AMPC control method for hub motor suspension system of electric vehicle

Abstract

The invention relates to the technical field of vehicle engineering and automatic control, in particular to an AMPC control method for a hub motor suspension system of an electric vehicle. The method comprises the following steps: S1, obtaining pavement preview information; s2, inputting road excitation in the information into a prediction model, and outputting dynamic response of the hub motor suspension system; s3, generating a control signal based on an adaptive model prediction control method; and S4, adjusting the dynamic response by using a control signal. By means of the method, the hub motor suspension system can perceive the road condition in advance through the road surface preview information, control signals are generated and hub motor input and suspension parameters are adjusted in combination with the AMPC method, the response speed, stability and comfort of the system are improved, and therefore the control requirements under different road conditions are met.

Description

Technical Field

[0001] The present invention relates to the field of vehicle engineering and automatic control technology, and in particular to an AMPC control method of a wheel hub motor suspension system of an electric vehicle. Background Art

[0002] With the rapid development of electric vehicle technology, in-wheel motor electric vehicles have gradually become a research hotspot and an ideal choice in the automotive industry due to their flexible layout, high transmission efficiency and high space utilization. The in-wheel motor is integrated inside the wheel, which reduces the complexity of the traditional transmission system and improves the driving efficiency and handling performance of the vehicle. However, with the widespread application of in-wheel motors, new technical challenges have also been brought about.

[0003] At present, the suspension vibration reduction technology for in-wheel motor electric vehicles has made some progress, mainly focusing on dealing with the impact of increased unsprung mass on the suspension system. However, most of these existing vibration reduction technologies only consider the increase in unsprung mass or completely ignore the problems caused by it, and do not fully address the deterioration of suspension performance caused by the unbalanced electromagnetic excitation force caused by the weight of the in-wheel motor itself and the eccentricity of the stator and rotor. In addition, electric vehicles can be equipped with many advanced sensors, such as lidar and binocular cameras, which can provide vehicles with rich environmental and road information, but current vibration reduction technologies have not yet fully utilized the data from these high-precision sensors to optimize suspension control.

[0004] Therefore, the existing technology has the following main problems: first, it does not fully consider the adverse effects of the increase in unsprung mass and unbalanced electromagnetic excitation force caused by the wheel hub motor on the suspension performance; second, it fails to fully utilize the advanced sensors on electric vehicles to optimize the suspension control. In order to solve these problems, it is necessary to propose a new suspension vibration reduction technology that not only considers the impact of the special structure of the wheel hub motor on the suspension system, but also fully utilizes the data of sensors such as binocular cameras to achieve more accurate suspension vibration reduction control. Summary of the invention

[0005] The purpose of the present invention is to overcome the problem of reduced suspension vibration reduction performance caused by increased unsprung mass and unbalanced electromagnetic excitation force in existing wheel hub motor electric vehicles, and provide an AMPC control method for the wheel hub motor suspension system of an electric vehicle. By introducing binocular vision to obtain road information and combining the adaptive model predictive control (AMPC) strategy, the damping force of the suspension system is dynamically adjusted, thereby effectively reducing the suspension dynamic deflection, wheel dynamic load and vehicle body acceleration peak, and improving the vehicle's driving smoothness and stability.

[0006] In order to achieve the above-mentioned object of the invention, the present invention provides the following technical solutions:

[0007] An AMPC control method for a wheel hub motor suspension system of an electric vehicle comprises the following steps:

[0008] S1. Obtain road preview information;

[0009] S2. Input the road surface excitation in the information into the prediction model, and output the dynamic response of the wheel hub motor suspension system;

[0010] S3. Generate a control signal based on an adaptive model predictive control method;

[0011] S4. Adjust the dynamic response using a control signal.

[0012] Preferably, the road surface preview information in step S1 includes road surface elevation information and road surface grade information, and obtaining road surface excitation according to the road surface preview information includes the following steps:

[0013] S11. Acquire binocular visual image information;

[0014] S12. Processing the binocular visual image information using a semi-global block matching algorithm to generate a disparity map;

[0015] S13. Obtaining a depth map according to the disparity map, and obtaining road elevation information from the depth map;

[0016] S14. Discretize the road elevation information to obtain road excitation.

[0017] The road elevation information is obtained through the binocular camera, the disparity map is used to generate the depth map and the road information is discretized, so that the road surface characteristics ahead can be accurately obtained and quantified, providing accurate basic data for subsequent dynamic response control.

[0018] Preferably, step S2 includes establishing a prediction model of the wheel hub motor suspension system, the prediction model including an electromagnetic model of the wheel hub motor and a dynamic model of the suspension system; taking the road surface excitation as an input of the prediction model and outputting a dynamic response of the wheel hub motor suspension system, including the following steps:

[0019] S21. Establish an electromagnetic model of the hub motor and obtain the electromagnetic excitation force of the hub motor;

[0020] S22. Establish a dynamic model of the suspension system;

[0021] S23. The electromagnetic excitation force of the hub motor, the damping force of the suspension system and the road excitation are used as inputs of the dynamic model to output the dynamic response of the hub motor suspension system; the dynamic response includes sprung mass acceleration, suspension dynamic deflection and tire dynamic travel.

[0022] Through this model, the system can simulate more realistic dynamic behaviors based on physical characteristics, improving the accuracy of the control strategy.

[0023] Preferably, the electromagnetic model of the hub motor is based on an outer rotor permanent magnet synchronous motor, and the electromagnetic excitation force is calculated using a finite element simulation method, and the air gap magnetic field force wave is determined based on the Maxwell tensor method. The use of finite element simulation can accurately calculate the distribution of the air gap magnetic flux, thereby more accurately describing the electromagnetic characteristics of the hub motor under different working conditions. The selection of the outer rotor permanent magnet synchronous motor structure combined with finite element analysis helps to accurately capture the impact of the unbalanced electromagnetic force caused by the eccentricity of the motor on the suspension system. The radial force wave and tangential force wave obtained by the Maxwell tensor method enable the control strategy to more accurately predict and compensate for the dynamic changes of the electromagnetic force. This is crucial for the adaptive control of the suspension system, because the unbalanced electromagnetic force will directly affect the dynamic deflection of the suspension, thereby affecting the stability of the vehicle.

[0024] Preferably, the dynamic model of the suspension system adopts the dynamic model of the three-degree-of-freedom semi-active suspension system of a quarter vehicle. By adopting the dynamic model of the three-degree-of-freedom semi-active suspension system of a quarter vehicle, the calculation complexity is simplified, which is suitable for real-time control applications, thereby improving the response speed and control effect of the system under different road conditions.

[0025] Preferably, the suspension system also includes a magnetorheological damper, and the control signal acts on the magnetorheological damper to adjust the damping force of the magnetorheological damper, and the dynamic response is adjusted according to the damping force of the magnetorheological damper. By adjusting the damping force of the magnetorheological damper through the control signal, the suspension system can adjust the damping in real time according to different road conditions, thereby providing a more adaptable suspension response capability. Under different road conditions, the magnetorheological damper can respond quickly and change the damping characteristics of the suspension, thereby adjusting the sprung mass acceleration, suspension dynamic deflection and tire dynamic travel, so that the vehicle can remain stable on complex roads and reduce the vertical vibration of the vehicle body. This dynamic adjustment capability can improve the roll control of the vehicle when driving at high speed or turning, and improve the ride smoothness and comfort.

[0026] Preferably, the adaptive model predictive control method dynamically adjusts the weight matrix in the model predictive control method based on different road surface grade information, including the weight matrix of the input variables and the weight matrix of the state variables. By dynamically adjusting the weight matrix of the cost function according to the road surface grade information, the system can adapt to the control requirements under different road conditions, realize adaptive optimization control, and enable the vehicle to maintain optimal performance under various complex road conditions.

[0027] Preferably, the adaptive model predictive control method in S3 also includes a cost function, and the cost function dynamically adjusts the weight matrix of the input variables and the state variables according to the road surface grade information to adjust the dynamic response of the hub motor. The dynamic response is adjusted by introducing the cost function and adjusting the weight matrix of the input variables and the state variables. This method provides a flexible optimization means for the suspension system, which can make trade-offs according to different control requirements to achieve optimal dynamic performance.

[0028] Preferably, the adaptive model predictive control also includes applying constraints to the wheel hub motor suspension system, including: applying range constraints to the damping force of the magnetorheological damper, and applying amplitude constraints to the eccentric distance of the wheel hub motor. These constraints ensure the safety and stability of the system under actual physical conditions, prevent the limits of mechanical or electrical components from being exceeded, and ensure the reliable operation of the suspension system.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] The AMPC control method of the wheel hub motor suspension system of an electric vehicle described in the present invention obtains the road surface information ahead in advance through a binocular camera. The system can pre-adjust the suspension system based on the predicted information before the vehicle contacts the road surface, forming a "preview control". This method can respond to changes in road conditions more quickly, reduce the sudden impact of road excitation on the suspension, and effectively improve the adaptability of the suspension system. The introduction of the adaptive model predictive control (AMPC) strategy enables the system to generate the optimal control signal by predicting the future state to achieve precise adjustment of the dynamic deflection of the suspension. The AMPC method maintains the optimal control effect under different road conditions by weighing multiple state variables (such as vehicle vertical acceleration, wheel dynamic load, etc.), thereby ensuring the smoothness and stability of the vehicle. The control signal not only adjusts the damping characteristics of the suspension system, but also controls the input parameters of the wheel hub motor, which can effectively offset the unbalanced electromagnetic force generated by the wheel hub motor under dynamic eccentricity. This method reduces the negative impact of electromagnetic force on the suspension system, reduces the vertical vibration of the vehicle, and makes the system more stable. Through the road information obtained by the binocular camera and the control signal generated by the MPC strategy, the suspension system can automatically adjust the response parameters under different road conditions, so that the vehicle maintains a low dynamic deflection during driving. Smaller dynamic deflection reduces body vibration, provides better ride comfort, and maintains the vehicle's handling stability under complex road conditions, further improving driving safety.

[0031] In summary, through the AMPC control method of the wheel hub motor suspension system of an electric vehicle described in the present invention, the wheel hub motor suspension system can use the binocular camera to obtain road surface information to perceive the road conditions in advance, and through adaptive adjustment of the wheel hub motor input and suspension parameters, the response speed, stability and comfort of the system are improved, thereby meeting the control requirements under different road conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a flow chart of an AMPC control method for a wheel hub motor suspension system of an electric vehicle in Example 1 of the present invention;

[0033] Figure 2 is a schematic diagram of the AMPC method in Example 2 of the present invention;

[0034] Figure 3 Schematic diagram of the Bouc-Wen model of the magnetorheological damper in Example 2 of the present invention;

[0035] Figure 4 is a flow chart of a characteristic test of a magnetorheological damper in Example 2 of the present invention;

[0036] Figure 5 is a damping force-displacement (Fx) curve of the magnetorheological shock absorber in Example 2 of the present invention;

[0037] Figure 6 Schematic diagram of the geometric relationship between the installation position of the binocular camera and its field of view in Example 2 of the present invention;

[0038] Figure 7 Schematic diagram of the process of obtaining road preview information of the binocular camera in Embodiment 2 of the present invention;

[0039] Figure 8 This is a schematic diagram of road elevation change in Example 2 of the present invention;

[0040] Fig. 9 Schematic diagram of a semi-active suspension system in Embodiment 2 of the present invention;

[0041] Fig.10 This is a time domain waveform diagram of the unbalanced electromagnetic force of the hub motor under dynamic eccentricity conditions in Example 2 of the present invention;

[0042] Fig.11 This is a preview flow chart of the AMPC control strategy in Example 2 of the present invention. DETAILED DESCRIPTION

[0043] The AMPC control method of the wheel hub motor suspension system of an electric vehicle provided by the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. However, this should not be understood as the scope of the above-mentioned subject matter of the present invention being limited to the following embodiments, and all technologies implemented based on the content of the present invention belong to the scope of the present invention. Combined with the following description, the advantages and features of the present invention will be clearer. It should be noted that the drawings are all in a very simplified form and use non-precise proportions, which are only used to conveniently and clearly assist in explaining the purpose of the embodiments of the present invention.

[0044] Example 1

[0045] This embodiment provides an AMPC control method for the wheel hub motor suspension system of an electric vehicle, aiming to solve the problem of vehicle body vibration caused by the imbalance of electromagnetic excitation force inside the wheel hub motor. The electromagnetic excitation force is the force caused by the eccentricity of the motor. Figure 1 As shown, the following steps are included:

[0046] An AMPC control method for a wheel hub motor suspension system of an electric vehicle comprises the following steps:

[0047] S1. Obtain road preview information;

[0048] S2. Input the road surface excitation in the information into the prediction model, and output the dynamic response of the wheel hub motor suspension system;

[0049] S3. Generate a control signal based on an adaptive model predictive control method;

[0050] S4. Adjust the dynamic response using a control signal.

[0051] Preferably, the road surface preview information in step S1 includes road surface elevation information and road surface grade information, and obtaining road surface excitation according to the road surface preview information includes the following steps:

[0052] S11. Acquire binocular visual image information;

[0053] S12. Processing the binocular visual image information using a semi-global block matching algorithm to generate a disparity map;

[0054] S13. Obtaining a depth map according to the disparity map, and obtaining road elevation information from the depth map;

[0055] S14. Discretize the road elevation information to obtain road excitation.

[0056] The road elevation information is obtained through the binocular camera, the disparity map is used to generate the depth map and the road information is discretized, so that the road surface characteristics ahead can be accurately obtained and quantified, providing accurate basic data for subsequent dynamic response control.

[0057] Preferably, step S2 includes establishing a prediction model of the wheel hub motor suspension system, the prediction model including an electromagnetic model of the wheel hub motor and a dynamic model of the suspension system; taking the road surface excitation as an input of the prediction model and outputting a dynamic response of the wheel hub motor suspension system, including the following steps:

[0058] S21. Establish an electromagnetic model of the hub motor and obtain the electromagnetic excitation force of the hub motor;

[0059] S22. Establish a dynamic model of the suspension system;

[0060] S23. The electromagnetic excitation force of the hub motor, the damping force of the suspension system and the road excitation are used as inputs of the dynamic model to output the dynamic response of the hub motor suspension system; the dynamic response includes sprung mass acceleration, suspension dynamic deflection and tire dynamic travel.

[0061] Through this model, the system can simulate more realistic dynamic behaviors based on physical characteristics, improving the accuracy of the control strategy.

[0062] Preferably, the electromagnetic model of the hub motor is based on an outer rotor permanent magnet synchronous motor, and the electromagnetic excitation force is calculated using a finite element simulation method, and the air gap magnetic field force wave is determined based on the Maxwell tensor method. The use of finite element simulation can accurately calculate the distribution of the air gap magnetic flux, thereby more accurately describing the electromagnetic characteristics of the hub motor under different working conditions. The selection of the outer rotor permanent magnet synchronous motor structure combined with finite element analysis helps to accurately capture the impact of the unbalanced electromagnetic force caused by the eccentricity of the motor on the suspension system. The radial force wave and tangential force wave obtained by the Maxwell tensor method enable the control strategy to more accurately predict and compensate for the dynamic changes of the electromagnetic force. This is crucial for the adaptive control of the suspension system, because the unbalanced electromagnetic force will directly affect the dynamic deflection of the suspension, thereby affecting the stability of the vehicle.

[0063] Preferably, the dynamic model of the suspension system adopts the dynamic model of the three-degree-of-freedom semi-active suspension system of a quarter vehicle. By adopting the dynamic model of the three-degree-of-freedom semi-active suspension system of a quarter vehicle, the calculation complexity is simplified, which is suitable for real-time control applications, thereby improving the response speed and control effect of the system under different road conditions.

[0064] Preferably, the suspension system also includes a magnetorheological damper, and the control signal acts on the magnetorheological damper to adjust the damping force of the magnetorheological damper, and the dynamic response is adjusted according to the damping force of the magnetorheological damper. By adjusting the damping force of the magnetorheological damper through the control signal, the suspension system can adjust the damping in real time according to different road conditions, thereby providing a more adaptable suspension response capability. Under different road conditions, the magnetorheological damper can respond quickly and change the damping characteristics of the suspension, thereby adjusting the sprung mass acceleration, suspension dynamic deflection and tire dynamic travel, so that the vehicle can remain stable on complex roads and reduce the vertical vibration of the vehicle body. This dynamic adjustment capability can improve the roll control of the vehicle when driving at high speed or turning, and improve the ride smoothness and comfort.

[0065] Preferably, the adaptive model predictive control method dynamically adjusts the weight matrix in the model predictive control method based on different road surface grade information, including the weight matrix of the input variables and the weight matrix of the state variables. By dynamically adjusting the weight matrix of the cost function according to the road surface grade information, the system can adapt to the control requirements under different road conditions, realize adaptive optimization control, and enable the vehicle to maintain optimal performance under various complex road conditions.

[0066] Preferably, the adaptive model predictive control method in S3 also includes a cost function, and the cost function dynamically adjusts the weight matrix of the input variables and the state variables according to the road surface grade information to adjust the dynamic response of the hub motor. The dynamic response is adjusted by introducing the cost function and adjusting the weight matrix of the input variables and the state variables. This method provides a flexible optimization means for the suspension system, which can make trade-offs according to different control requirements to achieve optimal dynamic performance.

[0067] Preferably, the adaptive model predictive control also includes applying constraints to the wheel hub motor suspension system, including: applying range constraints to the damping force of the magnetorheological damper, and applying amplitude constraints to the eccentric distance of the wheel hub motor. These constraints ensure the safety and stability of the system under actual physical conditions, prevent the limits of mechanical or electrical components from being exceeded, and ensure the reliable operation of the suspension system.

[0068] Example 2

[0069] This embodiment provides an AMPC control method for an electric vehicle's wheel hub motor suspension system, which is used to solve the problem of vehicle body vibration caused by the imbalance of electromagnetic excitation force inside the wheel hub motor. The principle of the AMPC method is as follows: Figure 2As shown, firstly, the front road preview information is obtained through the binocular camera to determine the road surface grade; then, the MPC weight matrix is ​​adjusted according to the road surface grade; then, the optimal control scheme is solved through the MPC prediction model, cost function and constraint conditions; finally, the optimization result of the optimal control scheme is used to adjust the current of the shock absorber to realize the dynamic vibration reduction adjustment of the suspension system. The AMPC control method described in this embodiment is implemented by the following specific steps:

[0070] S1. Characteristic test and modeling of magnetorheological damper

[0071] In order to accurately describe the dynamic characteristics of the magnetorheological damper, Figure 3 The Bouc-Wen model of the shock absorber shown in the figure uses the Bouc-Wen model to describe the mechanical characteristics of the magnetorheological shock absorber, and uses the genetic algorithm to optimize and identify the model parameters to accurately reflect the characteristics of the magnetorheological shock absorber. The obtained magnetorheological shock absorber characteristic model will be combined with the semi-active suspension system to be used for the dynamic adjustment of the suspension system under different control conditions. The magnetorheological shock absorber is selected as the research object, and its characteristic is that the damping force can be changed by current regulation.

[0072] like Figure 4 As shown, a simulation model of magnetorheological damper is established.

[0073] Through the shock absorber external characteristic test, different currents and excitation speeds are input to obtain the mechanical characteristic curve of the magnetorheological shock absorber. In the shock absorber external characteristic test, the movement stroke of the magnetorheological shock absorber is set to 40mm, and the shock absorber external characteristic test device is used to test it. A ±20mm sinusoidal excitation is applied to the magnetorheological shock absorber. The vibration frequencies are 0.2Hz, 0.68Hz, 1.39Hz, 2.48Hz and 3.12Hz, respectively, and the currents are 0.0A, 0.5A, 1.5A, 2.5A, 3.5A and 4.5A, respectively, to obtain the dynamic characteristic data of the magnetorheological shock absorber.

[0074] The data results of the shock absorber external characteristic test are combined with the shock absorber Bouc-Wen model, and the parameters are identified using the Matlab optimization toolbox to obtain the Bouc-Wen model parameter diagram that meets the requirements. Specifically, the model parameters are optimized through genetic algorithms to ensure that the simulation model can accurately reflect the actual characteristics of the magnetorheological shock absorber. The optimization results generate a Bouc-Wen model parameter curve that meets the requirements, ensuring the accuracy and reliability of the model.

[0075] The Bouc-Wen simulation model was built in Simulink and simulated at a frequency of 3.21 Hz. The accuracy of the model was verified by comparing the damping force-displacement (Fx) curve of the magnetorheological damper with the simulation results and the experimental data. Figure 5 The results show that the maximum error between the simulation and the actual test is 11.8%, and the dynamometer diagram presents a full line without voids or distortion, indicating that the simulation model can well describe the actual characteristics of the magnetorheological damper.

[0076] Through the above tests and simulation analysis, it can be confirmed that the simulation model of the magnetorheological damper has high accuracy and can be used for further research on semi-active suspension systems. This provides a reliable model basis for the design of control strategies and the optimization of dampers.

[0077] S2, preview system based on binocular camera

[0078] Figure 6 The installation position of the binocular camera in the vehicle and the geometric relationship of its field of view are shown. The binocular camera is fixedly installed in front of the vehicle and can collect road preview information in real time. This embodiment adopts a preview system based on a binocular camera. The process of obtaining road preview information is as follows: Figure 7 As shown, the specific process is as follows:

[0079] S2.1 Stereo Camera Calibration

[0080] First, the binocular camera is calibrated, using an A2 chessboard as the calibration board and 50 sets of calibration board graphics as the acquisition objects. The internal and external parameters and distortion correction parameters are obtained to correct the optical distortion of the lens and ensure the accuracy of subsequent data processing. Then, the calibrated binocular camera is used to capture the left and right perspective images of the road surface.

[0081] S2.2 Disparity map calculation

[0082] The semi-global block matching algorithm (SBGM) is used to process the left and right view images taken by the binocular camera to generate a disparity map. The disparity map reflects the difference in pixel displacement between the two images and is the key to obtaining three-dimensional information.

[0083] S2.3 Three-dimensional reconstruction and depth information extraction

[0084] The depth of each pixel is calculated from the disparity map to generate a depth map. The depth information can be used to determine the distance between each point on the road and the camera.

[0085] S2.4 Obtaining road elevation information

[0086] Get road elevation information from the depth map:

[0087] Z h =Z i +∫adt

[0088] Among them, Z his the road elevation information, a is the vertical acceleration;

[0089]

[0090] Among them, Z s represents the distance between the camera and the ground when the vehicle is stationary, C p is the vehicle pitch angle, C c is the angle between the camera and the horizontal direction, C o is the angle between the road cameras, C i is the sum of the above three angles, Z d is the distance between the camera and the ground, X d is the distance between the camera and the center of mass of the vehicle. This information can provide accurate preview data for the suspension system and support real-time adjustment of the suspension state. Figure 8 shown.

[0091] S3. Establish electromagnetic model of hub motor

[0092] When constructing the electromagnetic model of the hub motor, the magnetic field distribution in the air gap needs to be considered. The magnetic field generated by the permanent magnet and the armature reaction will form radial and tangential electromagnetic excitation forces between the stator and the rotor. These electromagnetic excitation forces may cause vibrations of the motor and its fixed structure. Due to the special layout of the hub motor, eccentricity is prone to occur between the stator and the rotor in the case of road excitation, manufacturing errors and suspension load changes, resulting in uneven distribution of the air gap magnetic field density and unbalanced electromagnetic excitation forces.

[0093] S31. Modeling Method

[0094] The 24-stage 27-slot outer rotor permanent magnet synchronous motor was selected as the research object, and parametric modeling was performed using ANSYS Maxwell software to calculate the air gap magnetic flux distribution, electromagnetic excitation force waveform and electromagnetic excitation force of the wheel hub motor. Among them, the wheel hub motor of the electric vehicle adopts the outer rotor surface permanent magnet synchronous motor model. The system includes an outer rotor surface permanent magnet synchronous motor installed inside the wheel. Its outer rotor is directly connected to the wheel hub, and the rotating magnetic field generated by the inner stator interacts with the no-load magnetic field of the outer rotor permanent magnet to directly drive the wheel to rotate and achieve efficient power transmission. The air gap magnetic field is formed by the superposition of the permanent magnet and the armature magnetic field, which optimizes the motor performance.

[0095] S32. Calculation of electromagnetic excitation force

[0096] The air gap magnetic flux B(B) in the Cartesian coordinate system x ,B y ,B z ) is converted into the air gap magnetic flux B(B) in the cylindrical coordinate system γ ,Bθ ,B z ), and the electromagnetic excitation force is calculated based on this, and the radial air gap magnetic field and the tangential air gap magnetic field conversion equation can be obtained as follows:

[0097]

[0098] Among them, α is the mechanical angle when the motor rotates, B r is the radial air gap magnetic field, B t is the tangential air gap magnetic field.

[0099] According to the Maxwell tensor method, the radial force wave P of the air gap magnetic field is derived. r and tangential force wave P t ,

[0100]

[0101] These two force waves reflect the distribution of electromagnetic excitation force per unit area.

[0102] The hub motor will cause uneven air gap due to the stator-rotor offset, resulting in vertical unbalanced magnetic pulling force. The formula for calculating the vertical unbalanced electromagnetic excitation force of the hub motor is as follows:

[0103]

[0104] Among them, F b is the vertical unbalanced electromagnetic excitation force of the hub motor, L is the axial length of the hub motor, r is the air gap radius, and u0 is the vacuum permeability.

[0105] S33, simulation verification

[0106] The motor speed is set to 600RPM and the eccentricity is set to 25%. Finite element simulation is performed using Maxwell to obtain the unbalanced electromagnetic excitation force. Based on the simulation results, a Simulink model is established using the Matlab fitting toolbox to obtain the dynamic eccentric force characteristics controlled by vehicle speed and eccentricity, such as Fig.10 As shown, as the vehicle travels, the unbalanced electromagnetic force generated by the eccentricity of the hub motor rotor and stator changes periodically.

[0107] S4, AMPC control of suspension system

[0108] Based on the two-degree-of-freedom suspension model of the traditional 1 / 4 vehicle, a dynamic model of the three-degree-of-freedom semi-active suspension system of the electric vehicle driven by the wheel hub motor is established. This model more accurately reflects the dynamic response of the electric vehicle under various road conditions.

[0109] S41. Establish the dynamic model of the suspension system

[0110] The mass of the wheel hub motor is included in the calculation of the unsprung mass, and the electromagnetic excitation force generated by the wheel hub motor is considered to more accurately simulate the dynamic response of the vehicle under different road conditions. Figure 7 The dynamic model of a three-degree-of-freedom semi-active suspension system is presented to simulate the dynamic response of the vehicle under different road conditions.

[0111] Semi-active suspension system schematic diagram Fig. 9 As shown, according to D'Alembert's theorem, the differential equation of motion of the 1 / 4 vehicle model is:

[0112]

[0113] in:

[0114] M s : sprung mass, representing the mass of the vehicle body;

[0115] M u : Unsprung mass, including the mass of the hub motor stator, shaft and its accessories;

[0116] M t : The mass of the hub motor rotor and tire;

[0117] K s : Ignoring the damping M s Under the conditions, K s Represents the suspension stiffness, which is used to simulate the restoring force of the spring;

[0118] K b : Stiffness of hub motor bearing;

[0119] K t : The stiffness of the tire, describing the elastic properties of the tire;

[0120] C s : suspension damping, which represents the damping coefficient of the magnetorheological shock absorber;

[0121] F s : The damping force of the magnetorheological shock absorber is the adjustment object of the control system;

[0122] F b : The electromagnetic excitation force of the hub motor;

[0123] Z g : Road surface incentive;

[0124] Z s , Z u , Z t : represent the sprung mass, unsprung mass and vertical displacement of the tire respectively;

[0125] Z s -Zu : represents the dynamic deflection of the suspension;

[0126] Z u -Z g : Indicates the tire dynamic travel;

[0127] represent the sprung mass, unsprung mass and tire speed respectively;

[0128] They represent the sprung mass, unsprung mass and tire acceleration respectively.

[0129] S42, AMPC control strategy design and implementation

[0130] The AMPC control strategy is designed to predict the state changes of the suspension system at future moments based on road feature information and suspension dynamics model.

[0131] First, based on the road elevation information obtained by the camera, the system can predict the road conditions ahead and adjust the suspension damping force F s This is especially important in road conditions with more discrete shocks, because the high resolution of the binocular camera can better capture road surface details and achieve precise suspension adjustment.

[0132] Next, the three-degree-of-freedom dynamic model of the suspension system provides AMPC with accurate dynamic prediction capabilities, and calculates the optimal control strategy by inputting the current suspension state (such as displacement, speed) and preview road surface data.

[0133] In this way, by combining the real-time road information acquisition of the binocular camera with the dynamic analysis of the three-degree-of-freedom suspension model, this technical solution can dynamically adjust the suspension parameters according to different road conditions, reduce the peak values ​​of suspension dynamic deflection, wheel dynamic load and body acceleration, and ensure that the suspension system can maintain the best vibration reduction effect under different road conditions. For example, when the road surface is relatively flat, the control strategy will focus on improving driving comfort; when the road surface is relatively bumpy, the stability of the suspension and the control of the body acceleration are given priority. Under various road conditions, the system can dynamically adjust the suspension parameters, reduce the peak values ​​of suspension dynamic deflection, wheel dynamic load and body acceleration, and significantly improve the driving smoothness and stability of the vehicle.

[0134] Preferably, the weight coefficients of the MPC under different road surfaces at 60 km / s are obtained. When the vehicle is traveling and encounters a corresponding road surface grade, the road surface grade recognition system installed on the vehicle is used to identify the current road surface grade. After receiving the feedback road surface grade, the AMPC controller will convert the weight coefficient of the MPC controller into the weight coefficient under the corresponding road surface motor to improve the driving smoothness of the vehicle under different road surfaces. Among them, the road surface grade recognition system installed on the vehicle itself adopts the technology disclosed in "[1] Qin, Yechen, Dong, et al. Road profile classification for vehiclesemi-active suspension system based on adaptive neuro-fuzzy inference system[C] / / 2015 54th Ieee Conference on Decision and Control(cdc),2015:1533-1538."

[0135] S5. Establishing a predictive control model

[0136] S51. Establishment of state space equations

[0137] According to the evaluation index of the semi-active suspension dynamics model: sprung mass acceleration Suspension dynamic deflection Z s -Z u and tire travel Z u -Z g The feedback information is used to control the damping force in the suspension model through the AMPC controller, thereby improving the vehicle's driving smoothness and stability.

[0138] The evaluation index of the semi-active suspension dynamics model is selected as the output variable Y of the state space equation, and the state variable X, input variable U and disturbance variable W of the system are defined as:

[0139]

[0140] U=F s

[0141]

[0142] According to the dynamic equation, the system state equation can be expressed as:

[0143]

[0144] Y=Cx+Du

[0145] Among them, A, B, C, D, and E are the state matrices at time k in the discrete state. Specifically, A represents the state matrix, B represents the input matrix, C represents the output matrix, D represents the feedforward matrix, and E represents the disturbance input matrix. The formulas of the above matrices are expressed as follows:

[0146]

[0147]

[0148] The specific values ​​of A, B, C, D, and E depend on the physical parameters of the suspension system.

[0149] S52, Discretization Processing

[0150] When the vehicle is driving, the road surface information in front of the vehicle is first collected by the binocular camera. The road surface information obtained by the binocular camera is input into the controller as preview data to form a prediction set in the MPC (model predictive control) strategy for predicting the future state of the vehicle. The road surface information includes the road surface elevation information Z h and the corresponding discretized state (Z g ) k , where Z g Represents the road surface stimulus at the current moment.

[0151] As the vehicle continues to drive, when it enters the previously predicted road section, the road information of this section will be transformed from the predicted set to the actual road excitation, which will directly act on the suspension system. This excitation is used as one of the disturbance inputs of the system in the model predictive control to adjust the control strategy of the suspension and optimize the vibration reduction performance of the vehicle.

[0152] In the execution of the MPC method, the first step is to discretize all state variables so that the state of each discrete time step k corresponds to a specific road excitation (Z g ) k Therefore, MPC can use the current road elevation information Z at each time k h and road excitation (Z g ) k As disturbance input to the system, the response of the suspension system is gradually optimized.

[0153] In order to meet the discrete time solution requirements of model predictive control (MPC), the forward Euler method is used to discretize the system state equation to obtain the discretized state equation:

[0154] X(k+1)=(I+hA)X(k)+(hB)U(k)+(hE)W(k)

[0155] Y(k+1)=(hC)U(k)+(hD)W(k)

[0156] Where h is the discrete time, A d =I+hA,B d =hB,E d =hE,C d =hC,D d =hD, X(k) represents the measured value of the system state at time k, W(k) represents the measured value of the system disturbance at time k, Y(k) represents the system output at time k, U(k) represents the system control quantity at time k, X(k+1) represents the state variable of the system at time k+1, (Z g ) k The road height history output in S2, the preview process of the AMPC control strategy is as follows Fig.11 As shown. Then the predicted state and predicted output of the system at time k are:

[0157]

[0158]

[0159] Among them, △ is the perturbation output matrix, N p is the prediction step length, N c To control the step length.

[0160] S53. Definition of cost function

[0161] In order to optimize the control performance, a cost function J is defined to evaluate the deviation between the system state and the control input:

[0162]

[0163] can be transformed into:

[0164]

[0165] in, Represents the reference output, Q is the weight matrix of state variables, indicating the degree of attention to different states; R is the weight matrix of input variables, indicating the constraints on the change of control input; T represents the transposition of the matrix.

[0166] The specific forms of weight matrices Q and R are:

[0167] Q=diag[q1,q2,q3]

[0168] R=r

[0169] q1 is the sprung mass acceleration The weight coefficient, q2 represents the suspension dynamic deflection Z s -Z u The weight coefficient, q3 represents the tire dynamic travel Zu -Z g The weight coefficient of ; R is the weight matrix of the input variables, which represents the constraint on the change of the control input, R = r.

[0170] S54. Setting of constraints

[0171] The magnetorheological damper suspension is a semi-active suspension, which cannot meet the optimal damping control force requirements obtained by MPC. Therefore, when solving the optimal damping control force, the solution range should be limited, that is, the constraint conditions of the damping control force should be given.

[0172] Depend on Figure 5 It can be seen that the magnetorheological damper F s The upper limit of the absolute value of the curve damping force is 2300, and the lower limit is -2300, which is the actual constraint of the magnetorheological damper. The air gap width of the hub motor used in this embodiment is 0.8mm, which is the eccentric distance constraint of the hub motor. Therefore, when solving the optimal control force, the damping force F of the magnetorheological damper needs to be s Nonlinear constraints and amplitude constraints of suspension dynamic deflection are imposed to meet the physical limitations and safety requirements of the actual system. Specific constraints include:

[0173] (1) Actual constraints of magnetorheological damper: |F s |≤2300N;

[0174] (2) Hub motor eccentricity constraint: e≤0.8; where e=Z t -Z u .

[0175] These constraints reflect the physical properties of the MR damper and the effect of the wheel hub motor eccentricity on the system.

[0176] S55, Adaptive settings

[0177] Based on the driving environment of electric vehicles and considering some typical road conditions, the AMPC controller transforms the corresponding MPC weight matrix corresponding to the level of road conditions, as shown in Table 1:

[0178] Table 1 MPC weight matrix

[0179]

[0180] By adjusting the control weight according to different road conditions, the semi-active control effect can be effectively changed, which can improve the handling stability of the suspension system while reducing the improvement in ride comfort.

[0181] Those skilled in the art will appreciate that the embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware.

[0182] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described 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.

[0183] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. An AMPC control method for a wheel hub motor suspension system of an electric vehicle, characterized in that: The following steps are involved: S1. Obtain road preview information; S2. Input the road surface excitation in the information into the prediction model, and output the dynamic response of the wheel hub motor suspension system; S3. Generate a control signal based on an adaptive model predictive control method; S4. Adjust the dynamic response using a control signal.

2. The AMPC control method of the wheel hub motor suspension system of an electric vehicle according to claim 1, characterized in that: The road surface preview information in step S1 includes road surface elevation information and road surface grade information. Acquiring road surface excitation according to the road surface preview information includes the following steps: S11. Acquire binocular visual image information; S12. Processing the binocular visual image information using a semi-global block matching algorithm to generate a disparity map; S13. Obtaining a depth map according to the disparity map, and obtaining road elevation information from the depth map; S14. Discretize the road elevation information to obtain road excitation.

3. The AMPC control method of the wheel hub motor suspension system of an electric vehicle according to claim 2, characterized in that: Step S2 includes establishing a prediction model of the wheel hub motor suspension system, the prediction model including an electromagnetic model of the wheel hub motor and a dynamic model of the suspension system; taking the road surface excitation as an input of the prediction model and outputting a dynamic response of the wheel hub motor suspension system, including the following steps: S21. Establish an electromagnetic model of the hub motor and obtain the electromagnetic excitation force of the hub motor; S22. Establish a dynamic model of the suspension system; S23. The electromagnetic excitation force of the hub motor, the damping force of the suspension system and the road excitation are used as inputs of the dynamic model to output the dynamic response of the hub motor suspension system; the dynamic response includes sprung mass acceleration, suspension dynamic deflection and tire dynamic travel.

4. The AMPC control method of the wheel hub motor suspension system of an electric vehicle according to claim 3, characterized in that: The electromagnetic model of the hub motor is based on an outer rotor permanent magnet synchronous motor, uses a finite element simulation method to calculate the electromagnetic excitation force, and determines the air gap magnetic field force wave based on the Maxwell tensor method.

5. The AMPC control method of the wheel hub motor suspension system of an electric vehicle according to claim 3, characterized in that: The dynamic model of the suspension system adopts the dynamic model of a three-degree-of-freedom semi-active suspension system of a 1 / 4 vehicle.

6. The AMPC control method of the wheel hub motor suspension system of an electric vehicle according to claim 5, characterized in that: The suspension system further includes a magnetorheological damper, the control signal acts on the magnetorheological damper to adjust the damping force of the magnetorheological damper, and the dynamic response is adjusted according to the damping force of the magnetorheological damper.

7. The AMPC control method of the wheel hub motor suspension system of an electric vehicle according to any one of claims 1 to 6, characterized in that: The adaptive model predictive control method dynamically adjusts the weight matrix in the model predictive control method based on different road surface grade information, including the weight matrix of the input variables and the weight matrix of the state variables.

8. The AMPC control method of the wheel hub motor suspension system of an electric vehicle according to claim 7, characterized in that: S3 The adaptive model predictive control method also includes a cost function, which dynamically adjusts the weight matrix of input variables and state variables according to the road surface grade information to adjust the dynamic response of the hub motor suspension system.

9. The AMPC control method of the wheel hub motor suspension system of an electric vehicle according to claim 8, characterized in that: The adaptive model predictive control also includes applying constraints to the wheel hub motor, including: applying a range constraint to the damping force of the magnetorheological damper, and applying an amplitude constraint to the eccentric distance of the wheel hub motor.

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