PWM harmonic suppression method for vehicle permanent magnet synchronous motor driving system

Abstract

The invention relates to the technical field of motor harmonic suppression, in order to solve the known problem of motor harmonic, in particular to a PWM harmonic suppression method for a vehicle permanent magnet synchronous motor driving system, which comprises the following steps of: (1) acquiring three-phase stator current signal data of the vehicle permanent magnet synchronous motor driving system; (2) amplitude information of fundamental waves and harmonic components of all orders is extracted based on the three-phase signal data in the current analysis window; (3) constructing a plurality of PWM modulation mode candidate sets in a system control logic, and randomly generating a new candidate PWM mode according to a transition probability rule; (4) constructing a performance difference index between the current state and the candidate state for the generated candidate PWM control mode, and judging whether the candidate PWM mode is accepted as a new current state or not; (5) performing multiple rounds of global search and strategy screening, and finally enabling the system to approach the optimal PWM control state; and (6) the system generates a PWM driving signal according to the optimal strategy to drive the permanent magnet synchronous motor to operate stably.

Description

Technical Field

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

[0002] In the prior art, permanent magnet synchronous motors have become the mainstream choice for electric vehicle drive systems due to their high efficiency, high power density, and excellent speed regulation performance. The performance of the permanent magnet synchronous motor drive system directly affects the acceleration performance, energy consumption, and the stability and reliability of the overall vehicle operation of electric vehicles.

[0003] In the drive system, as a key component for realizing DC-AC energy conversion, the inverter's Pulse Width Modulation (PWM) strategy plays a decisive role in the output current quality and the electromagnetic performance of the motor. A reasonable PWM strategy can effectively suppress high-order harmonics and reduce Electromagnetic Interference (EMI), thereby improving the Electromagnetic Compatibility (EMC) and overall operation efficiency of the system.

[0004] However, in the context of the widespread application of silicon carbide power devices in high-performance inverter systems, due to their fast switching speed and high frequency, the generated voltage spikes and dv / dt change rates increase significantly, resulting in more serious harmonic problems caused by PWM. While the PWM modulation strategy realizes high-frequency control performance, it also brings more frequency-domain noise components, which have a significant impact on the Total Harmonic Distortion (THD) of the drive system, and further affect the EMI characteristics and motor control performance of the system.

[0005] Traditional harmonic suppression methods usually adopt 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, and are difficult to meet the dynamic optimization requirements under complex operating conditions.

[0006] Therefore, how to design an algorithm that can achieve adaptive search and optimization in the PWM control strategy space, dynamically select a PWM 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 urgently needs to be solved in the current high-performance permanent magnet synchronous motor drive system. Summary of the Invention

[0007] The object of the present invention is to address the problem of vibration noise of interior permanent magnet synchronous motors in the prior art, and propose a PWM harmonic suppression method for a vehicle permanent magnet synchronous motor drive system, including the following steps: (1) Collect the three-phase stator current signal data of the vehicle-mounted permanent magnet synchronous motor drive system, including the current components of three phases 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 within the current analysis window, use the fast Fourier transform algorithm to perform frequency domain conversion on the current signal, and extract the amplitude information of the fundamental wave and each harmonic component therefrom; according to the spectrum result, calculate the spectrum distribution and total harmonic distortion rate of the current under the current PWM modulation mode as the input of the optimization objective function; (3) Construct multiple candidate sets of PWM modulation modes in the system control logic. Each PWM mode includes different carrier frequencies, modulation depths, sector switching rules, or pulse width jitter characteristic perturbation factors; introduce the Markov chain Monte Carlo algorithm, and through the state space traversal strategy, starting from the current PWM mode state, randomly generate a new candidate PWM mode according to the transition probability rule; (4) For the generated candidate PWM control mode, on the premise of keeping other system parameters unchanged, simulate or extract its current response signal during actual operation, perform FFT frequency domain analysis and THD calculation again, based on the optimization objective function, with the goal of minimizing THD, construct the performance difference index between the current state and the candidate state, and judge whether to accept the candidate PWM mode as the new current state through the acceptance criterion in the Markov chain Monte Carlo algorithm, combined with the acceptance probability function; (5) Store the accepted PWM mode and its corresponding THD value into the state history queue, enter the next round of Markov chain Monte Carlo sampling and evaluation iteration. During the entire Markov chain Monte Carlo optimization process, continuously perform PWM mode perturbation, performance calculation, and state transition decision-making, conduct multiple rounds of global search and strategy screening, and finally make the system approach the optimal PWM control state; (6) When the set number of iterations is reached or the preset convergence condition is satisfied, count the PWM modulation mode with the smallest THD value among all sampling states, determine it as the optimal PWM control strategy under the current working condition, and the system generates a PWM drive signal according to this optimal PWM control strategy and outputs it to the silicon carbide power module to drive the permanent magnet synchronous motor to operate stably.

[0008] Preferably, the method for extracting the current waveform data within the analysis window in step (1) is: according to the Clark transformation of three-phase stationary coordinate transformation, transform the three-phase signals , , into two-phase orthogonal components , , and the transformation formula is:

[0009] The transformed and components are respectively subjected to fast Fourier transform (FFT) to obtain spectral amplitude sequences and , which are used to analyze their harmonic distribution characteristics; Extract the fundamental component amplitude and the amplitudes of each higher harmonic component in the spectrum, and calculate the THD under the PWM mode. The calculation formula is:

[0010] where 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 index of the Markov chain Monte Carlo algorithm to evaluate the harmonic suppression performance of the current PWM control mode under a given state.

[0011] Preferably, in step (3), for the constructed multiple candidate sets of PWM control modes, based on the Markov chain Monte Carlo algorithm, random jumps and optimal solution searches are performed in the state space. Each state jump corresponds to a perturbation and switching of the PWM mode. Specifically: Assume the current control state is the PWM mode , and a new state is randomly sampled from the candidate state set, and the corresponding new THD value is calculated; Denote the THD values corresponding to the old and new states as and respectively, and construct the acceptance probability function for state transition as:

[0012] where T is the temperature parameter of the Markov chain Monte Carlo algorithm, which is used to regulate the acceptance probability and search range; if the condition rand() < is satisfied, then accept the new state as the current PWM mode, otherwise maintain the current state unchanged; where rand() represents a random number generation function uniformly distributed in the interval [0, 1], which is used to simulate the probability acceptance mechanism to make the state transition have a certain randomness. Record the PWM mode and the corresponding THD value in each round of Markov chain Monte Carlo iteration, continuously perform state sampling and acceptance judgment, and constitute a Markov chain random search process to achieve global optimization of the harmonic characteristics under multiple PWM modes.

[0013] Preferably, after each new PWM control mode is received, harmonic analysis is performed on the current signal corresponding to the mode, the spectral 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 the Markov chain Monte Carlo optimization judgment. Specifically: Set the start time of the Fourier analysis window and the period length , and intercept the three-phase stator current signals under the current PWM mode 、 、 in the interval The sample data on is recorded in vector form , , Perform a 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 , and its amplitude is ; The total harmonic distortion rate calculation formula is:

[0014] Among them, represents the amplitude of the nth harmonic component, is the cut-off order of the harmonic analysis, is the fundamental amplitude; Compare the THD value with the THD value in the previous state and use it as the input parameter of the state acceptance function to guide the acceptance or rejection of the next control state in the Markov chain Monte Carlo chain, and continuously complete the optimization process of the PWM mode control strategy.

[0015] Preferably, calculate the THD of the current signal generated by the candidate PWM mode, construct an acceptance probability based on the optimization objective function, and combine the Markov chain Monte Carlo algorithm to determine whether to accept the PWM mode as the current optimal state. Specifically: After each state transition of the Markov chain Monte Carlo algorithm, a target function g is constructed to quantify the control performance under the current PWM mode. The target function aims to minimize the THD and constructs the following cost function form:

[0016] Among them, is the amplitude of the th harmonic component in the spectrum under the current PWM control state, is the fundamental component amplitude, is the harmonic cut-off order; The target function value of the current candidate state The objective function value of the previous accepted state Compare, and decide whether to accept the PWM control mode according to the following probability acceptance criterion:

[0017] where is the temperature factor for controlling the acceptance sensitivity, and controls the jump acceptance strategy according to the Metropolis-Hastings criterion.

[0018] Preferably, after the Markov chain Monte Carlo algorithm has gone through a preset number of iterations or reached a set convergence criterion, screen the objective function value from the set of accepted PWM control modes in all sampling histories The PWM control mode with the smallest value is denoted as the optimal PWM control strategy And generate the final PWM pulse signal for driving according to the optimal PWM control strategy, and the PWM pulse signal is based on the carrier frequency of the selected PWM mode modulation depth duty cycle and the reference three-phase voltage 、 、 , construct, and the specific control signal generation formula is as follows:

[0019] where is the reference phase voltage signal, is the triangular carrier signal generated according to the selected PWM mode.

[0020] Preferably, after each state transition, record the new PWM mode and its THD value into the state queue, and perform trend analysis. If the THD change rate is less than the set threshold for multiple consecutive iterations, it is determined that the local optimum is reached, and the current state is reset to the historical optimal PWM mode state.

[0021] Preferably, the PWM candidate mode further includes a carrier shape perturbation factor, including but not limited to sawtooth wave, triangular wave, and asymmetric waveform.

[0022] Preferably, before finally outputting the optimal PWM control mode, perform retrospective statistical analysis on the THD values corresponding to all accepted PWM modes, and perform multi-objective cross-validation in combination with system constraints such as electromagnetic interference, voltage ripple, and current peak. Only output the final control mode when the comprehensive constraint conditions are met.

[0023] The present invention has the following beneficial effects: The present invention integrates the Markov chain Monte Carlo probability optimization strategy and the frequency-domain performance evaluation mechanism, achieving the dynamic adaptability and global optimal selection of PWM modulation control. Compared with the traditional fixed PWM mode or local search algorithm, this method can overcome the defect of being easily trapped in local optimal solutions during the optimization process, improve the harmonic suppression effect and algorithm robustness, and finally provide a more efficient, low-harmonic, and scalable drive control solution for automotive permanent magnet synchronous motors. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0025] Figure 1 is a schematic flowchart of a PWM harmonic suppression method for an automotive permanent magnet synchronous motor based on the Markov chain Monte Carlo algorithm provided in the first aspect of the present invention; Figure 2 is a flowchart of the PWM mode sampling and optimization iteration process based on the minimum THD target provided in the second aspect of the present invention; Figure 3 is the evolution trajectory of the THD corresponding to the PWM mode during the optimization iteration provided in the embodiment of the present invention; Figure 4 is the THD performance change of the PWM mode during the sampling iteration process in the Markov chain Monte Carlo optimization provided in the embodiment of the present invention; Figure 5 is the FFT analysis diagram under the PWM control before optimization provided in the embodiment of the present invention; Figure 6 is the FFT analysis diagram under the PWM control after optimization provided in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the protection scope of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the protection scope of the present invention.

[0027] Embodiment The following are only the preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the following embodiments. All technical solutions falling within the idea of the present invention belong to the protection scope of the present invention.

[0028] Reference Figure 1 、 Figure 2 As shown, the first embodiment of the present invention discloses a PWM harmonic suppression method for a vehicle permanent magnet synchronous motor drive system based on the Markov chain Monte Carlo algorithm, which can be executed by a vehicle Markov chain Monte Carlo optimization control module. Specifically, it is executed by one or more processors in the Markov chain Monte Carlo optimization control module to implement the following method: S1. Collect three-phase current signal data of the vehicle permanent magnet synchronous motor under different operating conditions, including the current components of phases A, B, and C and their corresponding timestamps, and set the start time and length of the analysis window to extract the current analysis data sequence. Specifically, step S1 includes: Preferably, collect the three-phase stator currents of the electric vehicle to be controlled 、 、 signals, use the sampling module to collect the current data in real time under different operating conditions, and store the corresponding timestamp information. For subsequent modeling and analysis, the sliding time window method is used to intercept the current data sequence. Preferably, the start time of each window and the window length are set at the same time to extract the data segment for analysis.

[0029] In the current data preprocessing stage, to achieve the transformation from the three-phase stationary coordinate system to the two-phase rotating coordinate system, first perform the Clarke transformation on the three-phase current:

[0030] The , Perform fast Fourier transform (FFT) on each component to obtain the spectral amplitude sequence , , which is used to analyze its harmonic distribution characteristics; Extract the fundamental component amplitude in the spectrum and the amplitudes of each high-order harmonic component . Calculate the THD under the PWM mode. The calculation formula is:

[0031] where 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 index of the Markov chain Monte Carlo algorithm to evaluate the harmonic suppression performance of the current PWM control mode under a given state.

[0032] S2. Based on the three-phase current time-series waveform data within the current analysis window, use the fast Fourier transform algorithm to perform frequency-domain conversion on the current signal, and extract the amplitude information of the fundamental wave and each order harmonic component from it; according to the spectral results, calculate the spectral distribution and total harmonic distortion rate of the current under the current PWM modulation mode. Preferably, it is used as the input of the optimization objective function; Specifically, step S2 includes: according to the formula

[0033] Calculate the modulation depth , where is the amplitude of the voltage command output by the current closed-loop controller, is the DC bus voltage. This parameter characterizes the utilization degree of the carrier in the SVPWM modulation process and is closely related to the strength of the harmonic components in the current.

[0034] Furthermore, calculate the ratio of each typical order harmonic according to the harmonic amplitude extracted by FFT, such as: ,

[0035] where is the fundamental amplitude, , are the fifth and seventh harmonic amplitudes respectively, which are used to analyze the suppression effect of the modulation method on specific harmonics. According to the spectral distribution, extract the effective bandwidth defined as the frequency range width when the amplitude is greater than the maximum amplitude -20 dB times, and is expressed by the formula: , where

[0036] The above frequency-domain indicators can jointly constitute a spectral feature vector for evaluating the current PWM modulation performance: This vector will be used as the input of the objective function of the subsequent optimization model.

[0037] S3. Preferably, construct multiple candidate PWM control modes, their corresponding random perturbation characteristics, objective function definitions, and state transition probability settings, and randomly select candidate modes in the control mode state space based on the Markov chain Monte Carlo algorithm; Specifically, step S3 includes: constructing a set of multiple candidate PWM control modes , where each mode corresponds to a modulation strategy (specifically including the following variable factors: 1. Carrier frequency change; 2. Modulation depth adjustment; 3. Sector switching strategy), and design a unique random perturbation characteristic function for each mode , preferably, used to describe the time evolution form of the random modulation characteristics in this mode:

[0038] where is the perturbation amplitude, is the perturbation frequency, is the initial phase, is a white noise term that satisfies the Gaussian distribution. According to the frequency-domain feature vector extracted in the aforementioned step S2 , construct an optimization objective function for each candidate control mode , and its definition is as follows: Its definition is as follows:

[0039] where is the weight coefficient, which is weighted according to the system design index. The smaller the objective function value, the better the current waveform quality of this control mode under the current working conditions.

[0040] S4. Calculate the THD of the current signal generated by the candidate PWM mode, refer to the attached instructions Figures 3 - 4 , construct an acceptance probability based on the optimization objective function, and combine the Markov chain Monte Carlo strategy to determine whether to accept this PWM mode as the current optimal state; Specifically, step S4 includes: to realize the intelligent selection of the control mode in the state space, use the Markov chain Monte Carlo algorithm to perform random jumps in the state space. Let the current control state be the PWM mode , randomly sample a new state from the candidate state set , and calculate the corresponding new THD value ; Denote the THD values corresponding to the old and new states as and , and construct the acceptance probability function for state transition as:

[0041] where T is the temperature parameter of the Markov chain Monte Carlo algorithm, used to regulate the acceptance probability and the search range; if the condition rand() < is satisfied, then accept the new state as the current PWM pattern, otherwise maintain the current state unchanged; where rand() represents a random number generation function uniformly distributed in the interval [0, 1], used to simulate the probability acceptance mechanism, making the state transition have a certain randomness, which is beneficial to jumping out of the local optimal trap. Record the PWM pattern and the corresponding THD value in each round of Markov chain Monte Carlo iteration, continuously perform state sampling and acceptance judgment, constitute the Markov chain random search process, and realize the global optimization of the harmonic characteristics under multiple PWM patterns.

[0042] S5. After updating the PWM pattern, record the current pattern and the THD value, enter the next round of Markov chain Monte Carlo sampling and evaluation iteration, and continuously perform multiple rounds of global optimization process to approximate the global optimal PWM control strategy; Specifically, step S5 includes: Set the start time and the cycle length of the Fourier analysis window, intercept the three-phase stator current signals , , in the interval of the current PWM pattern, and record them in vector form , , Perform a 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 , and its amplitude is ; The total harmonic distortion rate calculation formula is:

[0043] where, represents the amplitude of the nth harmonic component, is the cut-off order of harmonic analysis, is the fundamental wave amplitude; compare this THD value with the THD value in the previous state, and use it as the input parameter of the state acceptance function to guide the acceptance or rejection of the next control state in the Markov chain Monte Carlo chain, and continuously complete the optimization process of the PWM mode control strategy.

[0044] S6. Output the PWM control mode with the optimal THD, and generate the corresponding PWM pulse signal to output the control signal in a table-driven manner, which is used to drive the silicon carbide inverter to control the permanent magnet synchronous motor to operate.

[0045] Specifically, step S6 includes: After the Markov chain Monte Carlo algorithm goes through a preset number of iteration rounds or reaches the set convergence criterion, screen the objective function value from the set of accepted PWM control modes in all sampling histories The PWM control mode with the smallest value is denoted as the optimal PWM control strategy And generate the final PWM pulse signal for driving according to this optimal PWM control strategy. Preferably, the PWM pulse signal is based on the carrier frequency of the selected PWM mode , modulation depth , duty cycle and the reference three-phase voltage , , , construct, and the specific control signal generation formula is as follows:

[0046] where is the reference phase voltage signal, is the triangular carrier signal generated according to the selected PWM mode.

[0047] Specifically, refer to the attached Figures 5 - 6 In this embodiment, finally, by calculating the cost function in each switching state of the sampling period above, replace the data corresponding to the minimum value of the cost function as the output value to complete the EMI suppression control.

[0048] In summary, the PWM control mode optimization method based on the Markov chain Monte Carlo algorithm constructs multiple candidate PWM control strategies with different perturbation characteristics, and combines the current current spectrum characteristics and the modulation objective function value for dynamic evaluation, and preferably selects the PWM modulation mode that satisfies the balance of electromagnetic compatibility and control performance in the state space, so as to realize the adaptive control of the electric vehicle drive system under different operating conditions. This method can effectively reduce the total harmonic distortion (THD) of the output current, suppress the spread of high-frequency harmonics, and improve the stability and reliability of the system operation, and is especially suitable for high-performance and low-interference power electronic control scenarios.

[0049] The above embodiments are only used to illustrate the technical concept and features of the present invention. The purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly, and it should not be used to limit the protection scope of the present invention. Any equivalent changes or modifications made according to the spirit and essence of the present invention should be covered within the protection scope of the present invention.

Claims

1. A PWM harmonic suppression method for a vehicle permanent magnet synchronous motor drive system, characterized in that, It includes the following steps: (1) Collect the three-phase stator current signal data of the vehicle permanent magnet synchronous motor drive system, including the current components of phases 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 within the current analysis window, use the fast Fourier transform algorithm to perform frequency domain conversion on the current signal, and extract the amplitude information of the fundamental wave and each harmonic component therefrom; According to the spectrum result, calculate the spectrum distribution and total harmonic distortion rate of the current under the current PWM modulation mode as the input of the optimization objective function; (3) Construct multiple candidate sets of PWM modulation modes in the system control logic. Each PWM mode includes different carrier frequencies, modulation depths, sector switching rules, or pulse width jitter characteristic perturbation factors; Introduce the Markov chain Monte Carlo algorithm. Through the state space traversal strategy, starting from the current PWM mode state, randomly generate a new candidate PWM mode according to the transition probability rule; (4) For the generated candidate PWM control mode, on the premise of keeping other system parameters unchanged, simulate or extract its current response signal during actual operation, perform FFT frequency domain analysis and THD calculation again. Based on the optimization objective function, with the goal of minimizing THD, construct the performance difference index between the current state and the candidate state, and determine whether to accept the candidate PWM mode as the new current state through the acceptance criterion in the Markov chain Monte Carlo algorithm in combination with the acceptance probability function; (5) Store the accepted PWM mode and its corresponding THD value into the state history queue, enter the next round of Markov chain Monte Carlo sampling and evaluation iteration. During the entire Markov chain Monte Carlo optimization process, continuously perform PWM mode perturbation, performance calculation, and state transfer decision-making, conduct multiple rounds of global search and strategy screening, and finally make the system approach the optimal PWM control state; (6) When the set number of iterations is reached or the preset convergence condition is satisfied, count the PWM modulation mode with the smallest corresponding THD value among all sampling states, determine it as the optimal PWM control strategy under the current working condition, and the system generates a PWM drive signal according to this optimal PWM control strategy and outputs it to the silicon carbide power module to drive the permanent magnet synchronous motor to operate stably.

2. A PWM harmonic suppression method for a vehicle permanent magnet synchronous motor drive system according to claim 1, characterized in that The method for extracting the current waveform data within the analysis window in step (1) is as follows: According to the Clark transformation of three-phase stationary coordinate transformation, the three-phase signals , , are converted into two-phase orthogonal components , , and the transformation formula is: The transformed and components are respectively subjected to fast Fourier transform (FFT) to obtain spectral amplitude sequences and , which are used to analyze their harmonic distribution characteristics; Extract the amplitude of the fundamental component in the spectrum and the amplitudes of each high-order harmonic component , calculate the THD in the PWM mode. The calculation formula is as follows: Among them 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 index of the Markov chain Monte Carlo algorithm to evaluate the harmonic suppression performance of the current PWM control mode under a given state.

3. A PWM harmonic suppression method for a vehicle permanent magnet synchronous motor drive system according to claim 1, characterized in that In step (3), for the constructed multiple candidate sets of PWM control modes, perform random jumps and optimal solution searches in the state space based on the Markov chain Monte Carlo algorithm. Each state jump corresponds to a perturbation and switching of the PWM mode. Specifically: Set the current control state to PWM mode , randomly sample a new state from the candidate state set , and calculate the corresponding new THD value ; Denote the THD values corresponding to the old and new states as and , and construct the acceptance probability function for state transition as follows: where T is the temperature parameter of the Markov chain Monte Carlo algorithm, which is used to regulate the acceptance probability and the search range; if the condition rand() < is satisfied, then the new state is accepted as the current PWM mode, otherwise the current state remains unchanged; where rand() represents a random number generation function uniformly distributed in the interval [0, 1], which is used to simulate the probability acceptance mechanism, making the state transition have a certain randomness. Record the PWM mode and the corresponding THD value in each round of Markov chain Monte Carlo iteration, continuously perform state sampling and acceptance judgment, form a Markov chain random search process, and realize the global optimization of the harmonic characteristics under multiple PWM modes.

4. A PWM harmonic suppression method for a vehicle permanent magnet synchronous motor drive system according to claim 1, characterized in that After accepting a new PWM control mode each time, perform harmonic analysis on the current signal under the corresponding mode, use FFT to extract the spectrum distribution of the current under the current control mode, and then calculate THD and use it as the basis for Markov chain Monte Carlo optimization judgment. Specifically: Set the start time of the Fourier analysis window and the period length , and intercept the three-phase stator current signals in the current PWM mode , , The sample data in the interval is recorded in vector form , , Perform a 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 and its amplitude is ; The calculation formula for the total harmonic distortion rate is: Among them, represents the amplitude of the nth harmonic component, is the cut-off order of harmonic analysis, is the fundamental wave amplitude; compare this THD value with the THD value in the previous state, and use it as the input parameter of the state acceptance function to guide the acceptance or rejection of the next control state in the Markov chain Monte Carlo chain, and continuously complete the optimization process of the PWM mode control strategy.

5. A PWM harmonic suppression method for a vehicle permanent magnet synchronous motor drive system according to claim 4, characterized in that Calculate the THD of the current signal generated by the candidate PWM pattern, construct the acceptance probability based on the optimization objective function, and combine the Markov Chain Monte Carlo algorithm to determine whether to accept this PWM pattern as the current optimal state. Specifically: After each state transition of the Markov Chain Monte Carlo algorithm, the objective function g is constructed to quantify the control performance under the current PWM pattern. The objective function aims to minimize the THD and is constructed in the following cost function form: Among them, is the amplitude of the -th harmonic component in the spectrum under the current PWM control state, is the amplitude of the fundamental wave component, is the harmonic cut-off order; The objective function value of the current candidate state is compared with the objective function value of the previous accepted state and it is decided whether to accept this PWM control mode according to the following probability acceptance criterion: Among them, is the temperature factor for controlling the acceptance sensitivity, and controls the jump acceptance strategy according to the Metropolis-Hastings criterion.

6. A PWM harmonic suppression method for a vehicle permanent magnet synchronous motor drive system according to claim 5, characterized in that After the Markov chain Monte Carlo algorithm has gone through a preset number of iteration rounds or reached a set convergence criterion, the objective function values are screened from the set of accepted PWM control patterns in the entire sampling history The PWM control pattern with the smallest value is denoted as the optimal PWM control strategy And the final PWM pulse signal for driving is generated according to this optimal PWM control strategy. The PWM pulse signal is based on the carrier frequency of the selected PWM pattern , modulation depth , duty cycle and the reference three-phase voltage , , , and is constructed. The specific control signal generation formula is as follows: Among them is the reference phase voltage signal, is the triangular carrier signal generated according to the selected PWM mode.

7. A PWM harmonic suppression method for a vehicle permanent magnet synchronous motor drive system according to claim 5, characterized in that After each state transition, record the new PWM pattern and its THD value into the state queue and perform trend analysis. If the THD change rate is less than the set threshold for multiple consecutive iterations, it is determined that the algorithm has fallen into a local optimum, and the current state is reset to the historical optimal PWM pattern state.

8. A PWM harmonic suppression method for 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, including but not limited to sawtooth waves, triangular waves, and asymmetric waveforms.

9. A PWM harmonic suppression method for a vehicle permanent magnet synchronous motor drive system according to claim 8, characterized in that, Before finally outputting the optimal PWM control pattern, perform retrospective statistical analysis on the THD values corresponding to all accepted PWM patterns, and perform multi-objective cross-validation in combination with system constraints such as electromagnetic interference, voltage ripple, and current peak. Only output the final control pattern when the comprehensive constraint conditions are met.

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