A multi-source excitation real-time closed-loop coupling method for optimizing control of an electric drive system

Abstract

This invention belongs to the field of simulation and control technology, specifically relating to a modeling and control strategy optimization method for integrated motor and gearbox systems. Addressing the technical pain points in existing electric drive NVH development, such as unidirectional open-loop mechanical-electromagnetic excitation coupling modeling, disconnect between control strategy optimization and operating conditions, and large simulation-test deviations, this invention proposes a unified three-domain modeling scheme. It constructs a multi-source excitation coupling framework integrating mechanical, electrical, and control systems, embedding the control algorithm as a real-time dynamic link into a rigid-flexible coupling dynamic model. It establishes a back-EMF harmonic model considering mechanical vibration feedback and a flexible gear meshing force model, achieving full-link bidirectional closed-loop real-time coupling of mechanical-electromagnetic excitation. Based on this high-fidelity platform, iterative optimization of vibration reduction control strategies is completed. The proposed modeling method can reduce simulation deviation to within 5%, achieve vibration suppression of over 40% at specific orders, reduce prototype iterations by 60%, shorten the development cycle by 40%, and support the development of high-performance, low-noise electric drive systems.

Description

Technical Field

[0001] This invention belongs to the field of multi-physics coupled dynamics modeling and simulation technology of electromechanical systems. Specifically, it relates to a modeling and analysis method for integrated motor and gearbox systems (such as electric propulsion systems and powertrains), and more particularly to a real-time closed-loop coupled modeling and numerical simulation optimization method for multi-source excitation of mechanical, electromagnetic and control systems. Background Technology

[0002] With the rapid development of aviation, shipbuilding, and new energy vehicles, the industry has placed increasingly stringent demands on the high power density, high reliability, and low vibration and noise (NVH) performance of electric drive systems. The "New Energy Vehicle Industry Development Plan (2021-2035)" clearly states the development goal of "improving the NVH performance of the entire vehicle." However, the current development of NVH in electric drive systems still faces many bottlenecks: relevant test data shows that for every 1000 rpm increase in the electric drive system's speed, the noise level increases by 1-2 decibels, resulting in a 40% rate of noise exceeding standards during high-speed cruising. Simultaneously, the deviation between laboratory modeling and simulation and powertrain testing under traditional development models often exceeds 15%, and prototype testing can take several days per run, with a single prototype costing over a million yuan to manufacture. If NVH problems are discovered late in development, the cost of redesigning is extremely high. Against this backdrop, integrated design of the motor and gearbox has become an important development direction for electric drive systems. This design can effectively improve the system's power density, but it also makes the multi-physics coupling relationships within the system more complex.

[0003] In an integrated motor-gearbox system (i.e., a mechatronic system of motor and gearbox), there are complex interactive coupling relationships between the motor, electromagnetic field, transmission chain, and structural components. The excitation sources within the system exhibit multi-source characteristics, including radial electromagnetic force and electromagnetic torque generated by the air gap magnetic field of the motor, time-varying meshing force generated by gear meshing, nonlinear support force of bearings, and external load excitation. These excitations are transmitted and coupled through flexible shaft systems, bearings, and housing structures, forming a complex "electromagnetic-mechanical-structural" coupled vibration problem. In addition, motor control strategies such as maximum torque-current ratio control, field weakening control, harmonic injection control, and active damping control play a crucial role in the dynamic behavior of the system: the control strategy directly determines the current waveform, modulation method, and switching frequency of the motor, thereby affecting the time-frequency characteristics and amplitude distribution of the electromagnetic excitation; at the same time, the control strategy relies on mechanical feedback signals such as speed and position, and the vibration response of the mechanical system will affect the adjustment behavior of the controller through the sensor feedback channel, forming a closed-loop coupling path of "mechanical vibration → feedback signal → control adjustment → electromagnetic excitation → mechanical vibration". Because there are significant mutual influences between different physical domains, such as mechanical vibration causing distortion of the air gap magnetic field and changes in electromagnetic parameters, while electromagnetic excitation in turn acts on the mechanical system, it is difficult to meet the requirements of high-precision analysis and low vibration noise control by relying solely on modeling of a single physical domain. There is an urgent need to establish a unified modeling method that can reflect the coupling relationship of multiple physical fields.

[0004] Existing research on motor-gearbox systems mostly adopts one of the following technical approaches: one type of method models the motor, electromagnetic excitation, and mechanical transmission system separately, considering only unidirectional excitation transmission; another type simplifies the transmission system into a rigid body electromechanical coupling model, ignoring the flexible effects of structures such as gears, rotors, and housings. Furthermore, existing electromechanical coupling modeling techniques largely focus on the bending and torsional vibration analysis of the transmission chain, lacking a quantitative description of the vibration of the housing structure and its feedback to the electromagnetic system. This makes it difficult to reveal the mechanism of closed-loop coupling between external loads, electromagnetic excitation, structural vibration, and the control system of the motor-gearbox system, specifically manifested in the following aspects:

[0005] Firstly, the simplified lumped parameter model reduces the motor and gearbox to a mass-spring-damping system, which, while computationally efficient, fails to describe key dynamic characteristics such as gear body flexibility and shell structure modes.

[0006] Secondly, the component separation or unidirectional excitation model calculates the electromagnetic force as a fixed or only variable excitation with respect to electrical parameters, and then inputs it into the mechanical dynamics model as a predetermined load. This method completely ignores the feedback effect of the vibration of the mechanical system on the motor. It is an open-loop model and cannot simulate the real coupling effect.

[0007] Third, the rigid structure assumption model treats gears, rotors, housings, etc. as rigid bodies. Although it partially considers time-varying meshing stiffness, it seriously underestimates the impact of structural flexibility on the dynamic characteristics of the system gears, load distribution, and vibration transmission.

[0008] Fourth, the ideal mathematical model of a unidirectional motor cannot accurately characterize the harmonics of the permanent magnet back electromotive force generated by the distortion of the air gap magnetic field due to mechanical vibration. This makes it impossible for traditional methods to accurately reveal the harmonic characteristics of the motor affected by the vibration of the mechanical system, as well as the mechanism of the motor's further influence on the vibration of the mechanical system.

[0009] While existing technologies have made some progress in electromechanical coupling modeling, key shortcomings remain, particularly in the integration of closed-loop coupling mechanisms and control strategies. For example, although patent document CN112287485B establishes an electromechanical-rigid-flexible coupling dynamic model, its essence is open-loop unidirectional coupling: that is, based on the rigid-flexible coupling model of the mechanical system, the electrical system excitation is applied as a pre-calculated external load, ignoring the reverse influence of mechanical system vibration on the electrical system. Furthermore, this patent only considers the flexibility of the drive shaft and housing, failing to adequately characterize the meshing dynamics of flexible gears and establishing the coupling relationship between the flexible deformation of the gear body and the meshing excitation, making it difficult to achieve a complete rigid-flexible coupling dynamic characterization. Similarly, patent document CN121302873A, although involving a co-simulation interface for bidirectional real-time coupling between mechanical and electromagnetic systems, primarily aims at optimizing gear design parameters, focusing on obtaining more accurate responses through the coupling model to iterate gear parameters. This results in repeated system-level simulation verification during product development, leading to long iteration cycles and low engineering efficiency. Meanwhile, the patent lacks sufficient modeling and analysis of the back electromotive force harmonic characteristics of the motor rotor under the influence of mechanical torsional vibration, making it difficult to truly reflect the feedback effect of mechanical vibration on electromagnetic excitation and weakening the physical consistency of electromechanical bidirectional coupling.

[0010] In addition, existing NVH control technologies are mostly focused on passive control methods, such as adding vibration damping pads and sound insulation materials. Although these can alleviate vibration and noise problems to some extent, the effect is limited and they will increase the weight and cost of the system. On the other hand, the application of active control technology is limited by the accuracy of modeling. Traditional control methods are usually based on the linear model design of the motor body, which cannot accurately reflect the coupling dynamics of the three domains of mechanics, electricity and control. This leads to the disconnect between the control algorithm design and the actual electromechanical coupling dynamics, and the control effect deviates from the expectations under real working conditions.

[0011] In summary, current technologies have not yet achieved true two-way closed-loop coupling between mechanical, electromagnetic, and control systems: that is, they cannot simultaneously consider the reverse influence of the mechanical system on electrical excitation and the closed-loop dynamic evolution process after this influence acts back on the mechanical system within a unified model. This deficiency has become a key technical bottleneck in the optimization process of electric drive system control strategies, limiting the design and verification capabilities of vibration reduction and noise reduction control methods under real electromechanical coupling conditions. It also leads to problems such as large discrepancies between simulation and actual vehicle performance, long development cycles, and high costs in the current NVH development of electric drive systems, making it difficult to meet the industry's demand for high-performance, low-vibration-noise electric drive systems. Therefore, it is necessary to propose a real-time coupled modeling and analysis method for multi-source excitation of mechanical, electromagnetic, and control systems to optimize vibration reduction and noise reduction control strategies for electric drive systems. This method aims to overcome the limitations of existing unidirectional coupling and engineering applications, providing support for the efficient development of control strategies and the collaborative optimization of dynamic performance. Summary of the Invention

[0012] The purpose of this invention is to address the shortcomings of existing technologies by providing a real-time closed-loop coupled modeling method for multi-source excitation of electromechanical and control systems to optimize the control strategy of electric drive systems. Addressing the pain points in existing electric drive NVH development, such as unidirectional open-loop electromechanical coupled modeling, disconnect between control optimization and operating conditions, and large deviations between simulation and actual vehicle conditions, this invention proposes a unified three-domain modeling scheme. It constructs an integrated electromechanical-control coupled framework, embedding the control algorithm as a real-time dynamic link into the simulation. It establishes a back-EMF harmonic model and a flexible gear meshing force model considering mechanical vibration feedback, achieving full-link closed-loop coupling. Based on this high-fidelity platform, iterative optimization of vibration reduction control strategies can be completed, compressing simulation deviations to within 5%, achieving active suppression of more than 40% of vibration at specific orders, reducing prototype iterations by 60%, shortening the development cycle by 40%, and supporting the development of high-performance, low-noise electric drive systems.

[0013] The objective of this invention is achieved through the following scheme: a multi-source excitation real-time closed-loop coupling method for optimizing the control of an electric drive system, comprising the following steps:

[0014] 1) Divide the mechatronics system of motor-gearbox into three independent collaborative subsystems: a rigid-flexible coupled mechanical system, an electromagnetic excitation subsystem considering mechanical vibration feedback, and a motor control strategy module. Establish simulation models for each collaborative subsystem and construct a three-domain bidirectional closed-loop coupled modeling framework of mechanical-electrical-control.

[0015] 2) Define the interactive input and output of the simulation models of each collaborative subsystem in the three-domain bidirectional closed-loop coupling modeling framework of mechanical-electrical-control, form a multi-source excitation real-time closed-loop coupling system model, and define the multi-source excitation loading nodes of the multi-source excitation real-time closed-loop coupling system model, as well as the transmission relationship of each excitation load;

[0016] 3) Set up a control parameter optimization module, and based on the simulation of a multi-source excitation real-time closed-loop coupled system model, construct a multi-objective optimization framework for vibration reduction and noise reduction, carry out adaptive optimization of control parameters, and complete parameter iterative verification and convergence solution through closed-loop simulation to obtain the optimal control strategy parameters that meet the NVH performance requirements of the electric drive system.

[0017] Preferably, in step 1), the simulation model established for the electromechanical transmission system is a rigid-flexible coupling dynamic model. The establishment of this rigid-flexible coupling dynamic model specifically includes:

[0018] 1-1) For rigid-flexible coupled mechanical systems, a rigid-flexible coupled dynamic model incorporating structural flexibility is established, and integrated modeling of flexible body condensation, flexible helical gear meshing, and internal excitation is completed. This model is used to accurately characterize the structural flexibility and mechanical dynamic excitation characteristics of the system in subsequent simulations.

[0019] 1-1-1) CAE simulation software was used to divide the electromechanical integrated shell, stator system and gear into finite element meshes. After calculating the free modes of the system, the dynamic characteristics of the flexible body were condensed to the key master nodes.

[0020] 1-1-2) Modeling of meshing force of flexible helical gears:

[0021] 1-1-2-1) For gear meshing pairs, the gear is first equivalently divided into multiple slices along the tooth width direction. Then, based on tooth surface contact analysis, the time-varying meshing stiffness of each slice during the meshing process is calculated, and the time-varying characteristics and distribution characteristics of the meshing stiffness across the entire tooth width are obtained, providing core mechanical parameters for the subsequent establishment of the meshing force model;

[0022] 1-1-2-2) Based on the time-varying meshing stiffness of each slice obtained in step 1-1-2-1), establish a meshing force model between the central nodes of the tooth surfaces of the driving and driven gears, solve the dynamic meshing force between the central nodes of the tooth surfaces of the two gears, and finally distribute the obtained dynamic meshing force to the tooth surface contact line according to the meshing stiffness of each slice.

[0023] 1-1-3) In the meshing force model, bearing force elements are used to simulate nonlinear bearing force, and spring damping force elements are used to simulate support force. The internal excitation is integrated into the meshing force model to construct the bearing force and flexible mechanical structure, forming a rigid-flexible coupled dynamic model that can completely characterize the internal dynamic characteristics of the flexible mechanical transmission system.

[0024] Preferably, in step 1), the simulation model established for the electromagnetic excitation subsystem is an electromagnetic excitation model, and the establishment of this electromagnetic excitation model specifically includes:

[0025] 1-2) For the electromagnetic excitation subsystem, establish an electromagnetic excitation model that considers mechanical vibration feedback:

[0026] 1-2-1) Establish a real-time mapping relationship between the mechanical vibration of the rigid-flexible coupled mechanical system and the air gap magnetic field distortion characteristics of the electromagnetic excitation subsystem;

[0027] 1-2-2) Based on the real-time mapping relationship obtained in step 1-2-1), extract the harmonic characteristics of the back EMF that are modulated in real time by mechanical vibration, and establish a real-time correlation model between the back EMF harmonics and the rotor vibration state.

[0028] 1-2-3) Based on the real-time correlation model between the back EMF harmonics and the rotor vibration state obtained in step 1-2-2), reconstruct the voltage balance equation and electromagnetic torque equation of the motor.

[0029] 1-2-4) By analyzing the air gap permeability and air gap magnetic flux density using the finite element method, multi-order electromagnetic force modeling is carried out, and several radial electromagnetic force models of different spatial and temporal orders are constructed. The electromagnetic excitation is applied to the stator and rotor condensation main nodes of the rigid-flexible coupling mechanical system in the form of concentrated torque and concentrated force to form an electromagnetic excitation model.

[0030] Preferably, in step 2), the interactive input and output of the simulation models of each collaborative subsystem in the three-domain bidirectional closed-loop coupling modeling framework of mechanical-electrical-control are defined, including:

[0031] 2-1) Define the input and output interfaces of the rigid-flexible coupling dynamic model as follows: These interfaces are used to receive the electromagnetic excitation output from the electromagnetic excitation model and to transmit the mechanical vibration response in the form of vibration state parameters to the motor control strategy module and the electromagnetic excitation model in real time.

[0032] 2-1-1) Define an electromagnetic excitation load input interface to receive the tangential electromagnetic torque and radial electromagnetic force corresponding to the predefined coupling node;

[0033] 2-1-2) Define the system boundary load input interface, which is used to output the load force of the condensation node and the support force of the box constraint support node;

[0034] 2-1-3) Define an internal state update input interface to update internal mechanical excitations such as gear meshing force and nonlinear bearing force in real time during the time-domain iteration process, so as to realize the closed-loop update of internal excitations;

[0035] 2-1-4) Define the real-time vibration state parameter output interface of the motor rotor, which is used to output the real-time vibration state parameters of the motor rotor to the electromagnetic excitation model, thereby correcting the air gap magnetic field distortion characteristics in real time, reconstructing the back electromotive force harmonics and the core equation of the motor, so as to realize the reverse feedback of mechanical vibration to electromagnetic parameters and open up the electromechanical bidirectional coupling path.

[0036] 2-1-5) Define the vibration characteristic parameter output interface for the key frequency band of the system, which is used to output the vibration characteristic parameters of the key frequency band of the system to the motor control strategy module. The control strategy adjusts the control command in real time to realize active vibration reduction closed-loop control based on vibration state.

[0037] 2-1-6) Define the full dynamic response data output interface of all critical physical nodes of the system, which is used to output the full dynamic response data of all critical physical nodes of the system to the control parameter optimization module. While performing NVH characteristic simulation analysis of electric drive system, it serves as the calculation basis for vibration reduction and noise reduction optimization target, and supports the adaptive optimization and iterative verification of control parameters.

[0038] Preferably, in step 2), the interactive input and output of the simulation models of each collaborative subsystem in the three-domain bidirectional closed-loop coupling modeling framework of mechanical-electrical-control are defined, including:

[0039] 2-2) Define the input and output interfaces of the electromagnetic excitation model as follows: These interfaces receive the real-time vibration state parameters from the rigid-flexible coupling mechanical system and the real-time control commands from the motor control strategy module. Simultaneously, the calculated electromagnetic excitation is transmitted to the rigid-flexible coupling mechanical system, and the real-time electromagnetic state is fed back to the motor control strategy module.

[0040] 2-2-1) Define a real-time vibration state parameter input interface to receive the vibration state parameters output in real time during the time-domain iterative solution of the rigid-flexible coupled mechanical system. This allows for the quantitative analysis of the distortion effect of mechanical vibration on the air gap magnetic field of the motor, extraction of the harmonic characteristics of the back electromotive force modulated by vibration, reconstruction of the motor voltage balance equation and electromagnetic torque equation, and realization of real-time reverse feedback of mechanical vibration on electromagnetic characteristics from the physical level.

[0041] 2-2-2) Define a real-time adjustment command input interface to receive real-time adjustment commands output by the motor control strategy module after real-time adjustment based on mechanical vibration feedback and electromagnetic state feedback. This allows for real-time updating of the boundary conditions of electromagnetic calculations and recalculation of electromagnetic torque and radial electromagnetic force, thereby enabling the control strategy to actively modulate electromagnetic excitation and providing an execution path for active vibration reduction in the electric drive system.

[0042] 2-2-3) Define the electromagnetic excitation load output interface of the mechanical system, which is used to load the tangential electromagnetic torque calculated in real time and the radial electromagnetic force covering different spatial and temporal orders onto the predefined stator and rotor condensation main nodes of the rigid-flexible coupling dynamic model. In the simulation process, the core external dynamic excitation of the mechanical system is restored, driving the mechanical system to generate vibration response and opening up the positive coupling path from electromagnetic excitation to mechanical vibration response.

[0043] 2-2-4) Define a real-time electromagnetic state feedback parameter output interface to output the real-time electromagnetic state feedback parameters to the motor control strategy module. Together with the vibration response parameters output by the rigid-flexible coupling dynamic model, it constitutes the dual feedback input of the control algorithm, providing the adjustment basis for vibration reduction control algorithms such as maximum torque-current ratio, harmonic injection, and active damping, and supporting the real-time closed-loop adjustment of the control strategy.

[0044] 2-2-5) Define a full-time electromagnetic characteristic simulation data output interface to output full-time electromagnetic characteristic simulation data to the control parameter optimization module. Together with the vibration response data output by the rigid-flexible coupling dynamic model, it constitutes the calculation basis for the vibration reduction and noise reduction optimization target, supporting the sensitivity analysis, multi-objective optimization and iterative verification of control parameters.

[0045] Preferably, in step 2), the interactive input and output of the simulation models of each collaborative subsystem in the three-domain bidirectional closed-loop coupling modeling framework of mechanical-electrical-control are defined, including:

[0046] 2-3) Define the input and output interfaces of the motor control strategy module as follows: These interfaces are used to receive the real-time electromagnetic state output by the electromagnetic excitation model and the real-time mechanical vibration response feedback from the rigid-flexible coupling dynamic model, and to output control commands to the electromagnetic excitation model in real time.

[0047] The inputs to the motor control strategy module include: the instantaneous rotor speed, angular acceleration, and vibration amplitude in the key frequency band fed back from the rigid-flexible coupling mechanical system, as well as the real-time electromagnetic state fed back from the electromagnetic excitation subsystem.

[0048] The output of the motor control strategy module includes the corrected d / q axis current command, the amplitude and phase of the injected harmonic current, and the updated control parameters.

[0049] Preferably, in step 2), steps 2-4) define the multi-source excitation loading nodes of the multi-source excitation real-time closed-loop coupled system model, and the transmission relationship of each excitation load, specifically including:

[0050] 2-4-1) Apply tangential electromagnetic torque and radial electromagnetic force to the stator condensation node and rotor condensation node; apply gear meshing force to the gear tooth condensation node;

[0051] 2-4-2) Apply bearing force to the bearing shrinkage joint and bearing bore shrinkage joint of the drive shaft;

[0052] 2-4-3) Apply the load force to the output end shrinkage node;

[0053] 2-4-4) Apply the support force to the box-shaped constraint support node;

[0054] 2-4-5) Define the force and torque transmission relationship of the coupled nodes.

[0055] Preferably, in step 3), the Newmark-β time-domain stepwise integration method is used to perform real-time closed-loop iterative solution of the system. Within each time step, state updates, excitation updates, and vibration response solutions are completed. This is used to obtain the system's full-time-domain dynamic response in the simulation and to realize closed-loop coupled calculation of the electromechanical-control three domains. Specifically, this includes:

[0056] 3-1-1) State Update: Based on the mechanical state at the previous moment, the electromagnetic excitation is corrected in real time to update the mechanical influence on the electromagnetic field; at the same time, the mechanical vibration response is fed back to the motor control strategy module, and the control strategy adjusts the output control commands in real time according to the preset vibration reduction algorithm.

[0057] 3-1-2) Excitation update: Recalculate the electromagnetic excitation based on the updated control command; at the same time, update the bearing force and meshing force based on the vibration of the bearing and tooth surface node, and update the external support force based on the vibration of the housing constraint node.

[0058] 3-1-3) Response solution: Apply all updated excitations to the rigid-flexible coupled mechanical model and solve for the system vibration response at the current time step;

[0059] 3-1-4) Iterative Loop: Use the system response at the current time step as the input for the next time step, repeat the above steps, iterate and solve the problem, and finally obtain the dynamic response of all key physical nodes of the system.

[0060] Preferably, step 3) further includes building an engineering co-simulation platform based on multibody dynamics and control simulation software to complete the interface configuration and collaborative solution of mechanical, electromagnetic, and control models. This platform is used to achieve efficient engineering calculation and data extraction for the closed-loop coupling of the electromechanical, electronic, and control domains in subsequent simulations, as detailed below:

[0061] 3-2-1) Import the flexible body models of the shell and gears generated by Abaqus into Simpack. At the same time, use discrete beam element models in Simpack to establish the flexible body dynamics models of each transmission shaft, define the parameters of the flexible meshing gears and the constraint relationships between each transmission component, and establish a rigid-flexible coupling multibody dynamics model of the motor-gearbox.

[0062] 3-2-2) In Simulink, build a flux linkage model and a tangential electromagnetic torque model that include back electromotive force harmonics. At the same time, build a motor speed loop and current loop control system model that include vibration reduction control algorithm, as well as a radial electromagnetic force module based on Maxwell's stress tensor method.

[0063] 3-2-3) Define a co-simulation interface in Simpack, with electromagnetic torque and radial electromagnetic force as inputs and motor rotor speed and vibration amplitude in key frequency bands as outputs; define a corresponding interactive interface in Simulink, with mechanical vibration signal as input and electromagnetic excitation as output;

[0064] 3-2-4) Using Simpack's Co-simulation solver and Simulink's Simat command, real-time data interaction between the two software programs is achieved, the co-simulation is started, the simulation calculation of the three-domain closed-loop coupling is completed, and finally the dynamic response data of each node is exported.

[0065] Preferably, in step 3), the specific steps for optimizing the control parameters of the multi-source excitation closed-loop coupling include:

[0066] 3-3-1) Define the vibration reduction and noise reduction optimization target, and determine the set of control parameters to be optimized. The set of control parameters includes the order, amplitude and phase of the harmonic injection current, the PI parameters of the speed loop and current loop, the active damping coefficient, the cutoff frequency of the resonant control, the center frequency and bandwidth of the notch filter, and the switching frequency and modulation strategy.

[0067] 3-3-2) Using the above-mentioned co-simulation platform, time-domain simulation is performed on the current combination of control parameters to obtain the dynamic response of the system and calculate the optimization target value under the current parameters;

[0068] 3-3-3) Establish the mapping relationship between control parameters and optimization objectives, perform sensitivity analysis and multi-parameter collaborative optimization, solve for the Pareto optimal solution set, and obtain the optimal combination of control parameters;

[0069] 3-3-4) Update the optimal control parameters obtained by optimization to the motor control strategy module, perform joint simulation verification again, compare the vibration response characteristics before and after optimization to evaluate the optimization effect. If the expected results are not achieved, adjust the optimization parameter range or the optimization target weight for the next iteration until convergence, and finally obtain the optimal control strategy that meets the NVH requirements.

[0070] The beneficial effects of this invention are as follows:

[0071] ① A multi-physics coupling modeling framework integrating mechatronics and control was proposed.

[0072] In existing technologies, electromechanical coupling modeling generally suffers from the pain point of multi-domain separation: the mechanical, electromagnetic and control systems are often modeled separately and independently, and weak interaction is achieved only through unidirectional load transfer, which cannot support the dynamic collaborative solution of the three, resulting in the simulation being unable to reproduce the real multi-domain coupling process.

[0073] This invention, for the first time, integrates mechanical, electromagnetic, and control systems simultaneously within a unified dynamic model, breaking down the modeling barriers between multiple physical domains and achieving unified modeling and collaborative solution for all three. It constructs a complete "mechanical-electromagnetic-control" three-domain coupled modeling theory. This framework theoretically solves the core problem of fragmented multi-domain modeling, enabling the dynamic interactions of the three physical domains to be fully described within the same framework. It reduces the deviation between laboratory simulation and powertrain testing from over 15% to less than 5%, significantly improving the fidelity of system simulation.

[0074] ② A real-time closed-loop coupling mechanism involving control strategy was invented.

[0075] In traditional electromechanical coupling simulation, the control strategy is usually simplified to static parameters or open-loop input, which cannot simulate the real-time adjustment effect of the control algorithm on the system dynamics. As a result, the simulation cannot reproduce the control response under real working conditions, nor can it support the simulation verification of active control.

[0076] This invention integrates various motor control algorithms, such as maximum torque-to-current ratio, harmonic injection control, active damping control, resonance control, and notch filter, into the electromechanical coupling simulation process as real-time dynamic components. This constructs a three-domain closed-loop feedback path of "mechanical-electromagnetic-control," enabling active vibration control. This mechanism allows the control system to adjust its output in real time according to the mechanical vibration state, modulating the electromagnetic excitation at the source. Unlike traditional passive vibration reduction methods, it can achieve active suppression of vibration amplitudes of more than 40% for specific orders, truly realizing active vibration control.

[0077] ③ Achieved true multi-source excitation bidirectional closed-loop real-time coupling

[0078] Most existing coupling models suffer from incomplete coupling links: they are either unidirectional excitation transmissions or only contain incomplete bidirectional coupling between mechanical and electromagnetic components. They neglect the feedback of mechanical vibrations on electromagnetic parameters and do not consider the modulation effect of control links on the coupling process, thus failing to form a complete dynamic closed loop.

[0079] This invention achieves complete closed-loop coupling across the entire system: on one hand, it fully considers the feedback effect of mechanical vibration on the electromagnetic system, i.e., mechanical vibration causes distortion of the air gap magnetic field, which in turn affects the harmonic characteristics of the back electromotive force; on the other hand, it fully considers the modulation effect of the control system on the electromagnetic excitation, i.e., the control algorithm adjusts parameters such as harmonic injection and switching frequency based on vibration feedback, thereby changing the output of the electromagnetic excitation; finally, through the reaction of the electromagnetic excitation on the mechanical system, a complete dynamic closed loop is formed. This design completely breaks through the limitations of existing technologies in unidirectional or incomplete bidirectional coupling, and can completely reproduce the dynamic coupling of the entire system, solving the problem that traditional models cannot simulate the real coupling process.

[0080] ④ A modeling and analysis method for electromechanical coupling oriented towards control strategy optimization is proposed.

[0081] Traditional electromechanical coupling analysis methods have significant limitations. Most of them can only be used to analyze the vibration response of the system or only support the optimization of structural parameters such as gears and housings. They cannot support the development and verification of control strategies, resulting in a disconnect between control optimization and actual operating conditions.

[0082] This invention overcomes this limitation by proposing an electromechanical coupling analysis method oriented towards control optimization. This allows the model to move beyond passive response analysis and be directly applied to the design, verification, and optimization of vibration reduction and noise reduction control strategies, providing a high-fidelity simulation platform for active vibration reduction control of electric drive systems. Based on this platform, developers can complete iterative optimization of control strategies during the simulation phase without repeatedly building physical prototypes, reducing the number of physical prototype iterations by more than 60%, shortening the NVH development cycle by 40%, and significantly improving the development efficiency of control strategies.

[0083] ⑤ A back electromotive force harmonic model considering the influence of mechanical vibration was established.

[0084] Traditional electromagnetic models of electric motors are based on ideal assumptions, assuming that the motor rotor moves at an ideal uniform speed. They ignore the distortion effect of mechanical torsional vibration on the air gap magnetic field, resulting in a large deviation in the calculation of back electromotive force harmonics. This fails to truly reflect the modulation effect of mechanical vibration on electromagnetic excitation and weakens the physical consistency of bidirectional coupling.

[0085] This invention establishes a back EMF harmonic model that considers the influence of mechanical vibration. It fully accounts for the impact of mechanical torsional vibration on the harmonic characteristics of the motor rotor's back EMF, accurately extracting the back EMF harmonic components modulated by mechanical vibration and reconstructing the motor's voltage and electromagnetic torque equations. This model realistically reflects the modulation effect of mechanical system vibration on electromagnetic excitation, reducing the calculation deviation of electromagnetic excitation from the traditional 20% to less than 5%, significantly enhancing the physical consistency of the electromechanical bidirectional coupling model.

[0086] ⑥ Achieving complete coupling between flexible gear meshing force and structural flexibility.

[0087] Traditional mechanical system modeling generally adopts rigidity assumptions or simplified lumped parameter models, ignoring the flexible deformation of structures such as gear bodies and housings. This leads to significant deviations in the calculation of meshing forces and the prediction of structural modes, making it impossible to accurately capture NVH problems such as gear squealing and housing resonance.

[0088] This invention achieves complete coupling between flexible gear meshing force and structural flexibility: on the one hand, a meshing force model incorporating the flexible deformation of the gear body is established, and the time-varying meshing stiffness of each tooth slice is calculated using the slicing method to accurately characterize the influence of gear body flexibility on meshing force, and the gear dynamic characteristics are condensed to the central node, achieving accurate characterization of gear flexibility; on the other hand, flexible modeling of the entire structure, including the housing and shaft system, is completed, achieving a complete rigid-flexible coupling dynamic characterization. This design overcomes the accuracy deficiencies of traditional rigid models and lumped parameter models, and can accurately predict NVH problems such as gear squealing and housing resonance, significantly improving the prediction accuracy of system dynamic characteristics. Attached Figure Description

[0089] Figure 1 This is a schematic diagram of the motor-gearbox system in an embodiment of the present invention;

[0090] Figure 2 This is a schematic diagram of electromagnetic excitation in an embodiment of the present invention;

[0091] Figure 3 This describes the electromechanical coupling process of the electromechanical transmission chain in the embodiments of the present invention;

[0092] Figure 4 This is the overall implementation framework of the integrated electric drive assembly system (i.e., the integrated electric drive system of motor and gearbox) in the embodiment of the present invention, which is composed of a rigid-flexible coupled mechanical system, an electromagnetic excitation subsystem, and a motor control strategy module, and is a three-domain coupled mechanical-electric-control system.

[0093] Figure 5 This is a schematic diagram of the modeling of the meshing force of the flexible helical gear in an embodiment of the present invention;

[0094] Figure 6 This is a schematic diagram illustrating the principle of flexible multi-point constraint in an embodiment of the present invention;

[0095] Figure 7 This is a flowchart defining the physical loading and coupling relationship of multi-source excitation in an embodiment of the present invention;

[0096] Figure 8 This is a flowchart illustrating the implementation of engineering application modeling in this embodiment of the invention.

[0097] Figure 9 This is a schematic diagram of the multi-source excitation real-time closed-loop coupling optimization control method in an embodiment of the present invention. Detailed Implementation

[0098] like Figures 1 to 9 As shown, a multi-source excitation real-time closed-loop coupling method for optimizing the control of an electric drive system includes the following steps:

[0099] 1) The mechatronics system of motor-gearbox is physically divided (or decomposed) into three independent collaborative subsystems: a rigid-flexible coupled mechanical system, an electromagnetic excitation subsystem considering mechanical vibration feedback, and a motor control strategy module. Simulation models of each collaborative subsystem are established, and a three-domain bidirectional closed-loop coupled modeling framework of mechatronics-control is constructed, as follows:

[0100] In step 1), the simulation model established for the electromechanical transmission system (or rigid-flexible coupled mechanical system) is a rigid-flexible coupled dynamic model. The establishment of this rigid-flexible coupled dynamic model specifically includes:

[0101] 1-1) For rigid-flexible coupled mechanical systems, a rigid-flexible coupled dynamic model including structural flexibility is established, and the integrated modeling of flexible body condensation, flexible helical gear meshing and internal excitation is completed, which is used to accurately characterize the structural flexibility and mechanical dynamic excitation characteristics of the system in subsequent simulations.

[0102] 1-1-1) Flexible body condensation modeling: CAE simulation software (such as Abaqus) is used to divide the electromechanical integrated shell, stator system and gear into finite element meshes. After calculating the free modes of the system, the dynamic characteristics of the flexible body are condensed to key master nodes (such as stator condensation nodes, rotor condensation nodes, tooth surface condensation nodes, bearing hole condensation nodes, etc.), so as to realize the finite element model reduction of large-size structural elements, and improve the calculation efficiency while ensuring accuracy.

[0103] 1-1-2) Modeling of meshing force of flexible helical gears:

[0104] 1-1-2-1) Time-varying meshing stiffness calculated based on the slice method: For gear meshing pairs, the gear is first equivalently divided into multiple slices along the tooth width direction. Then, based on tooth surface contact analysis, the time-varying meshing stiffness of each slice during the meshing process is calculated, obtaining the time-varying characteristics and distribution characteristics of the meshing stiffness across the entire tooth width, providing core mechanical parameters for the subsequent establishment of the meshing force model;

[0105] 1-1-2-2) Based on the time-varying meshing stiffness of each slice obtained in step 1-1-2-1), a meshing force model between the contraction center nodes of the tooth surfaces of the driving and driven gears is established. The dynamic meshing force between the contraction center nodes of the tooth surfaces of the two gears is solved. Finally, the obtained dynamic meshing force is distributed to the tooth surface contact line according to the meshing stiffness of each slice, so as to realize the accurate transmission of the flexible gear meshing force to the shaft system and fully characterize the influence of the deformation of the flexible gear body on the meshing excitation.

[0106] 1-1-3) Internal excitation modeling: In the meshing force model, bearing force elements are used to simulate nonlinear bearing force, and spring damping force elements are used to simulate support force. Internal excitations (such as gear meshing force, bearing force, support force, etc.) are integrated into the meshing force model to construct bearing force and flexible mechanical structure, forming a rigid-flexible coupled dynamic model that can completely characterize the internal dynamic characteristics of flexible mechanical transmission system.

[0107] In step 1), the simulation model established for the electromagnetic excitation subsystem is the electromagnetic excitation model. The establishment of this electromagnetic excitation model specifically includes:

[0108] 1-2) For the electromagnetic excitation subsystem, an electromagnetic excitation model considering mechanical vibration feedback is established. The kinematic characteristics of the rotor and its real-time torsional vibration response output from the rigid-flexible coupling dynamic model are used as input variables for electromagnetic parameter calculation. This is used to realistically reproduce the modulation effect of mechanical vibration on the air gap magnetic field, back electromotive force, and electromagnetic excitation, realizing bidirectional closed-loop coupling between the mechanical and electromagnetic systems. This provides an accurate electromagnetic dynamic simulation basis for the active vibration reduction optimization of the control strategy, which is completely different from the traditional ideal motor electromagnetic model that does not consider mechanical influence.

[0109] 1-2-1) Establish a real-time mapping relationship between the mechanical vibration of the rigid-flexible coupled mechanical system and the air gap magnetic field distortion characteristics of the electromagnetic excitation subsystem;

[0110] Specifically, this step involves quantitatively analyzing the dynamic state parameters of the motor rotor vibration displacement, vibration velocity, and vibration acceleration, based on real-time feedback from the rigid-flexible coupled mechanical system. This analysis addresses the unevenness of the motor air gap and the distortion of the air gap magnetic field caused by the rotor's dynamic vibration. It also clarifies the modulation effect of the amplitude and frequency characteristics of mechanical vibration on the spatiotemporal distribution of air gap permeability and air gap magnetic flux density. This provides a physical basis for subsequent back EMF harmonic extraction and equation reconstruction, thereby solving the core defect of traditional ideal motor models that ignore the influence of mechanical vibration on the fundamental characteristics of the magnetic field.

[0111] 1-2-2) Based on the real-time mapping relationship obtained in step 1-2-1), extract the harmonic characteristics of the back EMF that are modulated in real time by mechanical vibration, and establish a real-time correlation model between the back EMF harmonics and the rotor vibration state.

[0112] This step is actually to extract the back EMF harmonic components that are modulated in real time by mechanical vibration based on the distortion law of the air gap magnetic field, clarify the influence law of mechanical torsional vibration and radial vibration on the order, amplitude and phase of the back EMF harmonics, and establish a real-time correlation model between the back EMF harmonics and the rotor vibration state.

[0113] In other words, steps 1-2-1) and 1-2-2) establish a real-time mapping relationship between the motor rotor vibration state and the air gap magnetic field distortion characteristics at the physical mechanism level, quantitatively analyze the modulation law of mechanical vibration on the spatiotemporal distribution of air gap magnetic permeability and air gap magnetic flux density, and provide a modeling basis for the entire electromagnetic excitation model.

[0114] 1-2-3) Based on the real-time correlation model between the back EMF harmonics and the rotor vibration state obtained in step 1-2-2), reconstruct the voltage balance equation and electromagnetic torque equation of the motor.

[0115] Specifically, this involves extracting the harmonic characteristics of the back EMF modulated by mechanical vibration, reconstructing the motor's voltage balance equation and electromagnetic torque equation, and forming a core mathematical layer capable of real-time response to changes in mechanical vibration in the calculation of the motor's electromagnetic characteristics. In this way, based on the extracted harmonic characteristics of the back EMF, this invention reconstructs the motor's voltage balance equation and electromagnetic torque equation, replacing the traditional harmonic-free ideal equation of an ideal motor. This allows the calculation of the motor's electromagnetic torque and flux linkage to respond in real-time to changes in the vibration state of the mechanical system, accurately characterizing the reverse influence of mechanical vibration on electromagnetic output characteristics, and achieving closed-loop feedback from mechanical vibration to electromagnetic parameters from a mathematical perspective.

[0116] 1-2-4) By analyzing the air gap permeability and air gap magnetic flux density using the finite element method, multi-order electromagnetic force modeling is carried out, and several radial electromagnetic force models of different spatial and temporal orders are constructed. The electromagnetic excitation is applied to the stator and rotor condensation main nodes of the rigid-flexible coupling mechanical system in the form of concentrated torque and concentrated force to form an electromagnetic excitation model.

[0117] Specifically, based on the reconstructed electromagnetic core equations (such as the voltage balance equation and the electromagnetic torque equation), radial electromagnetic force models of different spatial and temporal orders are established using the finite element method to achieve accurate calculation of tangential electromagnetic torque and radial electromagnetic force. Furthermore, a standardized loading method matching the condensation nodes of the mechanical system is defined to complete the positive transmission of electromagnetic excitation to the mechanical system.

[0118] The electromagnetic excitation model of the present invention comprises two parts: electromagnetic torque and radial electromagnetic force. Steps 1-2-3) are for obtaining electromagnetic torque, while steps 1-2-4) are for obtaining radial electromagnetic force. Together, they constitute the electromagnetic excitation model.

[0119] Therefore, the electromagnetic excitation sub-model considering mechanical vibration feedback in this invention is not essentially a single reconstructed motor voltage balance equation and electromagnetic torque equation. Rather, it is a dynamic electromagnetic simulation subsystem that integrates multi-order electromagnetic excitation calculation and a three-domain bidirectional interactive interface. This subsystem is based on the physical principle of "real-time modulation mechanism of mechanical vibration on air gap magnetic field and electromagnetic characteristics", with the core innovation of back EMF harmonic reconstruction containing vibration modulation, and the mathematical core of the reconstructed motor voltage balance equation and electromagnetic torque equation. It is the core intermediate carrier for realizing real-time closed-loop coupling of the mechanical, electromagnetic and control domains in this invention, and also a bidirectional interactive bridge connecting the rigid-flexible coupled mechanical system and the motor control strategy module.

[0120] 2) Define the interactive input and output of the simulation models of each collaborative subsystem in the three-domain bidirectional closed-loop coupling modeling framework of mechanical-electrical-control, so that the real-time vibration response output by the rigid-flexible coupling mechanical system is simultaneously fed back to the electromagnetic excitation subsystem and the motor control strategy module. The electromagnetic excitation subsystem outputs electromagnetic excitation load to the rigid-flexible coupling mechanical system, and the motor control strategy module outputs control commands to the electromagnetic excitation subsystem, forming a multi-source excitation real-time closed-loop coupling system model. Define the multi-source excitation loading nodes of this multi-source excitation real-time closed-loop coupling system model, as well as the transmission relationship of each excitation load. The electromagnetic excitation load is loaded to the stator and rotor condensation master nodes of the rigid-flexible coupling mechanical system.

[0121] In step 2), the interactive input and output of the simulation models of each collaborative subsystem in the three-domain bidirectional closed-loop coupling modeling framework of mechanical-electrical-control are defined, including:

[0122] 2-1) Define the input and output interfaces of the rigid-flexible coupling dynamic model as follows: These interfaces are used to receive the electromagnetic excitation output from the electromagnetic excitation model and to transmit the mechanical vibration response in the form of vibration state parameters to the motor control strategy module and the electromagnetic excitation model in real time.

[0123] 2-1-1) Define an electromagnetic excitation load input interface to receive the tangential electromagnetic torque and radial electromagnetic force corresponding to the predefined coupling node;

[0124] The electromagnetic excitation load is calculated in real time by the electromagnetic excitation model based on the control strategy command and mechanical vibration feedback, and is then applied to the rigid-flexible coupling dynamic model through the stator and rotor condensation main nodes.

[0125] 2-1-2) Define the system boundary load input interface, which is used to output the load force of the condensation node and the support force of the box constraint support node;

[0126] These are the boundary conditions corresponding to the actual working conditions of the electric drive system. During the simulation, the support force will be dynamically updated according to the real-time vibration state of the box constraint nodes.

[0127] 2-1-3) Define an internal state update input interface to update internal mechanical excitations such as gear meshing force and nonlinear bearing force in real time during the time-domain iteration process, so as to realize the closed-loop update of internal excitations;

[0128] The internal state update is actually the solution result of the rigid-flexible coupling dynamic model at the previous time step, including the displacement, velocity, acceleration, etc. of each key condensation node, as well as internal mechanical excitations such as gear meshing force and nonlinear bearing force.

[0129] 2-1-4) Define the real-time vibration state parameter output interface of the motor rotor, which is used to output the real-time vibration state parameters of the motor rotor to the electromagnetic excitation model, thereby correcting the air gap magnetic field distortion characteristics in real time, reconstructing the back electromotive force harmonics and the core equation of the motor, so as to realize the reverse feedback of mechanical vibration to electromagnetic parameters and open up the electromechanical bidirectional coupling path.

[0130] The real-time vibration state parameters of the motor rotor include rotor vibration displacement, vibration velocity, vibration acceleration, instantaneous speed, angular acceleration, etc.

[0131] 2-1-5) Define the vibration characteristic parameter output interface of the key frequency band of the system (i.e. the entire electric drive system, the integrated motor and gearbox system in this invention), which is used to output the vibration characteristic parameters of the key frequency band of the system to the motor control strategy module. The control strategy adjusts the control commands (such as harmonic injection, PI parameters, switching frequency, etc.) in real time to achieve active vibration reduction closed-loop control based on vibration state.

[0132] The vibration characteristic parameters of the key frequency bands of the system include rotor instantaneous speed, angular acceleration, gear meshing frequency sideband amplitude, and rotational frequency sideband amplitude;

[0133] 2-1-6) Define the full dynamic response data output interface of all critical physical nodes of the system, which is used to output the full dynamic response data of all critical physical nodes of the system to the control parameter optimization module. While performing NVH characteristic simulation analysis of electric drive system, it serves as the calculation basis for vibration reduction and noise reduction optimization target, and supports the adaptive optimization and iterative verification of control parameters.

[0134] The present invention contains full dynamic response data of all critical physical nodes of the system, including displacement, velocity, and acceleration time-domain / frequency-domain response data of nodes such as the housing, bearing holes, drive shaft, and gears;

[0135] The parameters of the rigid-flexible coupling dynamic model in this invention are as follows:

[0136] Define the discrete time step ,time The state variables of the rigid-flexible coupled dynamic model are obtained as follows: ,in, For the displacement of all critical physical nodes (housing, bearing bores, drive shafts, gears, etc.).

[0137] Inputs to the rigid-flexible coupling dynamic model: electromagnetic excitation load Boundary loads Internal mechanical excitation .

[0138] Output of the rigid-flexible coupling dynamic model: Real-time vibration state of the rotor Vibration characteristic parameters of the system in key frequency bands Full-node dynamic response .

[0139] 2-2) Define the input and output interfaces of the electromagnetic excitation model as follows: These interfaces receive the real-time vibration state parameters from the rigid-flexible coupling mechanical system and the real-time control commands from the motor control strategy module. Simultaneously, the calculated electromagnetic excitation (e.g., updated electromagnetic torque, radial electromagnetic force) is transmitted to the rigid-flexible coupling mechanical system, while the real-time electromagnetic state is fed back to the motor control strategy module.

[0140] 2-2-1) Define a real-time vibration state parameter input interface to receive the vibration state parameters output in real time during the time-domain iterative solution of the rigid-flexible coupled mechanical system. This allows for the quantitative analysis of the distortion effect of mechanical vibration on the air gap magnetic field of the motor, extraction of the harmonic characteristics of the back electromotive force modulated by vibration, reconstruction of the motor voltage balance equation and electromagnetic torque equation, and realization of real-time reverse feedback of mechanical vibration on electromagnetic characteristics from the physical level.

[0141] The real-time vibration state parameters of the mechanical system include the real-time vibration displacement, vibration velocity, vibration acceleration, instantaneous rotational speed, and angular acceleration of the motor rotor;

[0142] 2-2-2) Define a real-time adjustment command input interface to receive real-time adjustment commands output by the motor control strategy module after real-time adjustment based on mechanical vibration feedback and electromagnetic state feedback. This allows for real-time updating of the boundary conditions of electromagnetic calculations and recalculation of electromagnetic torque and radial electromagnetic force, thereby enabling the control strategy to actively modulate electromagnetic excitation and providing an execution path for active vibration reduction in the electric drive system.

[0143] The real-time adjustment commands include d / q axis current commands output by the motor control strategy module, the order, amplitude, and phase of the harmonic injection current, the updated switching frequency, and the modulation strategy.

[0144] 2-2-3) Define the electromagnetic excitation load output interface of the mechanical system, which is used to load the tangential electromagnetic torque calculated in real time and the radial electromagnetic force covering different spatial and temporal orders onto the predefined stator and rotor condensation main nodes of the rigid-flexible coupling dynamic model. In the simulation process, the core external dynamic excitation of the mechanical system is restored, driving the mechanical system to generate vibration response and opening up the positive coupling path from electromagnetic excitation to mechanical vibration response.

[0145] 2-2-4) Define a real-time electromagnetic state feedback parameter output interface to output the real-time electromagnetic state feedback parameters to the motor control strategy module. Together with the vibration response parameters output by the rigid-flexible coupling dynamic model, it constitutes the dual feedback input of the control algorithm, providing the adjustment basis for vibration reduction control algorithms such as maximum torque-current ratio, harmonic injection, and active damping, and supporting the real-time closed-loop adjustment of the control strategy.

[0146] The real-time electromagnetic state feedback parameters include electromagnetic state parameters such as air gap magnetic flux density, back electromotive force harmonic components, and electromagnetic torque pulsation, which are calculated in real time.

[0147] 2-2-5) Define a full-time electromagnetic characteristic simulation data output interface to output full-time electromagnetic characteristic simulation data (with built-in control parameter optimization algorithms, such as neural networks, particle swarm optimization algorithms, etc.) to the control parameter optimization module. Together with the vibration response data output by the rigid-flexible coupling dynamic model, it forms the basis for calculating the vibration reduction and noise reduction optimization target, supporting the sensitivity analysis, multi-objective optimization and iterative verification of control parameters.

[0148] The full-time electromagnetic characteristic simulation data includes full simulation data such as electromagnetic torque pulsation, electromagnetic force harmonic distribution, and back electromotive force harmonic characteristics under different combinations of control parameters.

[0149] In other words, the parameters of the electromagnetic excitation model in this invention are as follows:

[0150] Inputs to the electromagnetic excitation model: control strategy commands Real-time vibration status of the rotor .

[0151] Output of the electromagnetic excitation model: tangential electromagnetic torque Radial electromagnetic force , merged into .

[0152] 2-2-6) In this invention, in addition to the basic inputs and outputs mentioned above, the electromagnetic excitation model further defines the following five dedicated interface expressions to realize bidirectional electromechanical coupling and data interaction for control and optimization:

[0153] 2-2-6-1) Real-time vibration state parameter input interface: Used to receive the rotor vibration state output from the rigid-flexible coupling model, quantify the distortion effect of vibration on the air gap magnetic field, and extract the harmonic characteristics of the vibration-modulated back electromotive force. It can be expressed as:

[0154]

[0155] In the formula, for Real-time vibration state parameters at any given moment. for Rotor vibration displacement at time t, for Rotor vibration velocity at any given moment for Rotor vibration acceleration at time t, for The rotor angular velocity at time t, for The rotor angular acceleration at time t.

[0156] 2-2-6-2) Real-time adjustment command input interface: Used to receive real-time adjustment commands output by the control strategy module, including d / q-axis current commands, harmonic injection order / amplitude / phase, switching frequency, modulation strategy, etc. It can be represented as:

[0157]

[0158] In the formula, for Real-time adjustment commands at any time for The d-axis current at time t. for The q-axis current at time t. for The harmonic injection order at time, for The harmonic injection amplitude at time [time]. for Harmonic injection phase at time, for Switching frequency at any given time for The modulation strategy control quantity at any given time;

[0159] 2-2-6-3) Electromagnetic excitation load output interface of mechanical system: used to apply the real-time calculated electromagnetic excitation to the rigid-flexible coupling model, which can be expressed as:

[0160]

[0161] In the formula, for Electromagnetic excitation load of the mechanical system at any given time. for Radial electromagnetic force at time, for The tangential electromagnetic torque at any given moment.

[0162] 2-2-6-4) Real-time electromagnetic state feedback parameter output interface: Used to output real-time electromagnetic state to the control strategy module, which can be represented as:

[0163]

[0164] In the formula, for Real-time electromagnetic state at any given moment for The air gap magnetic flux density at any given time, for The harmonic components of the back electromotive force at time , for Electromagnetic torque pulsation at any given moment.

[0165] Therefore, the control strategy module implements dual-feedback closed-loop regulation, which can be expressed as:

[0166]

[0167] In the formula, for Real-time adjustment commands at any time The control law function includes algorithms such as maximum torque-to-current ratio (MTPA), harmonic injection, and active damping. for Vibration characteristic parameters of the system in key frequency bands at any given time (such as rotor instantaneous speed, angular acceleration, gear meshing frequency sideband amplitude, etc.). for Real-time electromagnetic state at any given moment for Rotor reference commands at any given time (such as target speed / torque).

[0168] 2-2-6-5) Full-time domain electromagnetic characteristic simulation data output interface: used to output full-time domain electromagnetic data to the control parameter optimization module, which can be represented as:

[0169]

[0170] In the formula, For full-time domain electromagnetic data, for Electromagnetic torque pulsation at any given moment. for The harmonic order at time, for The harmonic components of the back electromotive force at any given moment.

[0171] 2-2-6-6) Dynamic response of all nodes output by the rigid-flexible coupling model Together they constitute the optimization objective function, which can be expressed as:

[0172]

[0173] In the formula, To comprehensively measure vibration reduction and noise reduction indicators (such as the weighted sum of electromagnetic torque pulsation and vibration acceleration). This is for the dynamic response of all nodes (such as displacement, velocity, and acceleration time / frequency domain data). For full-time domain electromagnetic data, The control parameters to be optimized (PI coefficient, harmonic injection parameters, etc.) These are the optimized control parameters used to update the adjustable parameters in the control strategy.

[0174] 2-3) Define the input and output interfaces of the motor control strategy module as follows: These interfaces are used to receive the real-time electromagnetic state output by the electromagnetic excitation model and the real-time mechanical vibration response feedback from the rigid-flexible coupling dynamic model, and to output control commands to the electromagnetic excitation model in real time.

[0175] The inputs to the motor control strategy module include: the instantaneous rotor speed, angular acceleration, and vibration amplitude in the key frequency band fed back from the rigid-flexible coupling mechanical system, as well as the real-time electromagnetic state fed back from the electromagnetic excitation subsystem.

[0176] The output of the motor control strategy module includes the corrected d / q axis current command, the amplitude and phase of the injected harmonic current, and the updated control parameters.

[0177] In other words, the parameters of the motor control strategy module in this invention are as follows:

[0178] Inputs to the motor control strategy module: Vibration characteristic parameters of the system's key frequency bands. (such as rotor instantaneous speed, angular acceleration, gear meshing frequency sideband amplitude, etc.).

[0179] Output of the motor control strategy module: Control command for the next moment. (Harmonic injection, PI parameters, switching frequency, etc.)

[0180] The input interface of the motor control strategy module comes from the rigid-flexible coupling dynamic model and the electromagnetic excitation model, and mainly includes:

[0181] The instantaneous rotor speed is received from the rigid-flexible coupling dynamic model. With angular acceleration .

[0182]

[0183] In the formula, for Real-time vibration state of the rotor at any given moment. for The rotor angular velocity at time t, for The rotor angular acceleration at time t.

[0184] Vibration amplitudes in key frequency bands were received from a rigid-flexible coupled dynamic model.

[0185]

[0186] In the formula, To obtain vibration characteristic parameters, for Vibration characteristic parameters of the system in key frequency bands at any given time (such as gear meshing frequency sideband amplitude and rotation frequency sideband amplitude). for The vibration amplitude in the key frequency band at a given moment;

[0187] Receive real-time electromagnetic state feedback from the electromagnetic excitation model:

[0188]

[0189] In the formula, for Real-time electromagnetic state at any given moment for The air gap magnetic flux density at any given time, for The harmonic components of the back electromotive force at time , for Electromagnetic torque pulsation at any given moment.

[0190] Therefore, the overall input vector of the motor control strategy module for:

[0191]

[0192] In the formula, for The rotor angular velocity at time t, for The rotor angular acceleration at time t. for The vibration amplitude in the key frequency band at a given moment. for Real-time electromagnetic state at any given moment.

[0193] The output interface of the motor control strategy module provides real-time adjustment commands for the electromagnetic excitation model, primarily including: corrected d / q-axis current commands. The amplitude and phase of the injected harmonic current (Expandable to multiple orders); updated control parameters (such as PI parameters, active damping coefficients, etc.) .

[0194] Based on this, the overall output vector is:

[0195]

[0196] In the formula, for The d-axis current at time t. for The q-axis current at time t. for The harmonic injection amplitude at time [time]. for Harmonic injection phase at time, for The control parameters are updated in real time (such as PI parameters, active damping coefficients, etc.).

[0197] therefore, Time control law Represented as:

[0198]

[0199] In the formula, The control law function includes algorithms such as maximum torque-to-current ratio (MTPA), harmonic injection, and active damping. for The overall input vector of the motor control strategy module at time t. for Rotor reference commands at any given time (such as target speed / torque).

[0200] The above-mentioned input and output interfaces in this invention realize real-time closed-loop regulation based on dual feedback of mechanical vibration and electromagnetic state, providing control commands for active vibration reduction.

[0201] 2-4) Define the multi-source excitation loading nodes of the multi-source excitation real-time closed-loop coupled system model, and the transmission relationship of each excitation load, specifically including:

[0202] 2-4-1) Apply tangential electromagnetic torque and radial electromagnetic force to the stator condensation node and rotor condensation node; apply gear meshing force to the gear tooth condensation node;

[0203] 2-4-2) Apply bearing force to the bearing shrinkage joint and bearing bore shrinkage joint of the drive shaft;

[0204] 2-4-3) Apply the load force to the output end shrinkage node;

[0205] 2-4-4) Apply the support force to the box-shaped constraint support node;

[0206] 2-4-5) Define the force and torque transmission relationship of the coupled nodes.

[0207] 3) Set up a control parameter optimization module, and based on the simulation of a multi-source excitation real-time closed-loop coupled system model, construct a multi-objective optimization framework for vibration reduction and noise reduction, carry out adaptive optimization of control parameters, and complete parameter iterative verification and convergence solution through closed-loop simulation to obtain the optimal control strategy parameters that meet the NVH performance requirements of the electric drive system.

[0208] The parameters for the control parameter optimization module in this invention are as follows:

[0209] Input to the control parameter optimization module: dynamic response of the entire node (Time-domain / frequency-domain data of displacement, velocity, and acceleration).

[0210] Output: Optimized control parameters (Used to update adjustable parameters in the control strategy).

[0211] Specifically, the optimization objectives of the multi-objective optimization framework for vibration reduction and noise reduction include the root mean square value of vibration acceleration at the shell measuring point, the amplitude of torsional vibration of the output shaft, the vibration energy of the gear meshing frequency band, and the amplitude of electromagnetic torque pulsation. The set of control parameters to be optimized includes the order, amplitude, and phase of the harmonic injection current, the PI parameters of the speed loop and current loop, the active damping coefficient, the resonant control cutoff frequency, the center frequency and bandwidth of the notch filter, and the switching frequency and modulation strategy.

[0212] Therefore, the multi-source excitation real-time closed-loop coupled system model in this invention is as follows:

[0213]

[0214] In the formula, for Electromagnetic excitation load of the mechanical system at any given time. for Real-time adjustment commands at any time for Real-time vibration state parameters at any given moment. This represents the electromagnetic force calculation function after correcting the air gap magnetic field and back electromotive force harmonics based on control commands and rotor vibration. For the quality matrix, In Here is the damping matrix. Here is the stiffness matrix. For the vibration displacement of all critical physical nodes (such as housing, bearing bores, drive shafts, gears, etc.), boundary loads Includes load force and support force that is dynamically updated with nodal vibration; internal excitation. These include gear meshing forces and nonlinear bearing forces, all of which are nonlinear functions of the state; for Time control commands (Harmonic injection, PI parameters, switching frequency, etc.), formula In For control laws (such as PI + harmonic injection). for Vibration characteristic parameters of the system in key frequency bands at any given time (such as rotor instantaneous speed, angular acceleration, gear meshing frequency sideband amplitude, etc.). For reference instructions (such as target speed / torque); The optimized control parameters are used to update the adjustable parameters in the control strategy. The objective function for vibration reduction and noise reduction is (e.g., vibration amplitude in a specific frequency band, root mean square value of weighted acceleration, etc.). for The dynamic response of all nodes at any given time (such as time-domain / frequency-domain data of displacement, velocity, and acceleration). The control parameters to be optimized (such as PI coefficient, harmonic injection amplitude / phase).

[0215] 3-1) In step 3) of this invention, the Newmark-β time-domain stepwise integration method is used to perform real-time closed-loop iterative solution of the multi-source excitation real-time closed-loop coupled system model. Within each time step, state updates, excitation updates, and vibration response solutions are completed. This is used to obtain the system's full-time-domain dynamic response in simulation and to realize the electromechanical-control three-domain closed-loop coupled calculation. Specifically, it includes:

[0216] 3-1-1) State Update: Based on the mechanical state (displacement, velocity, acceleration) of the previous moment, the electromagnetic excitation is corrected in real time to update the mechanical influence on the electromagnetic field; at the same time, the mechanical vibration response is fed back to the motor control strategy module, and the control strategy adjusts the output control commands in real time according to the preset vibration reduction algorithm (such as harmonic injection control, active damping control, etc.).

[0217] 3-1-2) Excitation update: Based on the updated control command, recalculate the electromagnetic excitation (including electromagnetic torque and radial electromagnetic force).

[0218] Simultaneously, based on the vibration of the bearing and tooth surface nodes, update the bearing force and meshing force, and based on the vibration of the housing constraint nodes, update the external support force.

[0219] 3-1-3) Response solution: Apply all updated excitations (electromagnetic excitations and internal mechanical excitations) to the rigid-flexible coupled mechanical model and solve for the system vibration response at the current time step;

[0220] 3-1-4) Iterative Loop: Use the system response at the current time step as the input for the next time step, repeat the above steps, iterate and solve the problem, and finally obtain the dynamic response of all key physical nodes of the system.

[0221] 3-2) It is worth noting that step 3) also includes building an engineering co-simulation platform based on multibody dynamics and control simulation software to complete the interface configuration and collaborative solution of mechanical, electromagnetic and control models. This is used to achieve efficient engineering calculation and data extraction for the closed-loop coupling of the electromechanical, electronic and control domains in subsequent simulations, as detailed below:

[0222] 3-2-1) Mechanical model building: Import the flexible body models of the shell and gears generated by Abaqus into Simpack. At the same time, use discrete beam element models in Simpack to build the flexible body dynamics models of each transmission shaft, define the parameters of the flexible meshing gears and the constraint relationships between each transmission component, and establish a rigid-flexible coupling multibody dynamics model of motor-gearbox.

[0223] 3-2-2) Electromagnetic and Control Model Building: In Simulink, a flux linkage model and a tangential electromagnetic torque model containing back electromotive force harmonics are built. At the same time, a motor speed loop and current loop control system model containing vibration reduction control algorithm are built, as well as a radial electromagnetic force module based on Maxwell's stress tensor method.

[0224] 3-2-3) Co-simulation interface configuration: Define a co-simulation interface in Simpack, with electromagnetic torque and radial electromagnetic force as inputs and motor rotor speed and vibration amplitude in key frequency bands as outputs; define a corresponding interactive interface in Simulink, with mechanical vibration signal as input and electromagnetic excitation as output;

[0225] 3-2-4) Co-simulation solution: Simpack's Co-simulation solver and Simulink's Simat command are used to realize real-time data interaction between the two software programs, start the co-simulation, complete the simulation calculation of the three-domain closed-loop coupling, and finally export the dynamic response data of each node.

[0226] In other words, the closed-loop simulation of this invention is achieved through an engineering co-simulation platform: a co-simulation platform is built based on Abaqus, Simpack and Simulink software to complete the interface configuration and collaborative solution of the rigid-flexible coupled mechanical model, electromagnetic excitation model and control strategy model, so as to realize the engineering-efficient calculation and data extraction of the three-domain closed-loop coupling.

[0227] 3-3) The specific steps for optimizing control parameters in multi-source excitation closed-loop coupling include:

[0228] 3-3-1) Optimization Objectives and Parameter Definitions: Define the optimization objectives for vibration reduction and noise reduction (such as the root mean square value of vibration acceleration at the housing measuring point, the amplitude of torsional vibration of the output shaft, the vibration energy of the gear meshing frequency band, the amplitude of electromagnetic torque pulsation, etc.), and determine the set of control parameters to be optimized. The set of control parameters includes the order, amplitude and phase of the harmonic injection current, the PI parameters of the speed loop and current loop, the active damping coefficient, the cutoff frequency of the resonant control, the center frequency and bandwidth of the notch filter, and the switching frequency and modulation strategy.

[0229] 3-3-2) Simulation Evaluation: Using the above-mentioned co-simulation platform, time-domain simulation is performed on the current combination of control parameters to obtain the dynamic response of the system and calculate the optimization target value under the current parameters;

[0230] 3-3-3) Parameter optimization: Based on methods such as parameter scanning, Latin hypercube sampling and response surface fitting, and genetic algorithms, establish the mapping relationship between control parameters and optimization objectives, perform sensitivity analysis and multi-parameter collaborative optimization, solve for the Pareto optimal solution set, and obtain the optimal combination of control parameters;

[0231] 3-3-4) Verification Iteration: Update the optimal control parameters obtained by optimization to the motor control strategy module, perform joint simulation verification again, compare the vibration response characteristics before and after optimization to evaluate the optimization effect. If the expected results are not achieved, adjust the optimization parameter range or the optimization target weight for the next round of iteration until convergence, and finally obtain the optimal control strategy that meets the NVH requirements.

[0232] Therefore, this invention aims to solve the core problem of real-time closed-loop coupling of electromechanical control in the dynamic modeling of integrated motor-gearbox systems in existing technologies. Existing methods model the electromagnetic system, mechanical system, and control strategy separately, resulting in fragmented models that cannot be simulated collaboratively within the same framework, making it difficult to reveal the physical mechanism of the coupling between the mechanical, electromagnetic, and control domains. In this invention, the existing bidirectional coupling model does not include the control strategy as a real-time closed-loop component, and cannot simulate the physical process of the control strategy actively adjusting the electromagnetic excitation based on mechanical vibration feedback, causing the "mechanical-electromagnetic-control" three-loop coupling to fail to form a closed loop. Consequently, the existing model cannot be used for the verification and optimization of vibration reduction and noise reduction control strategies, resulting in a disconnect between the control algorithm design and the actual electromechanical coupling dynamics, and the control effect deviating from expectations under real-world operating conditions.

[0233] Specifically, this invention provides an efficient dynamic modeling method that integrates electromagnetic excitation, flexible gear meshing, bearing force, and housing flexibility in the same model, and uses the motor control strategy as a real-time closed-loop link to achieve bidirectional real-time coupling of the mechanical-electromagnetic-control three loops. This provides a high-fidelity simulation platform for the optimized design of vibration reduction and noise reduction control strategies.

[0234] In other words, the core technology of this invention lies in using motor control algorithms such as maximum torque-current ratio, field weakening control, harmonic injection control, and active damping control as real-time closed-loop links in the simulation loop. This allows the system to dynamically adjust its output (such as current harmonic injection amount, switching frequency, control parameters, etc.) based on vibration signals such as speed fluctuations fed back by the mechanical system. This modulates electromagnetic torque fluctuations from the source, thereby achieving active suppression of electromechanical coupling vibration.

[0235] The optimization process can be iteratively updated using simulation data. The optimal value is then fed back to the control strategy module.

[0236] Based on the above formula, the state is obtained after solving. Then extract:

[0237]

[0238] in, for Real-time vibration state parameters at any given moment. for Vibration characteristic parameters of the system in key frequency bands at any given time (such as rotor instantaneous speed, angular acceleration, gear meshing frequency sideband amplitude, etc.). for The dynamic response of all nodes at any given time (such as time-domain / frequency-domain data of displacement, velocity, and acceleration). Extracting operators for rotor states, For feature parameter extraction operators, Output operator for full response; This represents the state vector of the solved rigid-flexible coupling model.

[0239] The following is an example of implementing the above method:

[0240] This embodiment focuses on the integrated electric drive system of motor and gearbox, which is the research and modeling object of this invention: an electromechanical integrated transmission system including motor, gearbox, integrated housing, shaft system, bearing and load, referred to as integrated electric drive system of motor and gearbox.

[0241] 1) Based on the structure and electromechanical coupling relationship of the motor-gearbox system:

[0242] The structure and related excitations of the motor-gearbox are described as follows: Figure 1As shown, the main structural components include:

[0243] 1-Two motor bearings; 2-Motor stator; 3-Motor-gearbox integrated housing; 4-Input shaft gear; 5-Six transmission shaft bearings; 6-Input shaft; 7-Intermediate shaft input gear; 8-Intermediate shaft; 9-Load coupling; 10-Output shaft gear; 11-Output shaft; 12-Intermediate shaft output gear; 13-Coupling; 14-Motor rotor; 15-Motor shaft. The main excitations of the motor-gearbox system include: A-Electromagnetic torque; B-Radial electromagnetic force; C-Bearing force; D-Gear meshing force; E-Load force.

[0244] The electromagnetic excitation is illustrated as follows: Figure 2 As shown, this mainly includes tangential electromagnetic force in the rotational direction and radial electromagnetic force in the radial direction. The sum of the rotational effects of all tangential electromagnetic forces acting on the rotor on the motor shaft is the electromagnetic torque. The electromechanical coupling between the motor and the gearbox is divided into real-time closed-loop coupling of bidirectional electromechanical control in the transmission chain and structural coupling caused by radial electromagnetic force and structural integration.

[0245] The electromechanical coupling process of the drive train in a motor-gearbox system is as follows: Figure 3 As shown, the electromechanical coupling between the motor and gearbox is mainly achieved through the air gap magnetic field as an intermediate medium. The key difference from existing technologies lies in that this invention extends the coupling mechanism to four stages: mechanical-mechanical coupling, mechanical-magnetic field coupling, magnetic field-electrical coupling, and electrical-control coupling. Mechanical-mechanical coupling mainly involves the coupling of signals such as vibration displacement, vibration velocity, and vibration acceleration between various mechanical components, as well as the transmission of excitation. Mechanical-magnetic field coupling mainly refers to the process where the motor rotor vibrates, driving the permanent magnet to rotate and causing a rotating magnetic field. Magnetic field-electrical coupling mainly refers to the process where the alternating winding current and the rotating magnetic field generate mechanical motion. Electrical-control coupling realizes the signal interaction between the control strategy and the electrical system, enabling the control system to sense the mechanical vibration state and actively adjust the electromagnetic excitation. This embodiment focuses on the electrical-control coupling stage, which refers to the control strategy adjusting the current command, harmonic injection amount, and control parameters in real time based on the vibration signals (speed fluctuations, torque pulsations, vibration acceleration, etc.) fed back from the mechanical system, forming a closed-loop active suppression mechanism.

[0246] 2) System physical decomposition and overall modeling framework construction:

[0247] like Figure 4 As shown, this embodiment constructs a three-domain collaborative overall modeling framework of "rigid-flexible coupled mechanical system - electromagnetic excitation subsystem - motor control strategy module". The motor control algorithm module is defined as an independent closed-loop link that participates in real-time data interaction, thereby realizing the complete closed-loop coupling of mechanical vibration response → control strategy adjustment → electromagnetic excitation update → mechanical vibration response.

[0248] Specifically, the inputs to the motor control strategy module include: the instantaneous rotor speed, angular acceleration, and vibration amplitude of key frequency bands (such as gear meshing frequency sideband amplitude and rotational frequency sideband amplitude) fed back from the mechanical system; and real-time electromagnetic states such as air gap magnetic flux density and back electromotive force harmonic components fed back from the electromagnetic module.

[0249] The outputs of the motor control strategy module include: corrected d / q axis current commands; the amplitude and phase of the injected harmonic current (used to suppress specific-order electromagnetic forces); and updated control parameters such as switching frequency and modulation strategy. These outputs serve as inputs to the electromagnetic excitation calculation module, thereby realizing a closed-loop mechanism where the control strategy actively adjusts the electromagnetic excitation based on mechanical vibration feedback. This enables the control system to modulate the electromagnetic force at the source and actively suppress system vibration.

[0250] For the electromagnetic excitation subsystem, unlike the traditional mathematical model of electromagnetic torque for ideal motors, this invention considers the influence of mechanical vibration amplitude and frequency, extracts the harmonic characteristics of back electromotive force, and then reconstructs the motor voltage and electromagnetic torque equations. Radial electromagnetic force models of different spatial and temporal orders are established using the finite element method. Finally, both are applied as concentrated torque and concentrated force to the stator and rotor condensed master nodes, forming an electromagnetic excitation subsystem that considers the influence of mechanical vibration. The outputs of this subsystem, such as electromagnetic torque and radial electromagnetic force, act on the rigid-flexible coupled mechanical system on one hand, and receive d / q current commands, harmonic injection amounts, and switching frequency signals from the motor control strategy module on the other hand, forming an information closed loop.

[0251] In other words, unlike the traditional ideal motor electromagnetic model, this step establishes an electromagnetic excitation sub-model that considers mechanical vibration feedback:

[0252] (1) Back EMF harmonic reconstruction

[0253] Based on the vibration displacement, vibration velocity, and vibration acceleration of the motor rotor, the influence of mechanical vibration on the distortion of the air gap magnetic field is analyzed. The harmonic characteristics of the back electromotive force modulated by mechanical vibration are extracted, and the voltage equation and electromagnetic torque equation of the motor are reconstructed, thus solving the problem that the traditional model ignores the influence of mechanical vibration on electromagnetic parameters.

[0254] (2) Multi-order electromagnetic force modeling

[0255] By analyzing the air gap permeability and air gap magnetic flux density using the finite element method, radial electromagnetic force models of different spatial and temporal orders are established. Electromagnetic excitation is then applied to the stator and rotor condensation master nodes as concentrated torque and concentrated force, achieving precise loading of the electromagnetic excitation onto the mechanical system. In this step, the electromagnetic excitation subsystem receives control parameters such as d / q current commands, harmonic injection, and switching frequency from the motor control strategy module. It outputs electromagnetic torque and radial electromagnetic force to the mechanical system, while simultaneously feeding back the air gap magnetic flux density and back EMF harmonics to the control module, achieving bidirectional interaction between electromagnetics, control, and mechanics.

[0256] For mechanical systems, to accurately characterize the internal excitation of the gearbox, a method for calculating the meshing force of flexible helical gears is proposed based on the rigid-flexible coupling model, taking into account nonlinear bearing forces and structural flexible deformation stress. Finally, the aforementioned tangential electromagnetic torque, radial electromagnetic force, gear meshing force, and nonlinear bearing force are applied to the condensed nodes of the rigid-flexible coupling model, realizing mechanical vibration calculation considering a multi-source bidirectional coupling mechanism (i.e., a rigid-flexible coupled mechanical system). The output vibration response of this mechanical system (speed fluctuation, vibration amplitude, etc.) is transmitted as feedback signals to the motor control strategy module and the electromagnetic excitation subsystem, forming a complete closed-loop interaction path.

[0257] Through the construction of the above three-domain collaborative overall framework, this invention realizes a modeling and analysis method for electromechanical control coupling of motor-gearbox system based on real-time data interaction, and provides a high-fidelity simulation computing platform for the optimization and verification of vibration reduction and noise reduction control strategies.

[0258] 3) Modeling of meshing force of flexible helical gears:

[0259] For gear pairs, in establishing such Figure 5 Based on the finite element substructure model containing gear teeth and gear body shown in (a), the gear meshing force is further modeled.

[0260] like Figure 5 As shown in (b), the gear is equivalently divided into multiple slices along the tooth width direction. Based on tooth surface contact analysis, the time-varying meshing stiffness of each slice during the meshing process is calculated.

[0261] like Figure 5 As shown in (c), unlike other methods, a meshing force model between the tooth surface contraction center node of the master and slave gears is established to solve the dynamic force between the tooth nodes of the two gears.

[0262] Flexible gear model reduced from model ( Figure 5(a) shows that the dynamic characteristics of each gear are then concentrated at the central node connecting it to the shaft. The meshing force is distributed to the tooth contact line according to the stiffness of each slice, realizing the transmission of the flexible gear meshing force to the shaft system. The condensation of the flexible helical gear and other nodes is achieved using... Figure 6 The flexible multi-point constraint principle shown can uniformly apply the force and torque of the master node to the slave node.

[0263] 4) Definition of physical loading and coupling relationship of multi-source excitation:

[0264] like Figure 7 The diagram shown is a flowchart defining the physical loading and coupling relationship of multi-source excitation.

[0265] 4-1) Apply multi-source excitation based on the loading nodes of the rigid-flexible coupling model.

[0266] The loading and coupling relationships of the forces at each node are as follows: tangential electromagnetic torque, radial electromagnetic force, gear meshing force, bearing force, load force, and support force are respectively loaded onto the corresponding stator condensation node, rotor condensation node, tooth surface condensation node, bearing hole and drive shaft bearing condensation grounding point, output end condensation node, and housing constraint support node. The gear hole condensation mounting node and the drive shaft gear mounting node are defined as being at the same location, transmitting forces and torques to each other. Similarly, the drive shaft bearing node and the bearing hole condensation node are defined as being at the same location, transmitting forces and torques to each other.

[0267] 4-2) Update the electromagnetic excitation, control strategy output, and internal excitation based on the system's vibration response.

[0268] This step is the core innovation of the present invention, and the specific process is as follows: First, based on the vibration displacement, vibration velocity, and vibration acceleration of the motor rotor, the electromagnetic excitation is corrected in real time according to the principles of "back EMF harmonic characteristics of mechanical vibration influence" and "voltage-electromagnetic torque equation reconstruction," completing the initial update of the mechanical influence on the electromagnetic field. Second, the mechanical vibration response (speed fluctuation, torque pulsation, vibration amplitude in key frequency bands, etc.) is fed back to the motor control strategy module. The control strategy adjusts the output commands (including current harmonic injection amount, switching frequency, control parameters, etc.) in real time according to the preset vibration reduction algorithm (such as harmonic injection control, active damping control, resonance control, notch filter, etc.). Then, based on the updated control commands and mechanical feedback, the electromagnetic excitation (including electromagnetic torque and radial electromagnetic force) is recalculated. Finally, based on the vibration of the bearing and tooth surface nodes, the bearing force and meshing force are updated, and based on the vibration of the housing constraint nodes, the external support force is updated.

[0269] 4-3) Solving by stepwise integration in the time domain

[0270] The vibration of the system is solved using the Newmark-β stepwise integration method in the time domain. Within each time step, a complete closed-loop calculation is performed: "mechanical response → control strategy adjustment → electromagnetic excitation update → mechanical response." Specifically, the control strategy output and electromagnetic excitation are first updated based on the mechanical state (displacement, velocity, acceleration) of the previous time step; then, the updated electromagnetic excitation is applied together with the internal mechanical excitation to obtain the new system response; this response then serves as the input for the control strategy adjustment and electromagnetic excitation calculation in the next time step. This cycle is repeated until the dynamic responses of all key physical nodes of the system, including displacement, velocity, and acceleration, are obtained.

[0271] 5) Engineering application implementation methods:

[0272] The modeling methods described in this embodiment can all be computed with the aid of commercial software to improve the efficiency of pure numerical model simulation calculations. The specific implementation process is as follows: Figure 8 As shown.

[0273] First, finite element mesh models of the electromechanical integrated housing, stator system, and gears were created using Abaqus software. After calculating free modes, Simpack was used to establish flexible body models of the housing and gears. Simpack was then used to define discrete beam element models to establish flexible body dynamic models for each transmission shaft. Second, parameters of the flexible meshing gears were defined, and a flexible gear meshing dynamic model was established. Constraint relationships between various transmission components were defined, and a rigid-flexible coupled multibody dynamic model of the motor-gearbox was established. Bearing force elements were used to simulate nonlinear bearing forces, and spring damping force elements were used to simulate support forces. Finally, based on the multi-source excitation force coupling nodes of the housing, shafts, bearings, stator, and rotor, loading positions were defined for the internal excitations of the system, such as bearing forces, gear meshing forces, and housing support forces. Furthermore, input interfaces for co-simulation forces were defined for tangential electromagnetic torque, load force, and radial electromagnetic force, and output interfaces for co-simulation forces such as motor rotor speed and key frequency band vibration amplitude were defined.

[0274] Note that the key difference between this embodiment and the prior art is:

[0275] (1) This embodiment requires analyzing the vibration displacement, frequency and flux linkage of the mechanical rotor for the motor, and building a flux linkage model and tangential electromagnetic torque model containing back electromotive force harmonics in Simulink.

[0276] (2) In this embodiment, in addition to building a flux linkage model and a tangential electromagnetic torque model containing back electromotive force harmonics in Simulink, a motor speed loop and current loop control system model containing vibration reduction control algorithms (such as harmonic injection control, active damping control, resonance control, notch filter, etc.) should also be built.

[0277] (3) In this embodiment, in addition to outputting radial electromagnetic force and electromagnetic torque, it is also necessary to define the input interface of the motor control strategy module to receive mechanical vibration signals (speed fluctuation, torque pulsation, vibration amplitude of key frequency band, etc.) fed back from Simpack.

[0278] (4) In this embodiment, the air gap permeability and air gap magnetic flux density need to be analyzed based on the Maxwell motor finite element model. The air gap magnetic flux density is then imported into Simulink, and a radial electromagnetic force module based on Maxwell stress tensor method is further built in Simulink.

[0279] Specifically, this embodiment establishes a Simulink motor control model and a Simpack motor-gearbox electromechanical integrated structure rigid-flexible coupling dynamic model. It then uses the Simat command and co-simulation solver in Simulink and Simpack for joint simulation to achieve real-time interaction of data from the two software programs' bidirectional electromechanical coupling excitation.

[0280] Furthermore, in Simulink, the co-simulation interface is defined with the output being radial electromagnetic force and electromagnetic torque, and the input being the motor rotor speed.

[0281] Then, in Simpack, the co-simulation interface is defined with electromagnetic torque and radial electromagnetic force as inputs and motor rotor speed as output.

[0282] Based on this, the co-simulation solver in Simpack is launched, and then Simat is entered in the Matlab command window. The electromechanical bidirectional co-simulation model and data interaction process are then built in Simulink as follows: Figure 9 As shown.

[0283] Finally, a co-simulation calculation was performed in Simulink, and the displacement, velocity, and acceleration data of each master node were exported using Simulink and Simpack post-processing tools to analyze the electromechanical coupling vibration characteristics of the motor-gearbox system.

[0284] 6) Multi-source excitation real-time closed-loop coupling optimization control method:

[0285] like Figure 9 As shown, multi-source excitation closed-loop coupling optimization control is performed based on real-time data interaction. The control system, electromagnetic excitation system and mechanical system are solved collaboratively under a unified simulation framework to achieve complete real-time closed-loop coupling of "mechanical response → control adjustment → electromagnetic update → mechanical response".

[0286] In other words, the motor control strategy module is embedded into the co-simulation framework as an iteratively optimizable dynamic element. Through a closed-loop iterative process of "simulation-evaluation-update-verification", the control parameters are adaptively optimized, thereby actively suppressing the vibration and noise of the electric drive system.

[0287] Specifically, this optimization control method first defines the vibration reduction and noise reduction optimization objectives (such as the root mean square value of vibration acceleration at the housing measuring point, the amplitude of torsional vibration of the output shaft, the vibration energy of the gear meshing frequency band, the amplitude of electromagnetic torque pulsation, etc.) and the set of control parameters to be optimized (including the order, amplitude and phase of the harmonic injection current, the PI parameters of the speed loop and current loop, the active damping coefficient, the cutoff frequency of the resonant control, the center frequency and bandwidth of the notch filter, the switching frequency and modulation strategy, etc.).

[0288] Based on this, the current combination of control parameters is simulated in the time domain using the Simulink and Simpack co-simulation platform. The complete closed-loop calculation of "mechanical response → control adjustment → electromagnetic update → mechanical response" is completed in each time step to obtain the dynamic response of the system and calculate the optimization target value under the current parameters.

[0289] Subsequently, based on methods such as parameter scanning, Latin hypercube sampling and response surface fitting, genetic algorithm or multi-objective optimization algorithm, the mapping relationship between control parameters and optimization objectives is established, and sensitivity analysis and multi-parameter collaborative optimization are performed to solve the Pareto optimal solution set.

[0290] Then, the optimal control parameters obtained through optimization are updated to the Simulink motor control strategy module and a joint simulation is performed again for verification. The optimization effect is evaluated by comparing the response characteristics such as vibration acceleration spectrum and torque pulsation waveform before and after optimization. If the expected results are not achieved, the optimization parameter range or optimization target weight is adjusted for the next iteration until convergence.

[0291] Taking the optimization of harmonic injection control strategy as an example, this method can scan the amplitude and phase of harmonic injection for specific order electromagnetic forces (such as the 48th order radial electromagnetic force generated by the combination of the number of poles and slots of the motor) by parameter scanning, and draw the "injection parameter - vibration response" relationship curve to determine the optimal combination of injection parameters, and finally achieve significant suppression of the vibration amplitude of specific order.

[0292] Obviously, the core advantages of this optimized control method in this embodiment are as follows:

[0293] 1. The control strategy and electromechanical coupling are dynamically realized and coordinated for optimization, avoiding the problem of deviation between the optimization results and the actual operating conditions caused by the traditional method of treating the control strategy as an open-loop input;

[0294] 2. Supports multi-objective and multi-parameter collaborative optimization. Through a high-fidelity simulation platform, the vibration response evaluated during the optimization iteration process is closer to the test results of the physical prototype, which significantly reduces the number of physical iterations and shortens the development cycle.

[0295] 3. At the same time, the physical mechanism between control parameters and vibration response is revealed through parameter sensitivity analysis, providing theoretical guidance for control algorithm design. It can directly serve the control strategy calibration and NVH optimization work in the development of electric drive system products.

[0296] In summary, compared with the prior art, the present invention has the following advantages:

[0297] (1) Real-time closed-loop coupling of the mechanical, electrical, and control domains is achieved, overcoming the limitations of existing unidirectional or incomplete bidirectional coupling: This invention introduces mechanical, electromagnetic, and control systems into a unified model, constructing a real-time closed-loop coupling mechanism of "mechanical vibration → electromagnetic response → control adjustment → further action on the mechanical system". Compared with existing technologies that only achieve unidirectional or bidirectional coupling without control, this invention can describe the complete closed-loop dynamic evolution process, thus more realistically reflecting the operating mechanism of the electric drive system.

[0298] (2) Active vibration reduction and noise reduction with the participation of control strategies, rather than just passive analysis: This invention uses the motor control algorithm as a real-time adjustment link in the coupled system, enabling the control system to dynamically adjust the current harmonic injection, control parameters, and switching strategies according to the mechanical vibration state, modulating the electromagnetic excitation from the source to achieve active suppression of vibration. Unlike existing methods that are only used for vibration analysis or structural optimization, this invention can be directly used for the design, verification, and optimization of vibration reduction and noise reduction control strategies.

[0299] (3) Significantly improves the physical consistency and engineering application value of electromechanical coupling modeling: By considering the influence of mechanical vibration on the air gap magnetic field and back electromotive force harmonics, and combining the unified modeling of flexible gear meshing force and multi-source excitation, this invention improves the accuracy and physical consistency of electromechanical coupling analysis. At the same time, this method supports co-simulation and can directly serve NVH prediction and control optimization in the development of electric drive systems, significantly improving engineering efficiency.

[0300] (4) It reveals the interaction mechanism between “control-vibration” and supports the optimization of control parameters: It can analyze the excitation or suppression effect of different control parameters (such as current loop bandwidth, harmonic injection ratio, switching frequency, active damping coefficient, etc.) on mechanical vibration (especially gear meshing frequency band vibration and electromagnetic force order vibration), and provide theoretical basis and parameter optimization guidance for active vibration reduction control.

[0301] (5) It has clear engineering feasibility: The modeling process is based on clear physical components, nodes and load paths, and adopts mature finite element, multibody dynamics and numerical integration techniques. It is realized through Simulink and Simpack co-simulation, which is easy to deploy on commercial software platforms and can be directly used for control strategy design and NVH optimization in the product development stage.

[0302] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications made to the present invention by those skilled in the art without departing from the spirit of the present invention shall fall within the protection scope of the present invention.

Claims

1. A multi-source excitation real-time closed-loop coupling method for optimizing the control of an electric drive system, characterized in that, Includes the following steps: 1) Divide the mechatronics system of motor-gearbox into three independent collaborative subsystems: a rigid-flexible coupled mechanical system, an electromagnetic excitation subsystem considering mechanical vibration feedback, and a motor control strategy module. Establish simulation models for each collaborative subsystem and construct a three-domain bidirectional closed-loop coupled modeling framework of mechanical-electrical-control. 2) Define the interactive input and output of the simulation models of each collaborative subsystem in the three-domain bidirectional closed-loop coupling modeling framework of mechanical-electrical-control, form a multi-source excitation real-time closed-loop coupling system model, and define the multi-source excitation loading nodes of the multi-source excitation real-time closed-loop coupling system model, as well as the transmission relationship of each excitation load; 3) Set up a control parameter optimization module, and based on the simulation of a multi-source excitation real-time closed-loop coupled system model, construct a multi-objective optimization framework for vibration reduction and noise reduction, carry out adaptive optimization of control parameters, and complete parameter iterative verification and convergence solution through closed-loop simulation to obtain the optimal control strategy parameters that meet the NVH performance requirements of the electric drive system.

2. The multi-source excitation real-time closed loop coupling method of claim 1, wherein, In step 1), the simulation model established for the electromechanical transmission system is a rigid-flexible coupling dynamic model. The establishment of this rigid-flexible coupling dynamic model specifically includes: 1-1) For rigid-flexible coupled mechanical systems, a rigid-flexible coupled dynamic model incorporating structural flexibility is established, and integrated modeling of flexible body condensation, flexible helical gear meshing, and internal excitation is completed. This model is used to accurately characterize the structural flexibility and mechanical dynamic excitation characteristics of the system in subsequent simulations. 1-1-1) CAE simulation software was used to divide the electromechanical integrated shell, stator system and gear into finite element meshes. After calculating the free modes of the system, the dynamic characteristics of the flexible body were condensed to the key master nodes. 1-1-2) Modeling of meshing force of flexible helical gears: 1-1-2-1) For gear meshing pairs, the gear is first equivalently divided into multiple slices along the tooth width direction. Then, based on tooth surface contact analysis, the time-varying meshing stiffness of each slice during the meshing process is calculated, and the time-varying characteristics and distribution characteristics of the meshing stiffness across the entire tooth width are obtained, providing core mechanical parameters for the subsequent establishment of the meshing force model; 1-1-2-2) Based on the time-varying meshing stiffness of each slice obtained in step 1-1-2-1), establish a meshing force model between the central nodes of the tooth surfaces of the driving and driven gears, solve the dynamic meshing force between the central nodes of the tooth surfaces of the two gears, and finally distribute the obtained dynamic meshing force to the tooth surface contact line according to the meshing stiffness of each slice. 1-1-3) In the meshing force model, bearing force elements are used to simulate nonlinear bearing force, and spring damping force elements are used to simulate support force. The internal excitation is integrated into the meshing force model to construct the bearing force and flexible mechanical structure, forming a rigid-flexible coupled dynamic model that can completely characterize the internal dynamic characteristics of the flexible mechanical transmission system.

3. The multi-source excitation real-time closed-loop coupling method according to claim 1, characterized in that, In step 1), the simulation model established for the electromagnetic excitation subsystem is the electromagnetic excitation model. The establishment of this electromagnetic excitation model specifically includes: 1-2) For the electromagnetic excitation subsystem, establish an electromagnetic excitation model that considers mechanical vibration feedback: 1-2-1) Establish a real-time mapping relationship between the mechanical vibration of the rigid-flexible coupled mechanical system and the air gap magnetic field distortion characteristics of the electromagnetic excitation subsystem; 1-2-2) Based on the real-time mapping relationship obtained in step 1-2-1), extract the harmonic characteristics of the back EMF that are modulated in real time by mechanical vibration, and establish a real-time correlation model between the back EMF harmonics and the rotor vibration state. 1-2-3) Based on the real-time correlation model between the back EMF harmonics and the rotor vibration state obtained in step 1-2-2), reconstruct the voltage balance equation and electromagnetic torque equation of the motor. 1-2-4) By analyzing the air gap permeability and air gap magnetic flux density using the finite element method, multi-order electromagnetic force modeling is carried out, and several radial electromagnetic force models of different spatial and temporal orders are constructed. The electromagnetic excitation is applied to the stator and rotor condensation main nodes of the rigid-flexible coupling mechanical system in the form of concentrated torque and concentrated force to form an electromagnetic excitation model.

4. The multi-source excitation real-time closed-loop coupling method according to claim 1, characterized in that, In step 2), the interactive input and output of the simulation models of each collaborative subsystem in the three-domain bidirectional closed-loop coupling modeling framework of mechanical-electrical-control are defined, including: 2-1) Define the input and output interfaces of the rigid-flexible coupling dynamic model as follows: These interfaces are used to receive the electromagnetic excitation output from the electromagnetic excitation model and to transmit the mechanical vibration response in the form of vibration state parameters to the motor control strategy module and the electromagnetic excitation model in real time. 2-1-1) Define an electromagnetic excitation load input interface to receive the tangential electromagnetic torque and radial electromagnetic force corresponding to the predefined coupling node; 2-1-2) Define the system boundary load input interface, which is used to output the load force of the condensation node and the support force of the box constraint support node; 2-1-3) Define an internal state update input interface to update internal mechanical excitations such as gear meshing force and nonlinear bearing force in real time during the time-domain iteration process, so as to realize the closed-loop update of internal excitations; 2-1-4) Define the real-time vibration state parameter output interface of the motor rotor, which is used to output the real-time vibration state parameters of the motor rotor to the electromagnetic excitation model, thereby correcting the air gap magnetic field distortion characteristics in real time, reconstructing the back electromotive force harmonics and the core equation of the motor, so as to realize the reverse feedback of mechanical vibration to electromagnetic parameters and open up the electromechanical bidirectional coupling path. 2-1-5) Define the vibration characteristic parameter output interface for the key frequency band of the system, which is used to output the vibration characteristic parameters of the key frequency band of the system to the motor control strategy module. The control strategy adjusts the control command in real time to realize active vibration reduction closed-loop control based on vibration state. 2-1-6) Define the full dynamic response data output interface of all critical physical nodes of the system, which is used to output the full dynamic response data of all critical physical nodes of the system to the control parameter optimization module. While performing NVH characteristic simulation analysis of electric drive system, it serves as the calculation basis for vibration reduction and noise reduction optimization target, and supports the adaptive optimization and iterative verification of control parameters.

5. The multi-source excitation real-time closed-loop coupling method according to claim 1, characterized in that, In step 2), the interactive input and output of the simulation models of each collaborative subsystem in the three-domain bidirectional closed-loop coupling modeling framework of mechanical-electrical-control are defined, including: 2-2) Define the input and output interfaces of the electromagnetic excitation model as follows: These interfaces receive the real-time vibration state parameters from the rigid-flexible coupling mechanical system and the real-time control commands from the motor control strategy module. Simultaneously, the calculated electromagnetic excitation is transmitted to the rigid-flexible coupling mechanical system, and the real-time electromagnetic state is fed back to the motor control strategy module. 2-2-1) Define a real-time vibration state parameter input interface to receive the vibration state parameters output in real time during the time-domain iterative solution of the rigid-flexible coupled mechanical system. This allows for the quantitative analysis of the distortion effect of mechanical vibration on the air gap magnetic field of the motor, extraction of the harmonic characteristics of the back electromotive force modulated by vibration, reconstruction of the motor voltage balance equation and electromagnetic torque equation, and realization of real-time reverse feedback of mechanical vibration on electromagnetic characteristics from the physical level. 2-2-2) Define a real-time adjustment command input interface to receive real-time adjustment commands output by the motor control strategy module after real-time adjustment based on mechanical vibration feedback and electromagnetic state feedback. This allows for real-time updating of the boundary conditions of electromagnetic calculations and recalculation of electromagnetic torque and radial electromagnetic force, thereby enabling the control strategy to actively modulate electromagnetic excitation and providing an execution path for active vibration reduction in the electric drive system. 2-2-3) Define the electromagnetic excitation load output interface of the mechanical system, which is used to load the tangential electromagnetic torque calculated in real time and the radial electromagnetic force covering different spatial and temporal orders onto the predefined stator and rotor condensation main nodes of the rigid-flexible coupling dynamic model. In the simulation process, the core external dynamic excitation of the mechanical system is restored, driving the mechanical system to generate vibration response and opening up the positive coupling path from electromagnetic excitation to mechanical vibration response. 2-2-4) Define a real-time electromagnetic state feedback parameter output interface to output the real-time electromagnetic state feedback parameters to the motor control strategy module. Together with the vibration response parameters output by the rigid-flexible coupling dynamic model, it constitutes the dual feedback input of the control algorithm, providing the adjustment basis for vibration reduction control algorithms such as maximum torque-current ratio, harmonic injection, and active damping, and supporting the real-time closed-loop adjustment of the control strategy. 2-2-5) Define a full-time electromagnetic characteristic simulation data output interface to output full-time electromagnetic characteristic simulation data to the control parameter optimization module. Together with the vibration response data output by the rigid-flexible coupling dynamic model, it constitutes the calculation basis for the vibration reduction and noise reduction optimization target, supporting the sensitivity analysis, multi-objective optimization and iterative verification of control parameters.

6. The multi-source excitation real-time closed-loop coupling method according to claim 1, characterized in that, In step 2), the interactive input and output of the simulation models of each collaborative subsystem in the three-domain bidirectional closed-loop coupling modeling framework of mechanical-electrical-control are defined, including: 2-3) Define the input and output interfaces of the motor control strategy module as follows: These interfaces are used to receive the real-time electromagnetic state output by the electromagnetic excitation model and the real-time mechanical vibration response feedback from the rigid-flexible coupling dynamic model, and to output control commands to the electromagnetic excitation model in real time. The inputs to the motor control strategy module include: the instantaneous rotor speed, angular acceleration, and vibration amplitude in the key frequency band fed back from the rigid-flexible coupling mechanical system, as well as the real-time electromagnetic state fed back from the electromagnetic excitation subsystem. The output of the motor control strategy module includes the corrected d / q axis current command, the amplitude and phase of the injected harmonic current, and the updated control parameters.

7. The multi-source excitation real-time closed-loop coupling method according to claim 1, characterized in that, In step 2), steps 2-4) define the multi-source excitation loading nodes of the multi-source excitation real-time closed-loop coupled system model, as well as the transmission relationship of each excitation load, specifically including: 2-4-1) Apply tangential electromagnetic torque and radial electromagnetic force to the stator condensation node and rotor condensation node; apply gear meshing force to the gear tooth condensation node; 2-4-2) Apply bearing force to the bearing shrinkage joint and bearing bore shrinkage joint of the drive shaft; 2-4-3) Apply the load force to the output end shrinkage node; 2-4-4) Apply the support force to the box-shaped constraint support node; 2-4-5) Define the force and torque transmission relationship of the coupled nodes.

8. The multi-source excitation real-time closed-loop coupling method according to claim 1, characterized in that, In step 3), the Newmark-β time-domain successive integration method is used to perform real-time closed-loop iterative solution of the system. Within each time step, state updates, excitation updates, and vibration response solutions are completed. This is used to obtain the system's full-time-domain dynamic response in the simulation and to achieve closed-loop coupled calculation of the electromechanical-control three domains. Specifically, this includes: 3-1-1) State Update: Based on the mechanical state at the previous moment, the electromagnetic excitation is corrected in real time to update the mechanical influence on the electromagnetic field; at the same time, the mechanical vibration response is fed back to the motor control strategy module, and the control strategy adjusts the output control commands in real time according to the preset vibration reduction algorithm. 3-1-2) Excitation update: Recalculate the electromagnetic excitation based on the updated control command; at the same time, update the bearing force and meshing force based on the vibration of the bearing and tooth surface node, and update the external support force based on the vibration of the housing constraint node. 3-1-3) Response solution: Apply all updated excitations to the rigid-flexible coupled mechanical model and solve for the system vibration response at the current time step; 3-1-4) Iterative Loop: Use the system response at the current time step as the input for the next time step, repeat the above steps, iterate and solve the problem, and finally obtain the dynamic response of all key physical nodes of the system.

9. The multi-source excitation real-time closed-loop coupling method according to claim 1, characterized in that, Step 3) also includes building an engineering co-simulation platform based on multibody dynamics and control simulation software, completing the interface configuration and collaborative solution of mechanical, electromagnetic, and control models, which is used to achieve efficient engineering calculation and data extraction of the closed-loop coupling of the electromechanical, electronic, and control domains in subsequent simulations, as detailed below: 3-2-1) Import the flexible body models of the shell and gears generated by Abaqus into Simpack. At the same time, use discrete beam element models in Simpack to establish the flexible body dynamics models of each transmission shaft, define the parameters of the flexible meshing gears and the constraint relationships between each transmission component, and establish a rigid-flexible coupling multibody dynamics model of the motor-gearbox. 3-2-2) In Simulink, build a flux linkage model and a tangential electromagnetic torque model that include back electromotive force harmonics. At the same time, build a motor speed loop and current loop control system model that include vibration reduction control algorithm, as well as a radial electromagnetic force module based on Maxwell's stress tensor method. 3-2-3) Define a co-simulation interface in Simpack, with electromagnetic torque and radial electromagnetic force as inputs and motor rotor speed and vibration amplitude in key frequency bands as outputs; define a corresponding interactive interface in Simulink, with mechanical vibration signal as input and electromagnetic excitation as output; 3-2-4) Using Simpack's Co-simulation solver and Simulink's Simat command, real-time data interaction between the two software programs is achieved, the co-simulation is started, the simulation calculation of the three-domain closed-loop coupling is completed, and finally the dynamic response data of each node is exported.

10. The multi-source excitation real-time closed-loop coupling method according to claim 1, characterized in that, In step 3), the specific steps for optimizing the control parameters of the multi-source excitation closed-loop coupling include: 3-3-1) Define the vibration reduction and noise reduction optimization target, and determine the set of control parameters to be optimized. The set of control parameters includes the order, amplitude and phase of the harmonic injection current, the PI parameters of the speed loop and current loop, the active damping coefficient, the cutoff frequency of the resonant control, the center frequency and bandwidth of the notch filter, and the switching frequency and modulation strategy. 3-3-2) Using the above-mentioned co-simulation platform, time-domain simulation is performed on the current combination of control parameters to obtain the dynamic response of the system and calculate the optimization target value under the current parameters; 3-3-3) Establish the mapping relationship between control parameters and optimization objectives, perform sensitivity analysis and multi-parameter collaborative optimization, solve for the Pareto optimal solution set, and obtain the optimal combination of control parameters; 3-3-4) Update the optimal control parameters obtained by optimization to the motor control strategy module, perform joint simulation verification again, compare the vibration response characteristics before and after optimization to evaluate the optimization effect. If the expected results are not achieved, adjust the optimization parameter range or the optimization target weight for the next iteration until convergence, and finally obtain the optimal control strategy that meets the NVH requirements.

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