A PWM harmonic suppression method for a permanent magnet synchronous motor drive system for vehicles

Abstract

The present application relates to the technical field of motor harmonic suppression, in order to solve the known problem of motor harmonic, specifically a PWM harmonic suppression method for vehicle permanent magnet synchronous motor drive system, comprising the following steps: (1) collecting three-phase stator current signal data of vehicle permanent magnet synchronous motor drive system; (2) extracting the amplitude information of fundamental wave and each order harmonic component based on three-phase signal data in the current analysis window; (3) constructing multiple PWM modulation mode candidate sets in the system control logic, randomly generating a new candidate PWM control mode according to the transition probability rule; (4) for the generated candidate PWM control mode, constructing the performance difference index of the current state and the candidate state, judging whether to accept the candidate PWM control mode as the new current state; (5) performing multiple rounds of global search and strategy screening, finally making the system approach the optimal PWM control state; (6) the system generates PWM drive signal according to the optimal strategy to drive permanent magnet synchronous motor to run stably.

Description

Technical Field

[0001] The present invention relates to the technical field of motor harmonic suppression, and in particular to a PWM harmonic suppression method for a vehicle permanent magnet synchronous motor drive system. Background Art

[0002] Permanent magnet synchronous motors (PMSMs) are currently the mainstream choice for electric vehicle drive systems due to their high efficiency, high power density, and excellent speed regulation. The performance of PSM drive systems directly impacts an electric vehicle's acceleration, energy consumption, and overall vehicle stability and reliability.

[0003] In a drive system, the inverter is a key component for DC-AC energy conversion. Its pulse width modulation (PWM) strategy plays a decisive role in the output current quality and the motor's electromagnetic performance. A reasonable PWM strategy can effectively suppress high-order harmonics and reduce electromagnetic interference (EMI), thereby improving the system's electromagnetic compatibility (EMC) and overall operating efficiency.

[0004] However, with the widespread adoption of silicon carbide power devices in high-performance inverter systems, their fast switching speeds and high frequencies significantly increase the voltage spikes and dv / dt variation rates they generate, leading to more severe PWM-induced harmonic issues. While PWM modulation strategies achieve high-frequency control performance, they also introduce more frequency-domain noise components, significantly impacting the total harmonic distortion (THD) of the drive system, which in turn affects the system's EMI characteristics and motor control performance.

[0005] Traditional harmonic suppression methods usually use fixed PWM strategies, filter hardware or preset parameter optimization methods. These methods have defects such as weak adaptability, poor global performance, and high hardware complexity, making it difficult to adapt to the dynamic optimization needs under complex operating conditions.

[0006] Therefore, how to design an algorithm that can realize adaptive search and optimization in the PWM control strategy space and dynamically select the PWM control mode with better harmonic performance, so as to effectively suppress THD and EMI while ensuring current control accuracy, has become a key technical problem that needs to be urgently solved in the current high-performance permanent magnet synchronous motor drive system. Summary of the Invention

[0007] The present invention aims to address the vibration noise problem of built-in permanent magnet synchronous motors in the prior art and propose a method for suppressing PWM harmonics in a vehicle permanent magnet synchronous motor drive system, comprising the following steps:

[0008] (1) Collect the three-phase stator current signal data of the automotive permanent magnet synchronous motor drive system, including the three-phase current components A, B, and C and their corresponding time series data, input the current signal into the data processing module, set the sampling start time and window length required for Fourier analysis, and extract the current waveform data within the analysis window;

[0009] (2) Based on the three-phase current time series waveform data in the current analysis window, the fast Fourier transform algorithm is used to convert the current signal into the frequency domain, and the amplitude information of the fundamental wave and each order harmonic component is extracted from it; according to the spectrum results, the spectrum distribution and total harmonic distortion rate of the current under the current PWM modulation mode are calculated as the input of the optimization objective function;

[0010] (3) Construct multiple PWM modulation mode candidate sets in the system control logic. Each PWM control mode contains different carrier frequencies, modulation depths, sector switching rules, or pulse width jitter characteristic disturbance factors. The Markov chain Monte Carlo algorithm is introduced to randomly generate a new candidate PWM control mode based on the transition probability rule starting from the current PWM control mode state through the state space traversal strategy.

[0011] (4) For the generated candidate PWM control mode, under the premise of keeping other system parameters unchanged, simulate or extract its current response signal in actual operation, perform FFT frequency domain analysis and THD calculation again, and construct the performance difference index between the current state and the candidate state based on the optimization objective function with the goal of minimizing THD. Then, use the acceptance criterion in the Markov chain Monte Carlo algorithm and the acceptance probability function to determine whether to accept the candidate PWM control mode as the new current state;

[0012] (5) The accepted PWM control mode and its corresponding THD value are stored in the state history queue and enter the next round of Markov chain Monte Carlo sampling and evaluation iteration. During the entire Markov chain Monte Carlo optimization process, the PWM control mode perturbation, performance calculation and state transition decision are continuously executed, and multiple rounds of global search and strategy screening are performed, ultimately making the system approach the optimal PWM control state;

[0013] (6) When the set number of iterations is reached or the preset convergence conditions are met, the PWM modulation mode with the smallest THD value in all sampling states is counted and determined as the optimal PWM control strategy under the current working conditions. The system generates a PWM drive signal based on the optimal PWM control strategy and outputs it to the silicon carbide power module to drive the permanent magnet synchronous motor to operate stably.

[0014] Preferably, the method for extracting the current waveform data in the analysis window in step (1) is: according to the three-phase stationary coordinate transformation Clark transformation, the three-phase signal 、 、 Converted into two-phase quadrature components 、 , the transformation formula is:

[0015]

[0016] The transformed 、 The components are subjected to fast Fourier transform FFT to obtain the spectrum amplitude sequence 、 , used to analyze its harmonic distribution characteristics;

[0017] Extract the amplitude of the fundamental component in the spectrum And the amplitude of each higher harmonic component , calculate THD in PWM control mode, the calculation formula is:

[0018]

[0019] in is the fundamental frequency, N is the maximum harmonic analysis order, Represents the amplitude at the corresponding frequency; the THD value is used as the objective function indicator of the Markov Chain Monte Carlo algorithm to evaluate the harmonic suppression performance of the current PWM control mode under a given state.

[0020] Preferably, in step (3), random jumps and optimal solution searches are performed in the state space based on the Markov chain Monte Carlo algorithm for the constructed multiple PWM control mode candidate sets, and each state jump corresponds to a disturbance and switching of the PWM control mode, specifically:

[0021] Set the current control state to PWM control mode , randomly sample a new state from the candidate state set , and calculate the corresponding new THD value ;

[0022] The THD values ​​corresponding to the new and old states are recorded as and , the acceptance probability function of the constructed state transfer is:

[0023]

[0024] Where T is the temperature parameter of the Markov chain Monte Carlo algorithm, which is used to control the acceptance probability and search range; if the condition rand()< , then accept the new state As the current PWM control mode, otherwise maintain the current state unchanged; rand() represents a random number generator uniformly distributed in the interval [0, 1], which is used to simulate the probabilistic acceptance mechanism to make the state transition have a certain degree of randomness. The PWM control mode and the corresponding THD value in each round of Markov chain Monte Carlo iteration are recorded. State sampling and acceptance judgment are continuously performed to form a Markov chain random search process, realizing global optimization of harmonic characteristics under multiple PWM control modes.

[0025] Preferably, after each new PWM control mode is accepted, harmonic analysis is performed on the current signal under the corresponding mode, and the spectrum distribution of the current under the current control mode is extracted using FFT, and then the THD is calculated and used as the basis for Markov chain Monte Carlo optimization judgment, specifically:

[0026] Set the start time of the Fourier analysis window and cycle length , intercept the three-phase stator current signal under the current PWM control mode 、 、 In the interval Sample data on , recorded as vector form , , Perform fast Fourier transform on the normalized current signal to obtain the frequency domain amplitude distribution , and calculate the normalized amplitude spectrum , where the fundamental frequency is , whose amplitude is ; The total harmonic distortion calculation formula is:

[0027]

[0028] in, represents the amplitude of the nth harmonic component, is the cutoff order of harmonic analysis, is the fundamental amplitude; the THD value is compared with the THD value in the previous state and used as the input parameter of the state acceptance function to guide the acceptance of the next control state in the Markov chain Monte Carlo chain, and continuously complete the optimization process of the PWM control mode control strategy.

[0029] Preferably, the THD of the current signal generated by the candidate PWM control mode is calculated, the acceptance probability is constructed based on the optimization objective function, and the Markov chain Monte Carlo algorithm is combined to determine whether the PWM control mode is accepted as the current optimal state, specifically:

[0030] After each round of state transfer, the Markov chain Monte Carlo algorithm constructs an objective function g to quantify the control performance in the current PWM control mode. The objective function takes the minimum THD as the goal and constructs the following cost function form:

[0031]

[0032] in, The first The amplitude of the harmonic component of order, is the amplitude of the fundamental component, is the harmonic cutoff order;

[0033] The objective function value of the current candidate state The objective function value of the last accepted state Compare and decide whether to accept the PWM control mode based on the following probability acceptance criteria:

[0034]

[0035] in, In order to control the temperature factor of acceptance sensitivity, the jump acceptance strategy is controlled according to the Metropolis-Hastings criterion.

[0036] Preferably, after the Markov chain Monte Carlo algorithm has passed a preset number of iterations or reached a set convergence criterion, the objective function value is screened from the set of accepted PWM control modes in the entire sampling history. The minimum PWM control mode is recorded as the optimal PWM control strategy And according to the optimal PWM control strategy, a PWM pulse signal for driving is generated, wherein the PWM pulse signal is based on the carrier frequency of the selected PWM control mode. , modulation depth Duty cycle And reference three-phase voltage 、 、 , construction, the specific control signal generation formula is as follows:

[0037]

[0038] in is the reference phase voltage signal, This is the triangular carrier signal generated according to the selected PWM control mode.

[0039] Preferably, after each round of state transfer, the new PWM control mode and its THD value are recorded in the state queue and trend analysis is performed. If the THD change rate is less than the set threshold for multiple consecutive iterations, it is determined that it has fallen into a local optimum and the current state is reset to the historical optimal PWM control mode state.

[0040] Preferably, the PWM candidate mode further includes a carrier shape disturbance factor, including but not limited to a sawtooth wave, a triangle wave, and an asymmetric waveform.

[0041] Preferably, before finally outputting the optimal PWM control mode, a retrospective statistical analysis is performed on the THD values ​​corresponding to all accepted PWM control modes, and a multi-objective cross-validation is performed in combination with electromagnetic interference, voltage ripple, and current peak system constraints, and the final control mode is output only when the comprehensive constraints are met.

[0042] This invention has the following beneficial effects: It integrates a Markov chain Monte Carlo probabilistic optimization strategy with a frequency-domain performance evaluation mechanism to achieve dynamic self-adaptation and global optimal selection for PWM modulation control. Compared with traditional fixed PWM control modes or local search algorithms, this method overcomes the vulnerability of the optimization process to local optimal solutions, improves harmonic suppression and algorithm robustness, and ultimately provides a more efficient, low-harmonic, and scalable drive control solution for automotive permanent magnet synchronous motors. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For those skilled in the art, other relevant drawings can be obtained based on these drawings without creative work.

[0044] Figure 1 It is a flow chart of a method for suppressing PWM harmonics of a permanent magnet synchronous motor for a vehicle based on a Markov chain Monte Carlo algorithm provided in the first aspect of the present invention;

[0045] Figure 2 is a flow chart of the PWM control mode sampling and optimization iterative process based on the minimum THD target provided by the second aspect of the present invention;

[0046] Figure 3 is the evolution trajectory of THD corresponding to the PWM control mode in the optimization iteration provided by the embodiment of the present invention;

[0047] Figure 4THD performance change of PWM control mode in a sampling iteration process in Markov chain Monte Carlo optimization provided by the embodiment of the application;

[0048] Figure 5 FFT analysis graph under the PWM control before optimization provided by the embodiment of the application;

[0049] Figure 6 FFT analysis graph under the PWM control after optimization provided by the embodiment of the application. DETAILED DESCRIPTION

[0050] In order to make the objectives, technical solutions and advantages of the embodiments of the application clearer, the technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only some of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by a person of ordinary skill in the art without creative work are within the protection scope of the application. Therefore, the following detailed description of the embodiments of the application provided in the drawings is not intended to limit the scope of the claimed application, but only represents selected embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by a person of ordinary skill in the art without creative work are within the protection scope of the application.

[0051] EMBODIMENT

[0052] The following is only a preferred embodiment of the application, and the protection scope of the application is not limited to the following embodiments. Any technical solution falling within the concept of the application is within the protection scope of the application.

[0053] REFERENCE Figure 1 、 Figure 2 As shown in the figure, the first embodiment of the application discloses a PWM harmonic suppression method for a vehicle permanent magnet synchronous motor driving system based on a Markov chain Monte Carlo algorithm, which can be executed by a vehicle Markov chain Monte Carlo optimization control module, in particular, by one or more processors in the Markov chain Monte Carlo optimization control module to implement the following method:

[0054] S1, collecting three-phase current signal data of a vehicle permanent magnet synchronous motor under different operating conditions, including A, B and C three-phase current components and corresponding time stamps, and setting an analysis window start time and length, and extracting current analysis data sequences;

[0055] Specifically, step S1 includes: preferably, collecting three-phase stator currents of a to-be-controlled electric vehicle 、 、 Signal, the sampling module is used to collect the current data under different operating conditions in real time and store the corresponding timestamp information. In order to facilitate subsequent modeling and analysis, the current data sequence is intercepted by sliding time window. Preferably, the start time and window length of each window are set at the same time. , extract the data segments for analysis.

[0056] In the current data preprocessing stage, in order to realize the transformation from the three-phase stationary coordinate system to the two-phase rotating coordinate system, the three-phase current is first subjected to Clarke transformation:

[0057]

[0058] The transformed 、 The components are subjected to fast Fourier transform FFT to obtain the spectrum amplitude sequence 、 , used to analyze its harmonic distribution characteristics;

[0059] Extract the amplitude of the fundamental component in the spectrum And the amplitude of each higher harmonic component , calculate THD in PWM control mode, the calculation formula is:

[0060]

[0061] in is the fundamental frequency, N is the maximum harmonic analysis order, Represents the amplitude at the corresponding frequency; preferably, the THD value is used as the objective function indicator of the Markov chain Monte Carlo algorithm to evaluate the harmonic suppression performance of the current PWM control mode under a given state.

[0062] S2, based on the three-phase current time series waveform data in the current analysis window, uses the fast Fourier transform algorithm to perform frequency domain conversion on the current signal, and extracts the amplitude information of the fundamental wave and each order harmonic component; based on the spectrum results, calculates the spectrum distribution and total harmonic distortion rate of the current under the current PWM modulation mode, and preferably uses them as the input of the optimization objective function;

[0063] Specifically, step S2 includes: according to the formula

[0064]

[0065] Calculating modulation depth ,in, is the voltage command amplitude output by the current closed-loop controller, is the DC bus voltage. This parameter characterizes the degree of carrier utilization during SVPWM modulation and is closely related to the strength of the harmonic components in the current.

[0066] Furthermore, according to the harmonic amplitude extracted by FFT Calculate the harmonic ratios of each typical order, such as:

[0067] ,

[0068] in is the fundamental amplitude, 、 The fifth and seventh harmonic amplitudes are used to analyze the suppression effect of the modulation method on specific harmonics. According to the spectrum distribution, the effective bandwidth is extracted. Defined as an amplitude greater than the maximum amplitude The frequency range width at -20dB times is expressed as:

[0069] ,in

[0070] The above frequency domain indicators can together constitute the spectrum feature vector for evaluating the current PWM modulation performance:

[0071] This vector will be used as the input of the objective function of the subsequent optimization model.

[0072] S3, preferably, constructing multiple candidate PWM control modes and their corresponding random disturbance characteristics, as well as objective function definitions and state transition probability settings, and randomly selecting candidate modes in the control mode state space based on a Markov chain Monte Carlo algorithm;

[0073] Specifically, step S3 includes: constructing multiple candidate PWM control mode sets , where each mode Corresponding to a modulation strategy (specifically including the following types of change factors: 1. Carrier frequency change 2. Modulation depth adjustment 3. Sector switching strategy), and designing its own unique random perturbation characteristic function for each mode , preferably, the time evolution form used to describe the random modulation characteristics under this mode is:

[0074]

[0075] in is the disturbance amplitude, is the disturbance frequency, is the initial phase, is a white noise term that satisfies Gaussian distribution. According to the frequency domain feature vector extracted in the above step S2 , for each candidate control mode Constructing the optimization objective function It is defined as follows:

[0076]

[0077] in, is the weight coefficient, which is weighted according to the system design indicators. The smaller the objective function value is, the better the current waveform quality of the control mode under the current working conditions is.

[0078] S4, calculate the THD of the current signal generated by the candidate PWM control mode, refer to the appendix of the manual Figure 3-4 , based on the optimization objective function, the acceptance probability is constructed, and the Markov chain Monte Carlo strategy is combined to determine whether the PWM control mode is accepted as the current optimal state;

[0079] Specifically, step S4 includes: in order to realize the intelligent selection of the control mode in the state space, using the Markov chain Monte Carlo algorithm to perform random jumps in the state space. Assume that the current control state PWM control mode , randomly sample a new state from the candidate state set , and calculate the corresponding new THD value ;

[0080] The THD values ​​corresponding to the new and old states are recorded as and , the acceptance probability function of the constructed state transfer is:

[0081]

[0082] Where T is the temperature parameter of the Markov chain Monte Carlo algorithm, which is used to control the acceptance probability and search range; if the condition rand()< , then accept the new state As the current PWM control mode, otherwise maintain the current state unchanged; rand() represents a random number generator uniformly distributed in the interval [0, 1]. This function simulates a probabilistic acceptance mechanism, imbuing state transitions with a degree of randomness, helping to escape local optimality. The PWM control mode and corresponding THD value are recorded in each Markov chain Monte Carlo iteration. State sampling and acceptance judgment are continuously performed, forming a Markov chain random search process, achieving global optimization of harmonic characteristics under multiple PWM control modes.

[0083] S5, after updating the PWM control mode, records the current mode and THD value, and enters the next round of Markov chain Monte Carlo sampling and evaluation iteration, continuing multiple rounds of global optimization process to approach the global optimal PWM control strategy;

[0084] Specifically, step S5 includes:

[0085] Set the start time of the Fourier analysis window and cycle length , intercept the three-phase stator current signal under the current PWM control mode 、 、 In the interval Sample data on , recorded as vector form , , Perform fast Fourier transform on the normalized current signal to obtain the frequency domain amplitude distribution , and calculate the normalized amplitude spectrum , where the fundamental frequency is , whose amplitude is ; The total harmonic distortion calculation formula is:

[0086]

[0087] in, represents the amplitude of the nth harmonic component, is the cutoff order of harmonic analysis, is the fundamental amplitude; the THD value is compared with the THD value in the previous state and used as the input parameter of the state acceptance function to guide the acceptance of the next control state in the Markov chain Monte Carlo chain, and continuously complete the optimization process of the PWM control mode control strategy.

[0088] S6 outputs the PWM control mode with the optimal THD and generates a corresponding PWM pulse signal to output a control signal in a table-driven manner, which is used to drive the silicon carbide inverter to control the operation of the permanent magnet synchronous motor.

[0089] Specifically, step S6 includes:

[0090] After the Markov chain Monte Carlo algorithm has completed a preset number of iterations or reached the set convergence criterion, the objective function value is selected from the set of accepted PWM control modes in the entire sampling history. The minimum PWM control mode is recorded as the optimal PWM control strategy And generate the PWM pulse signal for driving finally according to the optimal PWM control strategy. Preferably, the PWM pulse signal is based on the carrier frequency of the selected PWM control mode. , modulation depth Duty cycle And reference three-phase voltage 、 、 , construction, the specific control signal generation formula is as follows:

[0091]

[0092] in is the reference phase voltage signal, This is the triangular carrier signal generated according to the selected PWM control mode.

[0093] For details, please refer to the attached manual. Figure 5-6 In this embodiment, the cost function under each switching state in the sampling period is finally calculated as above, and the data corresponding to the minimum value of the cost function is replaced as the output value to complete the EMI suppression control.

[0094] In summary, the PWM control mode optimization method described above, based on the Markov Chain Monte Carlo algorithm, constructs multiple candidate PWM control strategies with different disturbance characteristics. Dynamically evaluating these strategies based on the current spectrum characteristics and the modulation objective function value, it selects a PWM modulation mode in the state space that balances electromagnetic compatibility and control performance, enabling adaptive control of electric vehicle drive systems under different operating conditions. This method effectively reduces output current total harmonic distortion (THD), suppresses the spread of high-frequency harmonics, and improves system stability and reliability. It is particularly suitable for high-performance, low-interference power electronics control scenarios.

[0095] The above embodiments are intended only to illustrate the technical concepts and features of the present invention. Their purpose is to enable those skilled in the art to understand the contents of the present invention and implement them accordingly. They are not intended to limit the scope of protection of the present invention. Any equivalent changes or modifications made in accordance with the spirit of the present invention are intended to be covered by the scope of protection of the present invention.

Claims

1. A method for suppressing PWM harmonics in a vehicle permanent magnet synchronous motor drive system, characterized in that: The steps include: (1) Collect the three-phase stator current signal data of the automotive permanent magnet synchronous motor drive system, including the three-phase current components A, B, and C and their corresponding time series data, input the current signal into the data processing module, set the sampling start time and window length required for Fourier analysis, and extract the current waveform data within the analysis window; (2) Based on the three-phase current time series waveform data in the current analysis window, the fast Fourier transform algorithm is used to convert the current signal into the frequency domain, and the amplitude information of the fundamental wave and each order harmonic component is extracted from it; according to the spectrum results, the spectrum distribution and total harmonic distortion rate of the current under the current PWM control mode are calculated as the input of the optimization objective function; (3) Construct multiple PWM control mode candidate sets in the system control logic, each PWM control mode contains different carrier frequencies, modulation depths, sector switching rules, or pulse width jitter characteristic disturbance factors; introduce the Markov chain Monte Carlo algorithm, and through the state space traversal strategy, randomly generate a new candidate PWM control mode based on the transition probability rule starting from the current PWM control mode state; (4) For the generated candidate PWM control mode, under the premise of keeping other system parameters unchanged, simulate or extract its current response signal in actual operation, perform FFT frequency domain analysis and THD calculation again, and construct the performance difference index between the current state and the candidate state based on the optimization objective function with the goal of minimizing THD. Then, use the acceptance criterion in the Markov chain Monte Carlo algorithm and the acceptance probability function to determine whether to accept the candidate PWM control mode as the new current state; (5) The accepted PWM control mode and its corresponding THD value are stored in the state history queue and enter the next round of Markov chain Monte Carlo sampling and evaluation iteration. During the entire Markov chain Monte Carlo optimization process, the PWM control mode perturbation, performance calculation and state transition decision are continuously executed, and multiple rounds of global search and strategy screening are performed, ultimately making the system approach the optimal PWM control state; (6) When the set number of iterations is reached or the preset convergence conditions are met, the PWM control mode with the smallest THD value in all sampling states is counted and determined as the optimal PWM control strategy under the current working conditions. The system generates a PWM drive signal based on the optimal PWM control strategy and outputs it to the silicon carbide power module to drive the permanent magnet synchronous motor to operate stably.

2. The method for suppressing PWM harmonics in a vehicle permanent magnet synchronous motor drive system according to claim 1, characterized in that: The method for extracting the current waveform data in the analysis window in step (1) is: according to the three-phase stationary coordinate transformation Clark transformation, the three-phase signal 、 、 Converted into two-phase quadrature components 、 , the transformation formula is: The transformed 、 The components are subjected to fast Fourier transform FFT to obtain the spectrum amplitude sequence 、 , used to analyze its harmonic distribution characteristics; Extract the amplitude of the fundamental component in the spectrum And the amplitude of each higher harmonic component , calculate THD in PWM control mode, the calculation formula is: in is the fundamental frequency, N is the maximum harmonic analysis order, Represents the amplitude at the corresponding frequency; the THD value is used as the objective function indicator of the Markov Chain Monte Carlo algorithm to evaluate the harmonic suppression performance of the current PWM control mode under a given state.

3. The method for suppressing PWM harmonics in a vehicle permanent magnet synchronous motor drive system according to claim 1, characterized in that: Step (3) For the constructed multiple PWM control mode candidate sets, random jumps and optimal solution searches are performed in the state space based on the Markov chain Monte Carlo algorithm. Each state jump corresponds to a disturbance and switch of the PWM control mode, specifically: Assume the current control state PWM control mode is , randomly sample a new state from the candidate state set , and calculate the corresponding new THD value ; The THD values ​​corresponding to the new and old states are recorded as and , the acceptance probability function of the constructed state transfer is: Where T is the temperature parameter of the Markov chain Monte Carlo algorithm, which is used to control the acceptance probability and search range; if the condition rand()< , then accept the new state As the current PWM control mode, otherwise maintain the current state unchanged; rand() represents a random number generator uniformly distributed in the interval [0, 1], which is used to simulate the probabilistic acceptance mechanism to make the state transition have a certain degree of randomness. The PWM control mode and the corresponding THD value in each round of Markov chain Monte Carlo iteration are recorded. State sampling and acceptance judgment are continuously performed to form a Markov chain random search process, realizing global optimization of harmonic characteristics under multiple PWM control modes.

4. The method for suppressing PWM harmonics in a vehicle permanent magnet synchronous motor drive system according to claim 1, characterized in that: After each new PWM control mode is accepted, the current signal under the corresponding mode is subjected to harmonic analysis. The spectrum distribution of the current under the current control mode is extracted using FFT, and then the THD is calculated and used as the basis for Markov chain Monte Carlo optimization judgment. Specifically: Set the start time of the Fourier analysis window and cycle length , intercept the three-phase stator current signal under the current PWM control mode 、 、 In the interval Sample data on , recorded as vector form , , Perform fast Fourier transform on the normalized current signal to obtain the frequency domain amplitude distribution , and calculate the normalized amplitude spectrum , where the fundamental frequency is , whose fundamental amplitude is ; The total harmonic distortion calculation formula is: in, The first The amplitude of the harmonic component of order, is the cutoff order of harmonic analysis, is the fundamental amplitude; the THD value is compared with the THD value in the previous state and used as the input parameter of the state acceptance function to guide the acceptance of the next control state in the Markov chain Monte Carlo chain, and continuously complete the optimization process of the PWM control mode control strategy.

5. The method for suppressing PWM harmonics in a vehicle permanent magnet synchronous motor drive system according to claim 4, characterized in that: The THD of the current signal generated by the candidate PWM control mode is calculated, and the acceptance probability is constructed based on the optimization objective function. The Markov chain Monte Carlo algorithm is then used to determine whether the PWM control mode is accepted as the current optimal state. Specifically, After each round of state transfer, the Markov chain Monte Carlo algorithm constructs an objective function g to quantify the control performance in the current PWM control mode. The objective function takes the minimum THD as the goal and constructs the following cost function form: in, The first The amplitude of the harmonic component of order, is the amplitude of the fundamental component, is the cutoff order of harmonic analysis; The objective function value of the current candidate state The objective function value of the last accepted state Compare and decide whether to accept the PWM control mode based on the following probability acceptance criteria: in, In order to control the temperature factor of acceptance sensitivity, the jump acceptance strategy is controlled according to the Metropolis-Hastings criterion.

6. The method for suppressing PWM harmonics in a vehicle permanent magnet synchronous motor drive system according to claim 5, characterized in that: After the Markov chain Monte Carlo algorithm has completed a preset number of iterations or reached the set convergence criterion, the objective function value is selected from the set of accepted PWM control modes in the entire sampling history. The minimum PWM control mode is recorded as the optimal PWM control strategy And according to the optimal PWM control strategy, a PWM pulse signal for driving is generated, wherein the PWM pulse signal is based on the carrier frequency of the selected PWM control mode. , modulation depth Duty cycle And reference three-phase voltage 、 、 , construction, the specific control signal generation formula is as follows: in is the reference phase voltage signal, This is the triangular carrier signal generated according to the selected PWM control mode.

7. The method for suppressing PWM harmonics in a vehicle permanent magnet synchronous motor drive system according to claim 5, characterized in that: After each round of state transfer, the new PWM control mode and its THD value are recorded in the state queue and trend analysis is performed. If the THD change rate is less than the set threshold for multiple consecutive iterations, it is determined to be trapped in the local optimum and the current state is reset to the historical optimal PWM control mode state.

8. The method for suppressing PWM harmonics in a vehicle permanent magnet synchronous motor drive system according to claim 7, characterized in that: The PWM candidate pattern further includes a carrier shape perturbation factor, and the carrier shape perturbation factor is a sawtooth wave, a triangle wave, or an asymmetric waveform.

9. The method for suppressing PWM harmonics in a vehicle permanent magnet synchronous motor drive system according to claim 8, characterized in that: Before finally outputting the optimal PWM control mode, a retrospective statistical analysis is performed on the THD values ​​corresponding to all accepted PWM control modes, and a multi-objective cross-validation is performed in combination with the electromagnetic interference, voltage ripple, and current peak system constraints. The final control mode is output only when the comprehensive constraints are met.

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