Motor vibration noise control method and system based on modal resonance frequency prediction

Abstract

The invention provides a motor vibration noise control method and system based on modal resonance frequency prediction, and belongs to the field of motor control. The method comprises the following steps: acquiring each order of intrinsic mode frequency of the permanent magnet motor; a cost function parameter, a current constraint condition and a control target are set, and a model prediction control method is adopted to drive an inverter to operate, specifically, the current operation state quantity of the permanent magnet motor is collected; predicting a state response in a future finite time domain based on the operation state quantity; constructing a value function, and evaluating and optimizing a state change result; and selecting an optimal voltage vector, generating a pulse signal to drive an inverter to execute, obtaining a motor high-frequency electromagnetic force wave distribution condition, comparing the motor high-frequency electromagnetic force wave distribution condition with the intrinsic mode frequency of the permanent magnet motor, and optimizing an optimal voltage vector selection logic. The problems that harmonic waves are concentrated and resonance is easily caused by a fixed switching frequency modulation method in the prior art, and a passive suppression method is high in cost and poor in flexibility are solved.

Description

Technical Field

[0001] This invention belongs to the field of motor control technology, and in particular relates to a motor vibration and noise control method and system based on modal resonance frequency prediction. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Permanent magnet synchronous motors (PMSMs) have been widely used in many fields such as new energy vehicles, industrial drives, and home appliances due to their high power density, high efficiency, and excellent speed regulation performance. However, as application scenarios increasingly demand quiet operation and high comfort, the electromagnetic vibration and noise generated during operation have become increasingly prominent, becoming one of the key technological bottlenecks restricting their penetration into high-end fields.

[0004] During the operation of a permanent magnet motor, the complex internal air gap magnetic field excites electromagnetic force waves of multiple spatiotemporal orders. This physical phenomenon has become a key bottleneck restricting its performance improvement and application expansion. Among them, the 0th-order and 2p-order (p is the number of pole pairs of the motor) electromagnetic force waves are the core excitation sources causing motor vibration and noise. Under no-load conditions, the 0th-order electromagnetic force wave generated by the stator cogging effect will cause the overall structure of the motor to produce radial vibration with large amplitude in phase; the low-order 2p-order electromagnetic force wave will also have a significant impact on the motor vibration and noise.

[0005] When a motor is driven by a voltage source inverter and commonly employs space vector pulse width modulation (SVPWM) technology, the switching action introduces abundant high-frequency voltage and current harmonics. These harmonics are mainly concentrated in the "sideband" frequencies centered on the switching frequency and its integer multiples. The interaction of these high-frequency sideband currents with the permanent magnet field generates corresponding high-frequency sideband electromagnetic force waves, becoming the direct source of high-frequency vibration and noise. Particularly serious is that when the frequencies of these electromagnetic force waves introduced by the control strategy approach or coincide with a certain natural mode frequency of the motor's mechanical structure, it can trigger severe resonance, causing a sharp amplification of vibration and noise, severely impacting the motor's operational stability, reliability, and user experience. Although these problems can be mitigated by optimizing the motor's topology and parameter design, such methods typically involve complex redesign and manufacturing, resulting in high implementation costs and poor adaptability. Summary of the Invention

[0006] To overcome the shortcomings of the prior art, the present invention provides a motor vibration and noise control method and system based on modal resonance frequency prediction, which overcomes the problems of harmonic concentration and easy resonance caused by fixed switching frequency modulation method in the prior art, as well as the high cost and poor flexibility of passive suppression method.

[0007] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions: The first aspect of this invention provides a method for controlling motor vibration and noise based on modal resonance frequency prediction; A motor vibration and noise control method based on modal resonance frequency prediction includes: Modal testing was performed on the stator of the permanent magnet motor to obtain the natural modal frequencies of the permanent magnet motor. The cost function parameters, current constraints, and control objectives are set, and model predictive control is used to drive the inverter. Specifically: Collect the current operating status data of the permanent magnet motor; Based on the operating state variables, predict the state response when different inverter voltage vectors are applied in the future finite time domain. Construct a value function, and substitute the state change results into the constructed value function for evaluation and optimization; The optimal voltage vector that minimizes the function value is selected, a pulse signal is generated to drive the inverter to execute, and the distribution of the high-frequency electromagnetic force wave of the motor is obtained; By comparing the distribution of high-frequency electromagnetic force waves of the motor with the inherent mode frequencies of the permanent magnet motor, the optimal voltage vector selection logic is optimized to avoid resonance.

[0008] As a further technical solution, modal testing is performed on the stator of the permanent magnet motor to obtain the natural modal frequencies of the permanent magnet motor, including: A transient excitation is applied to the surface of the motor stator structure by a vibrating hammer. Excitation force signals are acquired by force sensors, and structural vibration response signals are acquired synchronously by a distributed array of accelerometers. The excitation force signal and vibration response signal are input into the modal analysis and data acquisition system to calculate the frequency response function and identify modal parameters, thereby obtaining the natural modal frequencies of the permanent magnet motor.

[0009] As a further technical solution, the cost function parameters include torque tracking weight, current control weight, and resonance frequency suppression optimization weight; The current constraint condition is set based on the fact that the controller's calculation output must meet the safety requirements of the motor inverter and permanent magnet, ensuring that the total stator current amplitude or dq axis current component never exceeds the preset safety boundary.

[0010] As a further technical solution, based on the aforementioned operating state variables, the state response is predicted under different inverter voltage vectors applied within a future finite time domain, including: Record the changes in state variables after the action of each effective inverter voltage vector under different initial operating state variables; Within each control cycle, the current operating state of the motor is collected and matched with the recorded state change values ​​to obtain the state change of the corresponding voltage vector. When there is no perfectly matching data, adjacent operating condition data is selected and the state change is calculated using a linear interpolation algorithm. The current operating state variables and state change variables are summed to obtain the predicted state response values ​​corresponding to each candidate voltage vector in the future finite time domain.

[0011] A discrete-time state-space model of a permanent magnet motor is established. The model is obtained by discretizing the motor's voltage equation and motion equation. The state variables include at least the dq-axis component of the stator current and the rotor electric angular velocity. Within each control cycle, the motor state variables at the current moment are collected as the initial values ​​for prediction; all effective voltage vectors that the inverter can output are enumerated; for each candidate voltage vector, the candidate voltage vector is used as the control input and substituted into the discrete mathematical model to recursively calculate the predicted values ​​of the motor state variables in one or more future sampling cycles.

[0012] As a further technical solution, the value function is:

[0013] in, Value function; This is the torque command value; To predict the torque value; The predicted stator current amplitude; The predicted frequency components of the electromagnetic force wave; These are the intrinsic modal frequencies obtained from modal testing; , , These are the torque tracking weight, current control weight, and resonance suppression weight, respectively. This is the resonance risk penalty function.

[0014] As a further technical solution, the optimal voltage vector that minimizes the function value is selected, a pulse signal is generated to drive the inverter, and the distribution of the high-frequency electromagnetic force wave of the motor is obtained, including: From all candidate voltage vectors, the one that minimizes the value function value is selected and determined as the optimal voltage vector for the current control cycle; The optimal voltage vector is converted into a corresponding space vector pulse width modulation signal or direct pulse signal, and the frequency distribution range of the high-frequency electromagnetic force wave of the motor is derived and determined based on the radial air gap magnetic flux density. The output is then sent to the switching transistor drive circuit of the inverter to control its turn-on and turn-off.

[0015] A second aspect of the present invention provides a motor vibration and noise control system based on modal resonance frequency prediction.

[0016] A motor vibration and noise control system based on modal resonance frequency prediction includes: The modal testing module is configured to perform modal testing on the stator of the permanent magnet motor, collect the frequency data of each natural mode of the motor, and transmit the data to the data storage module for storage. The data storage module is configured to store a modal frequency database, a state variable change value database, control parameter thresholds, and real-time acquired motor operation data. The model predictive control module is configured to: predict the motor state parameters in the future finite time domain by setting cost function parameters, current constraints and control objectives, and combining the state variable change value database; solve the optimal voltage vector that minimizes the cost function value through an optimization algorithm and send it to the inverter drive module. The electromagnetic force wave analysis module is configured to: derive the frequency distribution range of high-frequency electromagnetic force waves based on the number of pole pairs of the permanent magnet motor, the system fundamental frequency, and the inverter switching frequency; simultaneously analyze the amplitude and frequency distribution of high-frequency electromagnetic force waves corresponding to the output voltage vector of the model predictive control module in real time, and compare them with the inherent mode frequencies in the data storage module; if a resonance risk is determined, a parameter adjustment command is sent to the model predictive control module. The inverter drive module is configured to receive the pulse drive signal corresponding to the optimal voltage vector, control the switching of the inverter switching transistors to achieve drive and vibration noise control of the permanent magnet motor. A third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps of the motor vibration and noise control method based on modal resonance frequency prediction as described in the first aspect of the present invention.

[0017] A fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the motor vibration and noise control method based on modal resonance frequency prediction as described in the first aspect of the present invention.

[0018] The fifth aspect of the present invention provides a computer program product, including a computer program / instruction that, when executed by a processor, implements the steps in the above-described motor vibration and noise control method based on modal resonant frequency prediction.

[0019] The above one or more technical solutions have the following beneficial effects: (1) The modal resonance frequency prediction control method adopted in this invention does not change the geometric parameters and topology of the motor. Compared with the commonly used industrial methods of reducing vibration and noise by using skewed stators and segmented rotors, this method has no additional motor processing cost, avoids the generation of unbalanced magnetic pull and torsional vibration, and can effectively avoid high-frequency electromagnetic vibration. At the same time, this invention can be widely applied to various permanent magnet motors, without being limited by the pole slot matching and axial length of the permanent magnet motor, and can effectively improve the electromagnetic vibration and noise of the motor.

[0020] (2) This invention can suppress electromagnetic force waves near the motor switching frequency. Unlike fixed-mode modulation methods, model predictive control does not mechanically execute switching actions in every sampling period. Instead, it intelligently decides whether to maintain the current switching state or switch based on the real-time state of the system and the optimization objective. This working mechanism allows it to naturally form a changing switching frequency. The changing switching frequency can disperse harmonics over a wider frequency band, effectively avoiding harmonic concentration at specific frequencies, thereby significantly reducing high-frequency electromagnetic noise near the motor switching frequency. (3) The model predictive control of the present invention directly constrains the motor current harmonics and thus suppresses the amplitude of the motor electromagnetic force wave. It can actively avoid the occurrence of motor resonance by optimization and flexibly suppress resonance from the source. Compared with the traditional electromagnetic vibration noise reduction method, the electromagnetic vibration noise elimination in the present invention is more targeted.

[0021] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0022] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0023] Figure 1 This is a flowchart of the method in the first embodiment.

[0024] Figure 2 This is a schematic diagram comparing the three-phase current waveforms and the spectrum of the A-phase current of an 8-pole 48-slot permanent magnet motor under no-load conditions in the first embodiment using model predictive control.

[0025] Figure 3 This is a schematic diagram comparing the three-phase current waveforms and the spectrum of the A-phase current of an 8-pole 48-slot permanent magnet motor under rated load under model predictive control in the first embodiment.

[0026] Figure 4 This is a system structure diagram of the second embodiment. Detailed Implementation

[0027] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0028] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0029] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0030] Example 1 This embodiment discloses a motor vibration and noise control method based on modal resonance frequency prediction. By calculating the motor's natural modal frequencies, a model predictive control method is used to predict the corresponding motor state changes in future periods based on real-time state variables and inverter voltage vectors. The state change results are substituted into a preset value function for evaluation and optimization. The optimal voltage vector that minimizes the function value is selected, and a pulse signal is generated to drive the inverter to execute. This achieves the basic motor control objectives while suppressing sideband electromagnetic force waves and vibration noise near the switching frequency. At the same time, the distribution of high-frequency electromagnetic force waves of the motor is obtained and compared with the motor's natural modal resonance frequency to eliminate sideband radial electromagnetic force waves near the switching frequency and avoid resonance caused by overlap with the motor's natural modal frequencies.

[0031] Specifically, such as Figure 1 As shown, the motor vibration and noise control method based on modal resonance frequency prediction includes: Step S1: Perform modal testing on the stator of the permanent magnet motor to obtain the natural modal frequencies of the permanent magnet motor.

[0032] First, the stator structure to be tested is securely mounted on the test bench to simulate its actual operating boundary conditions, ensuring the test results are representative of engineering applications. During testing, operators use a modal excitation hammer equipped with a high-precision piezoelectric force sensor to sequentially and transiently strike several pre-selected excitation points on the stator surface, generating a wideband pulse excitation signal. Simultaneously, accelerometers distributed at key locations on the stator (such as tooth tips, yokes, and end rings) synchronously collect the structure's vibration response signals, forming a complete spatial measurement point network. All sensor signals are synchronously acquired at a high sampling rate through a multi-channel synchronous data acquisition instrument, and after signal conditioning, transmitted to a computer system with built-in professional modal analysis software.

[0033] In the signal processing stage, the acquired time-domain force signal and acceleration signals at each measuring point are first preprocessed, including windowing, filtering, and noise reduction. Subsequently, the time-domain signal is converted to the frequency domain using a fast Fourier transform, and the frequency response function of each measuring point relative to the excitation point is calculated based on the cross-power spectrum and auto-power spectrum of the input and output signals.

[0034] Based on the obtained frequency response function matrix, curve fitting is performed using parameter identification algorithms such as the multi-reference point least squares complex frequency domain method. The system automatically identifies the modal parameters of the structure, including natural frequencies, damping ratios, and mode shape vectors. Throughout the identification process, the system verifies and filters the identification results using modal judgment criteria and steady-state diagram analysis to eliminate false modes and ensure the accuracy and reliability of the extracted natural frequencies. The finally identified natural modal frequencies are stored in the system database in a structured list format, serving as key benchmark data for real-time frequency comparison and resonance risk assessment in subsequent active electromagnetic wave suppression control.

[0035] Step S2: Set the cost function parameters, current constraints and control objectives, and use model predictive control to drive the inverter.

[0036] The cost function parameters are used to quantitatively evaluate and compare the merits of different control decisions, including torque tracking weight, current control weight, and resonant frequency suppression optimization weight. The torque tracking weight penalizes the error between the actual electromagnetic torque and the target torque. A larger weight prioritizes a fast and accurate torque response during optimization. The current control weight penalizes the amplitude or error of the stator current. Its setting is typically dynamic; a higher weight is assigned below the base speed to minimize the current amplitude, improving efficiency and reducing losses. In the weak magnetic field region, the weight target is adjusted to ensure the current vector does not exceed the voltage limit circle to maintain system stability and prevent runaway. The resonant frequency suppression optimization weight consists of one or more frequency-related weight coefficients specifically designed to penalize voltage vector selections that generate electromagnetic force waves close to the inherent modal frequencies of the motor structure. When a resonance risk is predicted, the weight corresponding to that frequency component is automatically increased, guiding the controller to actively avoid control actions that could lead to resonance.

[0037] The setting of current constraints depends on the fact that the controller's calculation output must meet the safety requirements of the motor inverter and permanent magnet. Specifically, for current amplitude constraints, the combined vector amplitude of the stator three-phase current must not exceed the maximum allowable current of the inverter power devices; for dq axis current component constraints, the range of values ​​for direct axis current and quadrature axis current are restricted respectively.

[0038] Furthermore, in this embodiment, the model predictive control method does not involve the current inner loop and SVPWM module in FOC control, which is equivalent to eliminating the original current PID regulator and PWM modulation unit, and using a predictive and optimization model to select the voltage vector. The optimization objective of the predictive and optimization module of this control strategy is to minimize the error between the current and the reference current at the next moment of the system, while dispersing the current harmonics near the switching frequency, reducing high-frequency current harmonics, thereby reducing the amplitude of electromagnetic force waves at specific frequencies, and thus suppressing motor vibration and noise. Specifically, Step S21: Collect the current operating status data of the permanent magnet motor.

[0039] First, at the beginning of each control cycle, the current operating status of the motor is collected in real time through a sensor network installed on the motor and driver. This includes the three-phase stator current obtained by the current sensor, the rotor position angle and mechanical speed measured by the position sensor, and the DC bus voltage collected by the voltage sensor. These signals are then processed through analog-to-digital conversion and coordinate transformation, and used as the real-time input to the control system.

[0040] Step S22: Based on the operating state variables, predict the state response when different inverter voltage vectors are applied in the future finite time domain.

[0041] Record the state change values ​​of each effective inverter voltage vector after 1-3 control cycles under different initial operating state values, and construct a database of initial state value-voltage vector-state change value; in each control cycle, input the collected current operating state value of the motor into the database, retrieve matching offline data to obtain the state change value of the corresponding voltage vector, and when there is no completely matching data, select adjacent operating condition data and calculate the state change value through a linear interpolation algorithm.

[0042] By summing the current operating state quantities of the motor and the calculated state changes, the predicted state response values ​​corresponding to each candidate voltage vector in the future finite time domain are obtained, including the evolution of dq axis current, electromagnetic torque and speed.

[0043] Step S23: Construct a value function and substitute the state change results into the constructed value function for evaluation and optimization. For each candidate voltage vector, substitute its corresponding predicted state result into the value function for calculation; evaluate the overall performance by comparing the function values ​​corresponding to all candidate voltage vectors, providing a quantitative basis for selecting the optimal voltage vector in the future.

[0044] The value function is:

[0045] in, Value function; This is the torque command value; To predict the torque value; The predicted stator current amplitude; The predicted frequency components of the electromagnetic force wave; These are the intrinsic modal frequencies obtained from modal testing; , , These are the torque tracking weight, current control weight, and resonance suppression weight, respectively. This is the resonance risk penalty function.

[0046] Step S24: Select the optimal voltage vector that minimizes the function value, generate a pulse signal to drive the inverter to execute, and obtain the distribution of high-frequency electromagnetic force waves of the motor.

[0047] Within each control cycle, the model predictive controller traverses all candidate voltage vectors, substitutes the predicted state results corresponding to each vector into the value function for calculation, selects the voltage vector that minimizes the function value through numerical comparison, and determines it as the optimal voltage vector applied to the motor in the current control cycle.

[0048] The optimal voltage vector is then fed into the pulse generation unit. If space vector pulse width modulation is used, the duty cycle signal of the three-phase upper arm switch is calculated based on the sector where the vector is located and its duration, combined with the current DC bus voltage. If a direct torque control strategy is used, the corresponding switching state combination may be directly generated. The generated digital pulse signal is amplified by the isolation drive circuit and output to the gate of each phase switch of the inverter, controlling them to turn on and off according to a predetermined sequence, thereby synthesizing the required voltage waveform at the motor end.

[0049] Regarding the acquisition of high-frequency electromagnetic force wave distribution, the frequency distribution range of the motor's high-frequency electromagnetic force wave is derived and determined based on the radial air gap magnetic flux density. Specifically... The radial air gap magnetic flux density of a motor can be expressed as the product of the radial air gap magnetic flux density generated by the permanent magnets when the stator is not slotted and the change in the total magnetic permeability of the motor air gap caused by the slotting of the stator, as shown in the following formula:

[0050] In the formula, The air gap magnetic flux density of the motor; The radial air gap magnetic flux density generated by the permanent magnet when the stator is not slotted; Stator magnetic permeability; The amplitude of the radial air gap magnetic flux density component of order v; This is a constant component of the stator magnetic permeability; For time frequency; It is the extreme logarithm; For angle; Electrical angular velocity; For time; for l The next component is the stator permeability; The amplitude of the radial air gap magnetic flux density component of spatial order 0; Spatial order l The amplitude of the radial air gap magnetic flux density component of the order; The number of slots to be cut in the motor stator; This refers to the spatial frequency.

[0051] Electromagnetic force waves can be derived from the radial air gap magnetic flux density:

[0052] In the formula, This represents the electromagnetic force wave value. The vacuum permeability; for 0 The amplitude of the secondary magnetomotive force component.

[0053] By calculating the above expression in real time, the main frequency components and their distribution ranges of the high-frequency electromagnetic force waves that may be excited under the current switching mode are determined, with particular attention paid to the force wave components in the first sideband centered on the switching frequency and its multiples. This frequency distribution information will be compared in real time with the pre-stored inherent mode frequencies of the motor structure, serving as a key basis for optimizing voltage vector selection and avoiding resonance risks in subsequent cycles.

[0054] Step S25: Compare the distribution of the high-frequency electromagnetic force wave of the motor with the inherent mode frequency of the permanent magnet motor, optimize the optimal voltage vector selection logic, and avoid resonance.

[0055] The real-time calculated distribution of high-frequency electromagnetic force waves from the motor is compared with the pre-stored natural mode frequencies of the motor stator in a database. If certain force wave frequencies are identified as close to the natural mode frequencies (e.g., falling within a set threshold range), a resonance risk is identified. In this case, the weighting coefficients of the corresponding frequency suppression term in the value function are dynamically adjusted, or the evaluation logic of the candidate voltage vector is directly modified. In the next control cycle, voltage vectors that might excite that frequency component are preferentially avoided, thus achieving online, proactive resonance avoidance and suppressing vibration and noise amplification at the source. The entire process is executed cyclically in each control cycle, forming a closed-loop control system with frequency sensing and proactive vibration suppression capabilities.

[0056] Model predictive control (MPC) minimizes the error between the system's current and the reference current at the next moment, enabling the elimination of radial electromagnetic force waves at key orders and frequencies. The core advantage of this strategy lies in the autonomy and flexibility of its switching behavior. Unlike fixed-mode modulation methods, MPC does not mechanically execute switching actions in every sampling period. Instead, it intelligently decides whether to maintain the current switching state or switch based on the system's real-time state and optimization objectives. This mechanism allows for the natural generation of varying switching frequencies, which disperse harmonics across a wider frequency band, effectively avoiding harmonic concentration at specific frequencies. This significantly reduces high-frequency electromagnetic noise near the motor's switching frequency and allows for the acquisition of the distribution patterns of high-frequency electromagnetic force waves and high-frequency vibration noise. By comparing these patterns with the pre-calculated natural mode frequencies of the motor, resonance can be avoided when using this control scheme, preventing deterioration of the motor's vibration and noise performance.

[0057] Furthermore, to verify the effectiveness of the present invention, the motor winding current under the control of the method of the present invention was compared with the motor winding current under no-load and rated load conditions under SVPWM control. The average switching frequency of the switching devices under the two different control strategies, MPC and SVPWM, was made consistent. The sampling period of the MPC model built in this section was set to 20μs, making its average switching frequency equal to 10kHz. The results are as follows... Figure 2 and Figure 3 As shown, where, Figure 2 and Figure 3 Figure a shows the three-phase current waveform, figure b shows the current spectrum of phase A, and figure c shows the current spectrum of phase A near 10kHz / 20kHz. It can be seen that under SVPWM control, the high-frequency harmonics of the current are clearly concentrated around the first and second harmonics of the switching frequency. However, under MPC control, the high-frequency harmonics of the current do not show a clear regularity of concentration, and the maximum amplitude of the high-frequency current harmonics is about 0.6A, indicating a significant reduction in amplitude.

[0058] Example 2 This embodiment discloses a motor vibration and noise control system based on modal resonance frequency prediction; like Figure 4 As shown, the motor vibration and noise control system based on modal resonance frequency prediction includes: The modal testing module is configured to perform modal testing on the stator of the permanent magnet motor, collect the frequency data of each natural mode of the motor, and transmit the data to the data storage module for storage. The data storage module is configured to store a modal frequency database, a state variable change value database, control parameter thresholds, and real-time acquired motor operation data. The model predictive control module is configured to: predict the motor state parameters in the future finite time domain by setting cost function parameters, current constraints and control objectives, and combining the state variable change value database; solve the optimal voltage vector that minimizes the cost function value through an optimization algorithm and send it to the inverter drive module. The electromagnetic force wave analysis module is configured to: derive the frequency distribution range of high-frequency electromagnetic force waves based on the number of pole pairs of the permanent magnet motor, the system fundamental frequency, and the inverter switching frequency; simultaneously analyze the amplitude and frequency distribution of high-frequency electromagnetic force waves corresponding to the output voltage vector of the model predictive control module in real time, and compare them with the inherent mode frequencies in the data storage module; if a resonance risk is determined, a parameter adjustment command is sent to the model predictive control module. The inverter drive module is configured to receive the pulse drive signal corresponding to the optimal voltage vector, control the switching of the inverter switching transistors to achieve drive and vibration noise control of the permanent magnet motor. Example 3 The purpose of this embodiment is to provide a computer-readable storage medium.

[0059] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the motor vibration and noise control method based on modal resonance frequency prediction as described in Example 1.

[0060] Example 4 The purpose of this embodiment is to provide an electronic device.

[0061] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the motor vibration and noise control method based on modal resonance frequency prediction as described in Example 1.

[0062] Example 5 Embodiment 5 of the present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps in the motor vibration and noise control method based on modal resonance frequency prediction as described in Embodiment 1.

[0063] The steps and methods involved in the apparatuses of Embodiments 2, 3, 4, and 5 above correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0064] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0065] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A motor vibration and noise control method based on modal resonance frequency prediction, characterized in that, include: Modal testing was performed on the stator of the permanent magnet motor to obtain the natural modal frequencies of the permanent magnet motor. The cost function parameters, current constraints, and control objectives are set, and model predictive control is used to drive the inverter. Specifically: Collect the current operating status data of the permanent magnet motor; Based on the operating state variables, predict the state response when different inverter voltage vectors are applied in the future finite time domain. Construct a value function, and substitute the state change results into the constructed value function for evaluation and optimization; The optimal voltage vector that minimizes the function value is selected, a pulse signal is generated to drive the inverter to execute, and the distribution of the high-frequency electromagnetic force wave of the motor is obtained; By comparing the distribution of high-frequency electromagnetic force waves of the motor with the inherent mode frequencies of the permanent magnet motor, the optimal voltage vector selection logic is optimized to avoid resonance.

2. The motor vibration and noise control method based on modal resonance frequency prediction as described in claim 1, characterized in that, Modal testing was performed on the stator of the permanent magnet motor to obtain the natural modal frequencies of the permanent magnet motor, including: A transient excitation is applied to the surface of the motor stator structure by a vibrating hammer. Excitation force signals are acquired by force sensors, and structural vibration response signals are acquired synchronously by a distributed array of accelerometers. The excitation force signal and vibration response signal are input into the modal analysis and data acquisition system to calculate the frequency response function and identify modal parameters, thereby obtaining the natural modal frequencies of the permanent magnet motor.

3. The motor vibration and noise control method based on modal resonance frequency prediction as described in claim 1, characterized in that, The cost function parameters include torque tracking weight, current control weight, and resonant frequency suppression optimization weight. The current constraint condition is set based on the fact that the controller's calculation output must meet the safety requirements of the motor inverter and permanent magnet, ensuring that the total stator current amplitude or dq axis current component never exceeds the preset safety boundary.

4. The motor vibration and noise control method based on modal resonance frequency prediction as described in claim 1, characterized in that, Based on the aforementioned operating state variables, predict the state response under different inverter voltage vectors applied within a future finite time domain, including: Record the changes in state variables after the action of each effective inverter voltage vector under different initial operating state variables; Within each control cycle, the current operating state of the motor is collected and matched with the recorded state change values ​​to obtain the state change of the corresponding voltage vector. When there is no perfectly matching data, adjacent operating condition data is selected and the state change is calculated using a linear interpolation algorithm. The current operating state variables and state change variables are summed to obtain the predicted state response values ​​corresponding to each candidate voltage vector in the future finite time domain.

5. The motor vibration and noise control method based on modal resonance frequency prediction as described in claim 1, characterized in that, The value function is: in, Value function; This is the torque command value; To predict the torque value; The predicted stator current amplitude; The predicted frequency components of the electromagnetic force wave; These are the intrinsic modal frequencies obtained from modal testing; , , These are the torque tracking weight, current control weight, and resonance suppression weight, respectively. This is the resonance risk penalty function.

6. The motor vibration and noise control method based on modal resonance frequency prediction as described in claim 1, characterized in that, The optimal voltage vector that minimizes the function value is selected, and a pulse signal is generated to drive the inverter. The distribution of the high-frequency electromagnetic force wave of the motor is obtained, including: From all candidate voltage vectors, the one that minimizes the value function value is selected and determined as the optimal voltage vector for the current control cycle; The optimal voltage vector is converted into a corresponding space vector pulse width modulation signal or direct pulse signal, and the frequency distribution range of the high-frequency electromagnetic force wave of the motor is derived and determined based on the radial air gap magnetic flux density. The output is then sent to the switching transistor drive circuit of the inverter to control its turn-on and turn-off.

7. A motor vibration and noise control system based on modal resonance frequency prediction, characterized in that, include: The modal testing module is configured to perform modal testing on the stator of the permanent magnet motor, collect the frequency data of each natural mode of the motor, and transmit the data to the data storage module for storage. The data storage module is configured to store a modal frequency database, a state variable change value database, control parameter thresholds, and real-time acquired motor operation data. The model predictive control module is configured to: predict the motor state parameters in the future finite time domain by setting cost function parameters, current constraints and control objectives, and combining the state variable change value database; solve the optimal voltage vector that minimizes the cost function value through an optimization algorithm and send it to the inverter drive module. The electromagnetic force wave analysis module is configured to: derive the frequency distribution range of high-frequency electromagnetic force waves based on the number of pole pairs of the permanent magnet motor, the system fundamental frequency, and the inverter switching frequency; simultaneously analyze the amplitude and frequency distribution of high-frequency electromagnetic force waves corresponding to the output voltage vector of the model predictive control module in real time, and compare them with the inherent mode frequencies in the data storage module; if a resonance risk is determined, a parameter adjustment command is sent to the model predictive control module. The inverter drive module is configured to receive the pulse drive signal corresponding to the optimal voltage vector, control the switching of the inverter switching transistors to achieve drive and vibration noise control of the permanent magnet motor.

8. A computer-readable storage medium having a program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the motor vibration and noise control method based on modal resonance frequency prediction as described in any one of claims 1-6.

9. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the motor vibration and noise control method based on modal resonance frequency prediction as described in any one of claims 1-6.

10. A computer program product comprising a computer program / instructions that, when executed by a processor, implement the steps of the motor vibration and noise control method based on modal resonant frequency prediction as described in any one of claims 1-6.

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