A Multi-Hub Motor Euler-Lagrange Torque Dynamic Vector Distribution System and its Control Method

Abstract

This invention discloses a multi-hub motor Euler-Lagrange torque dynamic vector distribution system and its control method. The system treats the four hub motors of a distributed drive electric vehicle as a multi-hub motor electromechanical system. The torque distribution system of the multi-hub motor electromechanical system includes an integrated Euler-Lagrange torque controller and a driving assistance system. The method includes: pre-processing modeling of the four hub motors; pre-setting the Euler-Lagrange torque distribution control law; sampling the state variables of the multi-hub motor electromechanical system; determining the desired equilibrium point of the multi-hub motor electromechanical system; calculating the injection of dynamic interconnection coefficients and injection of friction damping coefficients; outputting the optimal given torque by the Euler-Lagrange torque distribution controller; and coordinating the modulation controller and the multi-half-bridge arm group driver to drive each hub motor. This invention can effectively adapt to various complex road conditions and improve the handling stability of distributed drive electric vehicles.

Description

Technical Field

[0001] This invention belongs to the field of torque distribution and collaborative control of multi-hub motors and is applied to distributed drive systems. Specifically, it relates to the method of dynamic vector distribution of torque of multi-hub motors in distributed drive systems based on Euler-Lagrange torque and its control. Background Technology

[0002] In recent years, distributed drive systems have demonstrated significant potential for application in multi-hub motor traction of electric vehicles due to their advantages such as independent four-wheel traction and high-efficiency transmission, offering broad prospects for improving the economy and stability of electric vehicles. Currently, the traction requirements of multi-hub motors in electric vehicles not only satisfy the responsiveness and robustness of individual hub motors but also comprehensively consider the coupling factors between multiple hub motors to ensure multi-hub motor coordination and goal consistency, thereby achieving vehicle handling stability. Therefore, conducting research on multi-hub motor traction control is of great significance for achieving stable control of electric vehicles.

[0003] Currently, distributed drive electric vehicles mostly employ hierarchical control. On one hand, the upper-level controller often considers factors such as tire adhesion and uses advanced intelligent algorithms to achieve torque distribution. Chinese patent publication CN117246343 A mentions a torque distribution control method for electric wheel drive vehicles. This method considers real-time vehicle data such as vehicle speed and lateral acceleration, uses fuzzy control to determine the vehicle's motion mode, and then uses the Lévy-Flight-Chaotic Mapping Particle Swarm Optimization algorithm to obtain the optimal torque to drive each wheel hub motor. However, this method has problems: it does not fully consider the electromagnetic and mechanical coupling between the wheel hub motors in a distributed drive electric vehicle caused by the presence of rigid components in the vehicle. Incoordination between the wheel hub motors can easily lead to tire slippage and dragging, significantly reducing the accuracy of the upper-level controller. On the other hand, currently, multi-motor controllers and multi-actuators act as lower-level slave controllers, receiving torque commands from the upper-level controller and coordinating the drive of multiple wheel hub motors. However, the existence of multi-motor controllers and multi-actuators can lead to communication interference and coupling, and this redundant structure further increases the risk of failure.

[0004] Therefore, considering the electromechanical coupling effects among multi-hub motors and addressing the communication coupling issues in redundant structures with multiple actuators and controllers, an Euler-Lagrange torque distribution system for multi-hub motors is designed. Based on the Euler-Lagrange nonlinear model, integrating the electrical and mechanical equations of the multi-hub motors, and taking the voltage stability of the multi-hub motors as a constraint, an Euler-Lagrange torque distribution controller is designed to output the optimal torque in real time, improving the control accuracy and collaborative performance of the multi-hub motor electromechanical system. The system receives the optimal torque command through a modulation controller and outputs the corresponding optimal voltage vector, achieving collaborative drive of four hub motors based on a multi-half-bridge arm group driver. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies. Addressing the mechanical and electromagnetic coupling inherent in the nonlinear multi-hub motor electromechanical system of distributed drive electric vehicles due to the presence of the transmission system, air suspension system, brakes, tires, and half-shafts, this invention proposes a multi-hub motor Euler-Lagrange torque distribution system and its control method. Taking multi-hub motor cooperative control as the control objective, a Euler-Lagrange torque distribution controller is constructed based on the Euler-Lagrange principle. It uses the expected speed, actual speed, and phase current of each hub motor as inputs, and stable voltage tracking of each hub motor as a constraint. Based on adjustment factors such as the injected interconnection coefficient and injected friction damping coefficient, and centered around the Euler-Lagrange torque distribution control law, the controller outputs the optimal given torque of each hub motor. This effectively reduces the impact of electromechanical coupling while distributing the torque of each hub motor in real time, effectively reducing tire slippage and drag caused by mismatched movements of the hub motors, and significantly improving the handling stability of distributed drive electric vehicles. Furthermore, the optimal torque signal from the four hub motors is received through two modulation controllers, and a unified motor control voltage signal is output through multi-voltage vector modulation, which significantly reduces the number of motor controllers and actuators. Finally, the multi-half-bridge arm group driver achieves coordinated driving of each hub motor through PWM drive signal.

[0006] The technical solution adopted in this invention is:

[0007] A torque distribution system for a multi-hub motor electromechanical system in a distributed drive electric vehicle is designed. Due to the presence of the transmission system, air suspension system, brakes, tires, and half-shafts in a distributed drive electric vehicle, strong electromechanical coupling exists between two hub motors on the same side, leading to dragging and slippage. Therefore, the four hub motors of a distributed drive electric vehicle are considered as a multi-hub motor electromechanical system. The torque distribution system of this multi-hub motor electromechanical system includes an integrated Euler-Lagrange torque controller and a driving assistance system. The integrated Euler-Lagrange torque controller consists of an Euler-Lagrange torque distribution controller, two modulation controllers, and two multi-half-bridge arm group drivers. The driving assistance system receives and records the driving environment around the vehicle from sensors, assisting the driver in safe driving operations: issuing an alarm when the vehicle deviates from its lane, providing a corrective torque to bring the vehicle back to the main lane, and responding when the road ahead suddenly changes direction. The system provides protective active braking when encountering obstacles and issues an alarm when driver fatigue is detected. The Euler-Lagrange torque distribution controller outputs the optimal given torque for each hub motor in real time based on the expected speeds of the four hub motors (hub motors I, II, III, and IV) obtained from driver operation information, the sampled actual speeds of the four hub motors, and the feedback values ​​of three-phase current. A modulation controller and a multi-half-bridge arm group driver act as a set of lower-level slave controllers to achieve direct cooperative driving of two hub motors on the same side of the distributed drive electric vehicle, while the two hub motors on the other side are directly cooperatively driven by another set of lower-level slave controllers. The modulation controller receives the optimal given torque of each hub motor and outputs the optimal modulation voltage, which is controlled by the multi-half-bridge arm group driver. In addition, the two modulation controllers and the two multi-half-bridge arm group drivers have completely identical structures.

[0008] Furthermore, a torque distribution control method for a multi-hub motor electromechanical system based on an Euler-Lagrange torque distribution controller, two modulation controllers, and two multi-half-bridge arm group drivers is designed. The Euler-Lagrange torque distribution controller, as the upper-level controller, mainly considers the electromechanical coupling factors between the two hub motors on the same side of the distributed drive electric vehicle. Based on the Euler-Lagrange principle, an Euler-Lagrange torque distribution control law is designed. The inputs are the feedback values ​​of the driver's operation information and the sampled state variables of the four hub motors. The injected interconnect matrix coefficients and injected friction damping matrix coefficients are used as coordination factors, and the voltage stability tracking of each hub motor is used as a constraint to achieve real-time optimal torque distribution. The modulation controllers receive the optimal given torque information and provide PWM drive signals to the multi-half-bridge arm group drivers via a torque / flux observer, hysteresis comparator, sector selection, and multi-voltage vector switching table module to collaboratively drive the four hub motors.

[0009] Taking hub motor-I and hub motor-II as two hub motors on the same side, and hub motor-III and hub motor-IV as two hub motors on the same side as examples, hub motor-I and hub motor-II are driven by modulation controller-I and multi-half-bridge arm group driver-I, while hub motor-III and hub motor-IV are driven by modulation controller-II and multi-half-bridge arm group driver-II. The required steps for the torque distribution control method of the multi-hub motor electromechanical system are described below:

[0010] Step 1: Preprocessing for modeling four hub motors: Establish the dynamic equations and voltage equations of four hub motors that take into account the rigid coupling effects caused by elastic components such as air suspension system, frame, and bearings in distributed drive electric vehicles. Based on the Euler-Lagrange principle, construct the Euler-Lagrange nonlinear model of the mechanical and electrical integrated multi-hub motor electromechanical system.

[0011] Step 2: Presetting the Euler-Lagrange Torque Distribution Control Law: First, based on the Euler-Lagrange nonlinear model of the multi-hub motor electromechanical system, construct the desired energy function and closed-loop multi-hub motor electromechanical system function. Assume that, given the injection interconnection matrix, injection friction damping matrix, and desired equilibrium point of the multi-hub motor electromechanical system, a closed-loop control feedback law satisfying the closed-loop multi-hub motor electromechanical system function can be found. Second, considering the coupling relationships of various state variables, define the injection interconnection matrix and injection friction damping matrix. Based on the actual speed of each hub motor, the q-axis current, and the d-axis current of each hub motor, determine the desired equilibrium point of the multi-hub motor electromechanical system. Finally, based on the coefficients of the injection interconnection matrix and injection friction damping matrix, the closed-loop control feedback law, and the desired equilibrium point of the multi-hub motor electromechanical system, solve for the voltage and torque tracking control laws. With voltage stability tracking as a constraint, the Euler-Lagrange torque distribution control law can be obtained by inverse solving.

[0012] Step 3: Sampling of state variables for the multi-hub motor electromechanical system: The state variables of the multi-hub motor electromechanical system include the actual speeds of the four hub motors and the d-axis and q-axis currents obtained through coordinate transformation. The rotary transformer feeds back the actual speeds of hub motors I, II, III, and IV. The analog-to-digital converter samples the three-phase currents of hub motors I, II, III, and IV.

[0013] Step 4: Determine the desired balance point of the multi-hub motor electromechanical system: Determine the desired speed of each hub motor based on the driver's operation information, and obtain the desired d-axis current and q-axis current of each hub motor by controlling id=0 based on the actual speed and desired speed of each hub motor.

[0014] Step 5: Calculation of injected dynamic interconnection coefficient and injected friction damping coefficient: The difference between the state variables obtained by subtracting the state variables of the multi-hub motor electromechanical system in Step 3 and the corresponding expected values ​​in Step 4 is used as input. The injected dynamic interconnection coefficient and injected friction damping coefficient can be solved by taking the local minimization of the closed-loop multi-hub motor electromechanical system function at the expected equilibrium point and the asymptotic stability in the dynamic process as the constraint condition.

[0015] Step 6: The Euler-Lagrange torque distribution controller outputs the optimal given torque: Input the state variables of the multi-hub motor electromechanical system sampled in Step 3, the difference of the state variables obtained in Step 5, the injected dynamic interconnection coefficient, and the injected friction damping coefficient. Calculate the optimal given torque for each hub motor in real time using the Euler-Lagrange torque distribution control law preset in Step 2.

[0016] Step 7: The modulation controller and the multi-half-bridge arm group driver work together to drive each hub motor: The modulation controller-I and modulation controller-II are based on torque control. They receive the optimal given torque obtained in step 6 and output the optimal modulation voltage. The multi-half-bridge arm group driver receives the optimal modulation voltage and realizes the coordinated drive of the four hub motors through the PWM drive signal of the arm being turned on and off.

[0017] Furthermore, in step one, the rigid coupling influencing factor of the multi-hub motor electromechanical system is generated by the torsional coupling torque of the two hub motors on the same side. The actual speed difference of the four hub motors of the distributed drive electric vehicle, measured by multiple rotary transformers, is used to calculate the disturbance torsional coupling torque through elastic system mechanics analysis. , Let be the disturbance torsional coupling torque of the i-th hub motor, where i = 1, 2, 3, 4, corresponding to hub motor I, hub motor II, hub motor III, and hub motor IV, respectively. The disturbance torsional coupling torques generated by hub motor I and hub motor II are equal in magnitude and opposite in direction; the disturbance torsional coupling torques generated by hub motor III and hub motor IV are equal in magnitude and opposite in direction. The Euler-Lagrange nonlinear model of the multi-hub motor electromechanical system uses the d-axis current, q-axis current, and actual rotational speed of the four hub motors to represent 12 state variables. A matrix of interconnection coefficients consisting of 12 rows and 12 columns and a 12x12 matrix of damping coefficients The electrical and mechanical internal structure of a multi-hub motor electromechanical system is represented by the d-axis voltage, q-axis voltage, and external torque acting on each hub motor, which represent the inputs of the multi-hub motor electromechanical system. Energy function of multi-hub motor electromechanical system The partial derivative as output The external torque is coupled to the disturbance torque. and load torque The composition is given by i = Ⅰ, Ⅱ, Ⅲ, Ⅳ;

[0018] Furthermore, in step two, the closed-loop multi-hub motor electromechanical system function... ,in The desired energy function of the multi-hub motor electromechanical system, and the closed-loop feedback law are as follows: In the formula, x * It is the expected equilibrium point of the state variables of the multi-hub motor electromechanical system. It is a 12-row, 12-column matrix of expected interconnection coefficients. It is a 12x12 matrix of expected damping coefficients. It is the desired energy function of the multi-hub motor electromechanical system, and D is the positive definite inertia matrix containing the internal parameters of each hub motor;

[0019] Furthermore, in step two, the interconnect matrix is ​​injected. and interconnection coefficient matrix Composition of expected interconnection coefficient matrix The injected interconnection matrix asymptotically stabilizes the 12 state variables within the multi-hub motor electromechanical system by injecting state variable decoupling coefficients. The injected interconnection matrix coefficients consist of 8 injected dynamic interconnection coefficients and 12 injected undetermined interconnection coefficients. These undetermined interconnection coefficients represent the coupling relationship between the current state variable and the rotational speed state variable. The injected friction damping matrix... and damping coefficient matrix Composition of the expected damping coefficient matrix Inject friction damping matrix The strong correlation with the speed of the multi-hub motor indicates that the injection of external dissipation coefficients achieves asymptotic stability of the state variables of the multi-hub motor electromechanical system; the injected friction damping matrix coefficients consist of 4 injected dynamic friction damping coefficients and 4 injected self-tuning friction damping coefficients.

[0020] Furthermore, in step two, the optimal given torque of each hub motor output by the Euler-Lagrange torque distribution control law is... for In the formula, It is the moment of inertia of the i-th hub motor. It is the electromagnetic torque of the i-th hub motor. It is the actual rotational speed of the i-th hub motor. Let m be the m-th state variable. , Let be the current regulation coefficient of the i-th hub motor, including the injected dynamic interconnection coefficient. It is the compensation term for the i-th hub motor, and i=1,2,3,4, which correspond to hub motor I, hub motor II, hub motor III, and hub motor IV, respectively.

[0021] Furthermore, in step five, the injected undetermined interconnection coefficient is obtained by decoupling the current and speed terms in the voltage tracking control law and is linearly represented by the injected dynamic interconnection coefficient; the injected dynamic interconnection coefficient and the injected dynamic friction damping coefficient can be tuned by taking the local minimization of the closed-loop multi-hub motor electromechanical system function at the desired equilibrium point and the asymptotic stability in the dynamic process as the constraint condition; while the injected self-tuning friction damping coefficient is an adjustable parameter.

[0022] Furthermore, in step seven, the modulation controller-I and modulation controller-II input the flux given value according to the detailed design parameters of the four hub motors. Both include a torque / flux observer, sector selection, hysteresis comparator and multi-voltage vector switch module. The optimal modulation voltage is represented by an octal number, and its corresponding binary number represents the on and off state of each arm of the multi-half-bridge arm group driver.

[0023] The beneficial effects of this invention are as follows:

[0024] (1) The present invention designs a multi-hub motor Euler-Lagrange torque distribution system. The integrated Euler-Lagrange torque controller, consisting of an Euler-Lagrange torque distribution controller, two modulation controllers and two multi-half-bridge arm group drivers, realizes the coordinated control of the multi-hub motor electromechanical system of distributed drive electric vehicles. It effectively reduces the number of motor controllers and actuators in the lower-level slave controller, reduces the length of communication optical fiber, and improves communication transmission efficiency.

[0025] (2) In step two, this invention is based on the Euler-Lagrange principle, taking into account the mechanical and electromagnetic coupling effects between the distributed drive hub motors, and establishes an Euler-Lagrange nonlinear model of the multi-hub motor electromechanical system. With the voltage stability tracking of each hub motor as a constraint, an Euler-Lagrange torque distribution control law is constructed. On the basis of ensuring voltage stability tracking, the electromechanical coupling effects between each hub motor are reduced. By outputting the optimal torque of each hub motor in real time, it can effectively adapt to various complex road conditions, improve the handling stability of the distributed drive electric vehicle, and reduce the drag and slippage caused by the mismatch of the hub motors of the vehicle.

[0026] (3) In step five of this invention, for the electromechanical system, an interconnection coefficient matrix and a friction damping coefficient matrix considering the coupling of the external speed and internal current of each hub motor are constructed, and a method for adjusting the dynamic interconnection coefficient and the dynamic friction damping coefficient is designed. The adjustment method is based on the decoupling of the external speed and internal current of the electromechanical system, which not only minimizes the local equilibrium point of the closed-loop system, but also makes the closed-loop system quickly and asymptotically stable during the dynamic process.

[0027] (4) In step five of this invention, a self-tuning friction damping coefficient is designed to reduce the mismatch motion caused by the difference in parameters of each hub motor by adjusting the tuning parameters, thereby improving the cooperative performance of each hub motor. Attached Figure Description

[0028] Figure 1 This is a structural block diagram of the Euler-Lagrange torque distribution system for multi-hub motors described in this invention;

[0029] Figure 2 This is a structural block diagram of another multi-hub motor Euler-Lagrange torque distribution system according to the present invention;

[0030] Figure 3 for Figure 1 and Figure 2 Block diagram of the control method for the integrated Euler-Lagrange torque controller;

[0031] Figure 4 for Figure 3 The control principle block diagram of the Euler-Lagrange torque distribution controller in the diagram;

[0032] Figure 5 for Figure 3 Block diagram of the modulation controller and multi-bridge arm group driver in the middle; Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. The specific examples described herein are for illustrative purposes only and are not intended to limit the invention.

[0034] This invention designs an Euler-Lagrange torque distribution system for a multi-hub motor, see [link / reference]. Figure 1 and Figure 2 The structural block diagram shown illustrates a distributed drive electric vehicle composed of hub motors I, II, III, and IV, which are controlled collaboratively by an integrated Euler-Lagrange torque controller and a driver assistance system. Additionally, hub motors I and II can represent hub motors on the same side of the front and rear wheels. (See also...) Figure 1 It can also represent the hub motor on the same side of the left and right wheels, participating in... Figure 2 The specific application choice depends on the internal structure of the actual distributed drive electric vehicle. For example, when the coupling effect between the front and rear wheels of a distributed drive electric vehicle is significant, hub motor I and hub motor II represent the front and rear hub motors, and the rigid coupling factor between the front and rear hub motors is mainly considered. An integrated Euler-Lagrange torque controller collects driver operation information, distributes the torque of each hub motor in real time, improves the load-carrying capacity of the multi-hub motor electromechanical system, and realizes coordinated control of each hub motor. Meanwhile, the driver assistance system collects information about the vehicle's external environment through various types of sensors, actively warns the driver when there is a potential threat, and directly brakes the hub motors in emergency situations through the modulation controller and the multi-semi-bridge arm group driver to ensure vehicle driving safety.

[0035] This invention designs an Euler-Lagrange torque distribution control method for multi-hub motors, see [link to relevant documentation]. Figure 3 The control method block diagram shows that the integrated Euler-Lagrange torque controller consists of an Euler-Lagrange torque distribution controller, two modulation controllers, and two multi-half-bridge arm group drivers. The Euler-Lagrange torque distribution controller determines the desired speeds of the four hub motors (hub motors I, II, III, and IV) based on driver operation information. , , , The system uses the d-axis and q-axis currents of the four hub motors obtained by coordinate transformation from the phase currents of each hub motor sampled by the analog-to-digital converter, and the actual rotational speeds of the four hub motors sampled by the rotary transformer to output the optimal given torque signal T for the four hub motors in real time. * e1 T * e2 T * e3 and T * e4 The modulation controller-I and the multi-half-bridge arm group driver-I act as a set of lower-level slave controllers. The modulation controller receives the optimal given torque signal and the phase current of each hub motor sampled by the analog-to-digital converter, and outputs the optimal modulation voltage signal. The multi-half-bridge arm group driver then uses PWM drive signals to achieve coordinated driving of hub motor-I and hub motor-II according to the switching state of each arm corresponding to the optimal modulation voltage. The other two hub motors on the same side, hub motor-III and hub motor-IV, are directly driven by the modulation controller-II and the multi-half-bridge arm group driver-II. In addition, the two modulation controllers and the two multi-half-bridge arm group drivers have completely identical structures. Figure 3 The required steps for the torque distribution control method of the multi-hub motor electromechanical system are described below:

[0036] Step 1: Preprocessing for modeling four hub motors: Establish the dynamic equations and voltage equations of four hub motors that take into account the rigid coupling effects caused by elastic components such as air suspension system, frame, and bearings in distributed drive electric vehicles. Based on the Euler-Lagrange principle, construct the Euler-Lagrange nonlinear model of the mechanical and electrical integrated multi-hub motor electromechanical system.

[0037] The rigid coupling influencing factors of the multi-hub motor electromechanical system are generated by the torsional coupling torque of the two hub motors on the same side, and the actual rotational speeds of the four hub motors of the distributed drive electric vehicle are measured by multiple rotary transformers. , , and The speed difference between hub motor I and hub motor II is calculated by subtraction. The speed difference between hub motor III and hub motor IV Through elastic system mechanics analysis, the disturbance torsional coupling torque was calculated. In the formula, Let be the disturbance torsional coupling torque of the i-th hub motor, where i = 1, 2, 3, 4, corresponding to hub motor I, hub motor II, hub motor III, and hub motor IV, respectively. In the formula, the disturbance torsional coupling torque experienced by hub motor I is... The disturbance torsional coupling torque experienced by hub motor II Equal amplitude, opposite force direction; the disturbance torsional coupling torque experienced by hub motor III The disturbance torsional coupling torque experienced by hub motor IV Equal amplitude, opposite direction of force , It is the stiffness coupling coefficient.

[0038] Furthermore, considering the influence of rigid coupling, the reconstructed dynamic equations of the multi-hub motor electromechanical system are as follows: In the formula It is the moment of inertia of the i-th hub motor. It is the electromagnetic torque of the i-th hub motor. It is the load torque of the i-th hub motor, and i = 1, 2, 3, 4, which correspond to hub motor I, hub motor II, hub motor III, and hub motor IV, respectively;

[0039] Furthermore, the voltage equation of the multi-hub motor electromechanical system is as follows: In the formula, and These are the d-axis and q-axis inductances of the i-th hub motor. and These are the d-axis and q-axis currents of the i-th hub motor. and These are the d-axis and q-axis voltages of the i-th hub motor. and These are the resistance and stator flux linkage of the i-th hub motor. It is the number of pole pairs of the i-th hub motor.

[0040] Furthermore, the Euler-Lagrange principle can describe nonlinear systems as follows: In the formula, It is the Lagrangian function, representing the kinetic energy of the nonlinear system. and potential energy The difference is zero for a multi-hub motor electromechanical system. For the system's state variables, For the generalized input variables of the system;

[0041] Furthermore, based on the established dynamic equations and voltage equations of the multi-hub motor electromechanical system, an Eulerian-Lagrange-based equation for the multi-hub motor electromechanical system is established. It is the sum of mechanical kinetic energy and electromagnetic kinetic energy. The actual rotational speeds of each hub motor in the multi-hub motor electromechanical system and the d-axis and q-axis currents of each hub motor are given. This includes the d-axis and q-axis voltages of each hub motor, the load torque of each hub motor, and the disturbance torsional coupling torque experienced by each hub motor.

[0042] Furthermore, in order to ensure stable tracking of the electrical circuit of the multi-hub motor electromechanical system and reduce the influence of rigid coupling at the mechanical level, the nonlinear system description based on the Euler-Lagrange principle is transformed into an energy function that can unify the electrical and mechanical equations of the nonlinear system. In the formula, The positive definite inertia matrix of each hub motor is obtained by partial differential of the Lagrange function;

[0043] Furthermore, based on the energy function and the Eulerian-Lagrange multi-hub motor electromechanical system equations, an Eulerian-Lagrange nonlinear model of the mechanically and electrically integrated multi-hub motor electromechanical system was constructed. In the formula, the d-axis current, q-axis current, and actual speed of the four-wheel hub motor represent 12 state variables. A 12x12 interconnection coefficient matrix and a 12x12 matrix of damping coefficients These represent the electrical and mechanical internal structures of the multi-hub motor electromechanical system, respectively. The d-axis voltage, q-axis voltage, and external torque acting on each hub motor represent the inputs of the multi-hub motor electromechanical system. Energy function of multi-hub motor electromechanical system partial differential As output ;

[0044] Step 2: Presetting the Euler-Lagrange Torque Distribution Control Law: First, based on the Euler-Lagrange nonlinear model of the multi-hub motor electromechanical system, construct the desired energy function and closed-loop multi-hub motor electromechanical system function. Assume that, given the injection interconnection matrix, injection friction damping matrix, and desired equilibrium point of the multi-hub motor electromechanical system, a closed-loop control feedback law satisfying the closed-loop multi-hub motor electromechanical system function can be found. Second, considering the coupling relationships of various state variables, define the injection interconnection matrix and injection friction damping matrix. Based on the actual speed of each hub motor, the q-axis current, and the d-axis current of each hub motor, determine the desired equilibrium point of the multi-hub motor electromechanical system. Finally, based on the coefficients of the injection interconnection matrix and injection friction damping matrix, the closed-loop control feedback law, and the desired equilibrium point of the multi-hub motor electromechanical system, solve for the voltage and torque tracking control laws. With voltage stability tracking as a constraint, the Euler-Lagrange torque distribution control law can be obtained by inverse solving.

[0045] Furthermore, the desired energy function and satisfy In the formula, It is the undetermined energy function of the multi-hub motor electromechanical system. It is the expected equilibrium point of the state variables; the closed-loop multi-hub motor electromechanical system function. ,in The desired energy function of the multi-hub motor electromechanical system, and the closed-loop feedback law are as follows: In the formula, x * It is the expected equilibrium point of the state variables of the multi-hub motor electromechanical system. It is a 12-row, 12-column matrix of expected interconnection coefficients. It is a 12x12 matrix of expected damping coefficients. It is the desired energy function of the multi-hub motor electromechanical system, and D is the positive definite inertia matrix containing the internal parameters of each hub motor;

[0046] Furthermore, the given injection interconnection coefficient matrix satisfies: That is, by injecting the interconnect matrix Injecting state variable decoupling coefficients asymptotically stabilizes the 12 state variables within the multi-hub motor electromechanical system, including the injected interconnect matrix. In the formula, , The injected interconnect matrix coefficients consist of 8 injected dynamic interconnect coefficients. , , , , , , , and 12 injection-undetermined interconnect coefficients , , , , , , , , , , , constitute;

[0047] Furthermore, the given injection friction damping coefficient matrix The strong correlation with the speed of the multi-hub motor indicates that injecting an external dissipation coefficient achieves asymptotic stability of the state variables of the multi-hub motor electromechanical system; the injection of a friction damping matrix... satisfy: In the formula, , , , , The injected friction damping matrix coefficients consist of four injected dynamic friction damping coefficients. , , , and 4 self-tuned injection friction damping coefficients , , , constitute;

[0048] Furthermore, the desired balance point of the multi-hub motor electromechanical system includes: , , In the formula, It is the expected d-axis current of the i-th hub motor. It is the expected q-axis current of the i-th hub motor. The expected speed of the i-th hub motor is obtained from the driver's operation information, and i = 1, 2, 3, 4, which correspond to hub motor I, hub motor II, hub motor III, and hub motor IV, respectively.

[0049] Furthermore, by substituting the injected interconnection matrix coefficients and injected friction damping matrix coefficients mentioned above into the closed-loop multi-hub motor electromechanical system function, and combining the closed-loop control feedback law and the desired equilibrium point of the multi-hub motor electromechanical system, the voltage and torque tracking control laws can be obtained. The torque tracking control law includes injected undetermined interconnection coefficients, injected dynamic interconnection coefficients, injected dynamic friction damping coefficients, and injected self-tuning friction damping coefficients that need to be tuned. Therefore, voltage stable tracking is used as a constraint. That is, the voltage on the d-axis of the i-th hub motor stably tracks the desired voltage on the d-axis, and the voltage on the q-axis of the i-th hub motor stably tracks the desired voltage on the q-axis, where i = 1, 2, 3, 4 correspond to hub motor I, hub motor II, hub motor III, and hub motor IV, respectively. Substituting these values ​​into the voltage tracking control law... This achieves decoupling of the d-axis and q-axis current terms and the speed term, and simultaneously yields the relationship between the injected undetermined interconnect coefficients and the injected dynamic interconnect coefficients, where, This is an adjustment coefficient that includes the injected undetermined interconnect coefficients. This is an adjustment coefficient that includes the injected dynamic interconnect coefficient. The influence of the rotational speed is caused by another hub motor with mechanical coupling, and i=1, 2, 3, 4 correspond to hub motor I, hub motor II, hub motor III, and hub motor IV, respectively. By substituting the decoupling and coefficient relationships into the torque tracking control law, the Euler-Lagrange torque distribution control law can be obtained by inverse solution. In the formula, It is the moment of inertia of the i-th hub motor. It is the electromagnetic torque of the i-th hub motor. It is the actual rotational speed of the i-th hub motor. Let m be the m-th state variable. , Let be the current regulation coefficient of the i-th hub motor, including the injected dynamic interconnection coefficient. It is the compensation term for the i-th hub motor, and i=1,2,3,4, which correspond to hub motor I, hub motor II, hub motor III, and hub motor IV, respectively.

[0050] Step 3: Sampling of state variables for the multi-hub motor electromechanical system: The state variables of the multi-hub motor electromechanical system include the actual speeds of the four hub motors and the d-axis and q-axis currents obtained through coordinate transformation. The rotary transformer feeds back the actual speeds of hub motors I, II, III, and IV. The analog-to-digital converter samples the three-phase currents of hub motors I, II, III, and IV.

[0051] Step 4: Determine the desired balance point of the multi-hub motor electromechanical system: Determine the desired speed of each hub motor based on the driver's operation information, and obtain the desired d-axis current and q-axis current of each hub motor by controlling id=0 based on the actual speed and desired speed of each hub motor.

[0052] Furthermore, based on the control with id=0, the desired d-axis current of the i-th hub motor is... The desired q-axis current of the i-th hub motor The expression for the desired d-axis voltage of the i-th hub motor can be calculated from the voltage equation of the multi-hub motor electromechanical system based on the desired d-axis and q-axis currents. and the expected voltage of the q-axis of the i-th hub motor ;

[0053] Step 5: Calculation of injected dynamic interconnection coefficient and injected friction damping coefficient: The difference between the state variables obtained by subtracting the state variables of the multi-hub motor electromechanical system in Step 3 and the corresponding expected values ​​in Step 4 is used as input. The injected dynamic interconnection coefficient and injected friction damping coefficient can be solved by taking the local minimization of the closed-loop multi-hub motor electromechanical system function at the expected equilibrium point and the asymptotic stability in the dynamic process as the constraint condition.

[0054] Furthermore, combining the voltage tracking control law, the injected undetermined interconnect coefficients are obtained by decoupling the current and speed terms in the voltage tracking control law: The adjustment coefficient for injecting the undetermined interconnect coefficients in the formula is: Adjustment coefficient of injected dynamic interconnect coefficient Therefore, the injected undetermined interconnection coefficients can be linearly expressed by the injected dynamic interconnection coefficients; the injected dynamic interconnection coefficients can be tuned by taking the local minimization of the closed-loop multi-hub motor electromechanical system function at the desired equilibrium point and its asymptotic stability during the dynamic process as constraints. and injection dynamic friction damping coefficient The self-tuning friction damping coefficient is an adjustable parameter, where, ;

[0055] Step 6: The Euler-Lagrange torque distribution controller outputs the optimal given torque: Input the state variables of the multi-hub motor electromechanical system sampled in Step 3, the difference of the state variables obtained in Step 5, the injected dynamic interconnection coefficient, and the injected friction damping coefficient. Calculate the optimal given torque for each hub motor in real time using the Euler-Lagrange torque distribution control law preset in Step 2.

[0056] Further, see Figure 4The Euler-Lagrange torque distribution controller achieves torque distribution based on the Euler-Lagrange nonlinear model. The Euler-Lagrange torque distribution controller determines the state equilibrium point of the multi-hub motor electromechanical system based on step four: the desired d-axis current of each hub motor. , , , and the expected q-axis current of each hub motor , , , Based on driver operation information: the desired speed of the four-wheel hub motors. , , , The system state equilibrium point is used as input; based on the sampled system state variables—the actual speed of each hub motor and the q-axis and d-axis currents of each hub motor obtained in step three—the state variable difference of the multi-hub motor electromechanical system is calculated; and the dynamic interconnection coefficients are injected based on the tuning in step five. , , , , , , , and injection dynamic friction damping coefficient , , , Based on the injection self-tuning friction damping coefficient , , , The torque of each hub motor is dynamically adjusted to achieve rapid and stable tracking of the system's desired trajectory during the dynamic process. With voltage stability tracking as a constraint, the optimal given torque signal is output in real time according to the Euler-Lagrange torque distribution control law preset in step two.

[0057] Step 7: The modulation controller and the multi-half-bridge arm group driver work together to drive each hub motor: The modulation controller-I and modulation controller-II are based on torque control. They receive the optimal given torque obtained in step 6 and output the optimal modulation voltage. The multi-half-bridge arm group driver receives the optimal modulation voltage and realizes the coordinated drive of the four hub motors through the PWM drive signal of the arm being turned on and off.

[0058] Furthermore, the modulation controller-I and modulation controller-II input the flux linkage setpoint based on the detailed design parameters of the four hub motors, and use the optimal setpoint torque output by the Euler-Lagrange torque distribution controller and the sampled phase current of each hub motor as inputs to output the optimal modulation voltage, effectively reducing the number of motor controllers in distributed drive electric vehicles; see also Figure 5 Both modulation controller-I and modulation controller-II include a torque / flux observer, sector selection, hysteresis comparator, and multi-voltage vector switching table module. The torque / flux observer calculates the electromagnetic torque and flux linkage of each hub motor based on the sampled phase currents of each hub motor. The modulation controller receives the optimal given torque signal for each hub motor from the Euler-Lagrange torque distribution controller in real time, combines it with the given flux linkage of each hub motor, and subtracts it from the observed electromagnetic torque and flux linkage of each hub motor to obtain the torque difference signal and flux linkage difference signal. These signals are then output after passing through the hysteresis comparator. Torque modulation signal and flux linkage modulation signal; the multi-voltage vector switch meter outputs the optimal modulation voltage based on the torque modulation signal and flux linkage modulation signal; the multi-half-bridge arm group driver contains six arms, and the upper and lower switching devices of each arm work in a complementary conduction state. The received optimal modulation voltage is represented by an octal number, and the corresponding binary number represents the conduction and cutoff state of each arm of the multi-half-bridge arm group driver. The multi-half-bridge arm group driver receives the optimal modulation voltage provided by the modulation controller through optical fiber, and realizes the coordinated control of each hub motor by the conduction and cutoff of each arm.

[0059] A preferred embodiment of the present invention is as follows: the driver issues vehicle status commands based on road conditions and the target path via the accelerator pedal opening and steering wheel angle signals; subsequently, the Euler-Lagrange torque distribution controller receives the desired speed of each wheel hub motor and the wheel hub motor phase current sampled by the analog-to-digital converter, and outputs the optimal given torque in real time; then, the modulation controller outputs the optimal modulation voltage to achieve coordinated drive of the four wheel hub motors. Specifically, as follows:

[0060] (1) During the straight-line driving phase of the distributed drive electric vehicle, the expected speed of each hub motor given by the driver's operation information is: The phase currents of hub motor I, hub motor II, hub motor III, and hub motor IV, sampled by the analog-to-digital converter, are as follows: , , and After coordinate transformation Obtain the d-axis and q-axis currents of hub motors I, II, III, and IV: , , , , , , , The Euler-Lagrange torque distribution controller receives the d-axis current and q-axis current of each hub motor, as well as the desired speed of each hub motor.

[0061] (2) Setting the injection dynamic interconnection coefficient , , , , , , , and injection dynamic friction damping coefficient , , , Based on each state variable and its difference, the Euler-Lagrange torque distribution control law is substituted to output the optimal given torque for each hub motor, where a self-tuning friction damping coefficient is injected. , , , Adjustments are needed based on the rotational inertia parameters of each hub motor. A larger rotational inertia requires longer start-up and acceleration times. According to the Euler-Lagrange torque distribution law, the self-tuning friction damping coefficient directly affects the speed of each hub motor; a larger self-tuning friction damping coefficient results in longer start-up and acceleration times. Therefore, adjusting the self-tuning friction damping coefficient can reduce the mismatch motion effects caused by differences in the parameters of each hub motor. For example, when hub motor I and hub motor III have the same parameters and a rotational inertia of 1.398 kg·m... 2 Hub motor II and hub motor IV have the same parameters and a moment of inertia of 1 kg·m. 2 When, select This reduces the impact of the parameters of each hub motor, outputs the optimal given torque of each hub motor in real time, and improves the collaborative robustness of each hub motor.

[0062] (3) The modulation controller receives the optimal given torque of each hub motor. The selection of the given flux linkage of each hub motor depends on the parameters of each hub motor. For example, when the stator flux linkage of hub motor I is 0.045Wb and the stator flux linkage of hub motor II is 0.054Wb, the given flux linkage of hub motor I is 0.045Wb and the given flux linkage of hub motor II is 0.054Wb. Based on the optimal given torque, given flux linkage and phase current of each hub motor, the modulation controller selects the appropriate optimal modulation voltage from the multi-voltage vector switching table to achieve coordinated control of each hub motor through the output modulation signal of the hysteresis comparator and the sector determination of the output sector.

[0063] Therefore, this invention constructs a multi-hub motor Euler-Lagrange torque distribution system and designs an integrated Euler-Lagrange torque controller to achieve coordinated control of multiple hub motors. This system includes an Euler-Lagrange torque distribution controller, two modulation controllers, and two multi-half-bridge arm group drivers. This invention constructs an Euler-Lagrange nonlinear model of the multi-hub motor electromechanical system and proposes an Euler-Lagrange torque distribution control law with voltage stability tracking of each hub motor as a constraint. This invention considers the dynamic process tracking stability of the closed-loop multi-hub motor electromechanical system and constructs a method for injecting dynamic interconnection coefficients and injecting dynamic friction damping coefficients to improve the fast response performance of each hub motor. Based on the mismatch motion caused by the differences in parameters of each hub motor, this invention constructs an injected self-tuning friction damping coefficient to improve the system robustness and the coordinated performance of each hub motor.

[0064] The above embodiments are only used to illustrate the design concept and features of the present invention. Their purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly. The scope of protection of the present invention is not limited to the above embodiments. Therefore, all equivalent changes or modifications made based on the principles and design ideas disclosed in the present invention are within the scope of protection of the present invention.

Claims

1. A multi-hub motor Euler-Lagrange torque dynamic vector distribution system, characterized in that: The four hub motors of the distributed drive electric vehicle are considered as a multi-hub motor electromechanical system; the torque distribution system of the multi-hub motor electromechanical system includes an integrated Euler-Lagrange torque controller and a driving assistance system; The integrated Euler-Lagrange torque controller consists of an Euler-Lagrange torque distribution controller, two modulation controllers, and two multi-half-bridge arm group drivers; The aforementioned driving assistance system receives and records the driving environment around the vehicle from sensors, assisting the driver in safe driving operations: issuing an alarm and providing a corrective torque to bring the vehicle back to the main lane when it deviates from the course; performing protective active braking when an obstacle suddenly appears in front of the vehicle; and issuing an alarm when driver fatigue is detected. The Euler-Lagrange torque distribution controller obtains the desired speeds of the four hub motors (hub motors I, II, III, and IV) from the driver's operation information, as well as the sampled actual speeds of the four hub motors and the three-phase current. The system uses variable feedback values ​​to output the optimal given torque for each hub motor in real time. A modulation controller and a multi-half-bridge arm group driver act as a set of lower-level slave controllers to achieve direct collaborative driving of two hub motors on the same side of the distributed drive electric vehicle. The other two hub motors on the same side are directly collaboratively driven by another set of lower-level slave controllers. The modulation controller receives the optimal given torque of each hub motor and outputs the optimal modulation voltage. It controls each hub motor through the multi-half-bridge arm group driver. In addition, the two modulation controllers and the two multi-half-bridge arm group drivers have completely identical structures.

2. The system according to claim 1, characterized in that: The torque distribution control method of the multi-hub motor electromechanical system relies on an Euler-Lagrange torque distribution controller, two modulation controllers, and two multi-half-bridge arm group drivers. The Euler-Lagrange torque distribution controller, as the upper-level controller, considers the electromechanical coupling factors between the two hub motors on the same side of the distributed drive electric vehicle. Based on the Euler-Lagrange principle, an Euler-Lagrange torque distribution control law is designed. The inputs are the feedback values ​​of the driver's operation information and the sampled state variables of the four hub motors. The injected interconnect matrix coefficients and injected friction damping matrix coefficients are used as coordination factors, and the voltage stability tracking of each hub motor is used as a constraint to achieve real-time optimal torque distribution. The modulation controllers receive the optimal given torque information and provide PWM drive signals to the multi-half-bridge arm group drivers through a torque / flux observer, hysteresis comparator, sector selection, and multi-voltage vector switching table module to collaboratively drive the four hub motors.

3. A method for dynamic vector distribution control of Euler-Lagrange torque in a multi-hub motor, characterized in that: Hub motor-I and hub motor-II are defined as two hub motors on the same side, and hub motor-III and hub motor-IV are defined as two hub motors on the same side. Hub motor-I and hub motor-II are driven by modulation controller-I and multi-half-bridge arm group driver-I, while hub motor-III and hub motor-IV are driven by modulation controller-II and multi-half-bridge arm group driver-II. The required steps for the torque distribution control method of the multi-hub motor electromechanical system are described below: Step 1: Preprocessing for modeling four hub motors: Establish the dynamic equations and voltage equations of four hub motors that take into account the rigid coupling effects caused by elastic components such as air suspension system, frame, and bearings in distributed drive electric vehicles. Based on the Euler-Lagrange principle, construct the Euler-Lagrange nonlinear model of the mechanical and electrical integrated multi-hub motor electromechanical system. Step 2, Euler-Lagrange Torque Distribution Control Law Presetting: First, based on the Euler-Lagrange nonlinear model of the multi-hub motor electromechanical system, construct the desired energy function and closed-loop multi-hub motor electromechanical system function, and assume that by giving the injection interconnection matrix, the injection friction damping matrix, and the desired equilibrium point of the multi-hub motor electromechanical system, a closed-loop control feedback law can be found that satisfies the closed-loop multi-hub motor electromechanical system function; Secondly, considering the coupling relationship of each state variable, the injection interconnection matrix and the injection friction damping matrix are defined. Based on the actual speed of each hub motor, the q-axis current and d-axis current of each hub motor, the desired equilibrium point of the multi-hub motor electromechanical system is determined. Finally, based on the coefficients of the injection interconnection matrix and the coefficients of the injection friction damping matrix, the closed-loop control feedback law, and the desired equilibrium point of the multi-hub motor electromechanical system, the voltage and torque tracking control laws are solved. With voltage stability tracking as the constraint, the Euler-Lagrange torque distribution control law can be obtained by inverse solution. Step 3: Sampling of state variables of multi-hub motor electromechanical system: The state variables of multi-hub motor electromechanical system include the actual speed of four hub motors and the d-axis current and q-axis current obtained through coordinate transformation. The rotary transformer feeds back the actual speed of hub motor-I, hub motor-II, hub motor-III and hub motor-IV. The analog-to-digital converter samples the three-phase currents of hub motors I, II, III, and IV. Step 4: Determine the desired balance point of the multi-hub motor electromechanical system: Determine the desired speed of each hub motor based on the driver's operation information, and obtain the desired d-axis current and q-axis current of each hub motor by controlling id=0 based on the actual speed and desired speed of each hub motor. Step 5: Calculation of injected dynamic interconnection coefficient and injected friction damping coefficient: The difference between the state variables obtained by subtracting the state variables of the multi-hub motor electromechanical system in Step 3 and the corresponding expected values ​​in Step 4 is used as input. The injected dynamic interconnection coefficient and injected friction damping coefficient can be solved by taking the local minimization of the closed-loop multi-hub motor electromechanical system function at the expected equilibrium point and the asymptotic stability in the dynamic process as the constraint condition. Step 6: The Euler-Lagrange torque distribution controller outputs the optimal given torque: Input the state variables of the multi-hub motor electromechanical system sampled in Step 3, the difference of the state variables obtained in Step 5, the injected dynamic interconnection coefficient, and the injected friction damping coefficient. Calculate the optimal given torque for each hub motor in real time using the Euler-Lagrange torque distribution control law preset in Step 2. Step 7: The modulation controller and the multi-half-bridge arm group driver work together to drive each hub motor: The modulation controller-I and modulation controller-II are based on torque control. They receive the optimal given torque obtained in step 6 and output the optimal modulation voltage. The multi-half-bridge arm group driver receives the optimal modulation voltage and realizes the coordinated drive of the four hub motors through the PWM drive signal of the arm being turned on and off.

4. The method according to claim 3, characterized in that: In step one, the rigid coupling influencing factor of the multi-hub motor electromechanical system is generated by the torsional coupling torque of the two hub motors on the same side. The actual speed difference of the four hub motors of the distributed drive electric vehicle, measured by multiple rotary transformers, is used to calculate the disturbance torsional coupling torque through elastic system mechanics analysis. , Let be the disturbance torsional coupling torque of the i-th hub motor, where i = 1, 2, 3, 4, corresponding to hub motor I, hub motor II, hub motor III, and hub motor IV, respectively. The disturbance torsional coupling torques generated by hub motor I and hub motor II are equal in magnitude and opposite in direction; the disturbance torsional coupling torques generated by hub motor III and hub motor IV are equal in magnitude and opposite in direction. The Euler-Lagrange nonlinear model of the multi-hub motor electromechanical system uses the d-axis current, q-axis current, and actual rotational speed of the four hub motors to represent 12 state variables. A matrix of interconnection coefficients consisting of 12 rows and 12 columns and a 12x12 matrix of damping coefficients The electrical and mechanical internal structure of a multi-hub motor electromechanical system is represented by the d-axis voltage, q-axis voltage, and external torque acting on each hub motor, which represent the inputs of the multi-hub motor electromechanical system. Energy function of multi-hub motor electromechanical system The partial derivative as output The external torque is coupled to the disturbance torque. and load torque The composition is given by i = Ⅰ, Ⅱ, Ⅲ, Ⅳ.

5. The method according to claim 3, characterized in that: In step two, the closed-loop multi-hub motor electromechanical system function ,in The desired energy function of the multi-hub motor electromechanical system, and the closed-loop feedback law are as follows: In the formula, x * It is the expected equilibrium point of the state variables of the multi-hub motor electromechanical system. It is a 12-row, 12-column matrix of expected interconnection coefficients. It is a 12x12 matrix of expected damping coefficients. It is the expected energy function of the multi-hub motor electromechanical system, and D is the positive definite inertia matrix containing the internal parameters of each hub motor.

6. The method according to claim 5, characterized in that: In step two, the interconnect matrix is ​​injected. and interconnection coefficient matrix Composition of expected interconnection coefficient matrix The injected interconnection matrix asymptotically stabilizes the 12 state variables within the multi-hub motor electromechanical system by injecting state variable decoupling coefficients. The injected interconnection matrix coefficients consist of 8 injected dynamic interconnection coefficients and 12 injected undetermined interconnection coefficients. These undetermined interconnection coefficients represent the coupling relationship between the current state variable and the rotational speed state variable. The injected friction damping matrix... and damping coefficient matrix Composition of the expected damping coefficient matrix Inject friction damping matrix The strong correlation with the speed of the multi-hub motor indicates that the injection of external dissipation coefficients achieves asymptotic stability of the state variables of the multi-hub motor electromechanical system; the injected friction damping matrix coefficients consist of 4 injected dynamic friction damping coefficients and 4 injected self-tuning friction damping coefficients.

7. The method according to claim 3, characterized in that: In step two, the optimal setpoint torque for each hub motor is output by the Euler-Lagrange torque distribution control law. for In the formula, It is the moment of inertia of the i-th hub motor. It is the electromagnetic torque of the i-th hub motor. R is the actual rotational speed of the i-th hub motor. i+2 This is the damping coefficient matrix of the (i+2)th motor; Let m be the m-th state variable, and the asterisk superscript on it indicates the expected equilibrium point of the corresponding state variable. , Let be the current regulation coefficient of the i-th hub motor, including the injected dynamic interconnection coefficient. It is the compensation term for the i-th hub motor, and i = 1, 2, 3, 4, which correspond to hub motor I, hub motor II, hub motor III, and hub motor IV, respectively.

8. The method according to claim 3, characterized in that: In step five, the injected undetermined interconnection coefficient is obtained by decoupling the current and speed terms in the voltage tracking control law and is linearly represented by the injected dynamic interconnection coefficient. The injected dynamic interconnection coefficient and the injected dynamic friction damping coefficient can be tuned by taking the local minimization of the closed-loop multi-hub motor electromechanical system function at the desired equilibrium point and the asymptotic stability in the dynamic process as constraints. The injected self-tuning friction damping coefficient is an adjustable parameter.

9. The method according to claim 3, characterized in that: In step seven, the modulation controller-I and modulation controller-II input the flux setpoint based on the detailed design parameters of the four hub motors. Both include a torque / flux observer, sector selection, hysteresis comparator, and multi-voltage vector switch module. The optimal modulation voltage is represented by an octal number, and its corresponding binary number represents the on and off states of each arm of the multi-half-bridge arm group driver.

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