension coordinated control method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the drive-brake-suspension coordinated control method according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the drive-brake-suspension coordinated control method according to any one of claims 1 to 6 is implemented.

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