Motor permanent magnet health state online evaluation and residual life prediction method and system

Abstract

The application discloses a motor permanent magnet health state online evaluation and residual life prediction method and system, and belongs to the technical field of motor state monitoring and fault prediction. The method comprises the following steps: state monitoring and signal acquisition working condition judgment; collecting back electromotive force signals and d-axis injection optimized high-frequency signals; FFT analysis, THD calculation, online identification of d-axis inductance Ld; F_THD and ALd calculation; normalization and cross-validation of characteristic quantities F_THD and ALd; under three-phase voltage balance, permanent magnet health index HI_magnet calculation; fitting of the downward trend of HI_magnet, and prediction of residual useful life RUL. The application can significantly improve the evaluation accuracy and robustness by fusing two strongly related and complementary characteristic quantities of back electromotive force harmonic distortion F_THD and d-axis inductance change ALd, realizes real-time early warning of permanent magnet demagnetization failure, and realizes trend prediction of residual life.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of motor state monitoring and fault prediction, more specifically to a motor permanent magnet health state online evaluation and residual life prediction method and system. BACKGROUND

[0002] Permanent magnet synchronous motor (PMSM) is the core power component of electric vehicles, and the health state of its permanent magnet directly affects the performance, efficiency and safety of the motor. Permanent magnets are prone to irreversible demagnetization under harsh conditions such as high temperature, overcurrent and vibration, leading to reduced motor performance and efficiency, and even causing serious faults. Therefore, it is crucial to accurately evaluate and predict the life of the permanent magnet health state in real time.

[0003] Chinese invention patent with publication number CN120541787A discloses a permanent magnet motor demagnetization fault diagnosis method based on deep learning. The patent uses "adaptive diagnosis strategy + U-Net prediction", although it introduces a practical strategy of dynamically adjusting the monitoring frequency, but its feature extraction still relies on additional vibration sensors, increasing the system hardware cost and installation complexity. At the same time, its U-Net model and bidding auction mechanism system are complex, and essentially it is still a data-driven black box model, which lacks integration with the physical mechanism of the motor. The deployment and operation of complex neural networks on the vehicle MCU are extremely challenging, with high power consumption and cost.

[0004] Chinese invention patent with publication number CN118686780A discloses a method for estimating the service life of an oil pump based on feedback signals. The patent calculates the deviation rate of multiple parameters (current, speed, temperature) relative to the initial value, sums the weighted values to obtain a comprehensive failure rate, and estimates the life loss according to the failure rate range. This method is too simple and linear, and fails to capture the nonlinear degradation process of permanent magnet demagnetization. Moreover, the weight setting relies on prior knowledge, lacks adaptability and scientificity, and the prediction accuracy is limited. SUMMARY

[0005] The present application aims to overcome the shortcomings of the prior art and provide a motor permanent magnet health state online evaluation and residual life prediction method.

[0006] The present application adopts the following technical solutions:

[0007] The motor permanent magnet health state online evaluation and residual life prediction method comprises the following steps:

[0008] Step one, state monitoring and signal collection working condition judgment, including back electromotive force signal collection working condition and d-axis inductance identification working condition;

[0009] Step two, signal acquisition and reconstruction: S21, under the condition of back electromotive force signal acquisition, collect the back electromotive force signal, including collecting three-phase terminal voltage and collecting rotor position information; S22, under the condition of d-axis inductance identification, the control algorithm superimposes a high-frequency sinusoidal voltage signal or a spread spectrum signal on the d-axis voltage command;

[0010] Step three, signal alignment and feature extraction: S31, align and coordinate transform the collected three-phase terminal voltage signal according to the synchronously collected rotor position information, convert it from the stationary three-phase coordinate system to the coordinate system rotating synchronously with the rotor, and obtain the back electromotive force waveform; S32, digitally filter the obtained back electromotive force waveform to extract the pure signal for THD calculation;

[0011] Step four, waveform transformation and F_THD calculation: perform fast Fourier transform on the aligned back electromotive force waveform to obtain its frequency spectrum; calculate the total harmonic distortion rate F_THD of the back electromotive force waveform;

[0012] Step five, online parameter identification and feature extraction: S51, extract the d-axis inductance parameter Ld of the motor in the d-axis inductance identification condition, and identify the d-axis inductance parameter Ld in real time; S52, establish a long-term historical record of the d-axis inductance parameter Ld, track its change trend, and calculate its offset ΔLd relative to the initial health value as a characteristic quantity representing the change of magnetic circuit saturation degree;

[0013] Step six, feature normalization and cross-validation: S61, normalize the characteristic quantities F_THD and ΔLd to eliminate the influence of dimensions and obtain the normalized feature vector: [F_THD_norm, ΔLd_norm]; S62, introduce cross-validation logic: when F_THD_norm abnormally rises, the system checks whether the collected three-phase voltage instantaneous values are balanced, if the three-phase voltage imbalance degree is out of limit, it is determined as winding fault; if balanced, go to step seven;

[0014] Step seven, feature fusion and health assessment: input the normalized feature vector through cross-validation into the pre-set weight function, and output the comprehensive permanent magnet health index HI_magnet;

[0015] Step eight, life prediction: fit the descending trend of HI_magnet, predict the time or mileage required for its value to drop to the pre-set failure threshold, i.e. the remaining useful life RUL.

[0016] The step one specifically comprises: S11, the drive torque instruction is zero and the motor speed is higher than a preset threshold, namely the no-load coasting state; or the drive torque instruction is lower than a first threshold and the motor speed fluctuation is lower than a second threshold, namely the light-load constant-speed operation state, then entering the back electromotive force signal collection condition; S12, the drive torque instruction is lower than a third threshold and the motor speed is stable, namely the steady-state light-load operation state, then entering the d-axis inductance identification condition.

[0017] In a preferred embodiment, the d-axis inductance identification is preferably performed by using a simplified model reference adaptive or recursive least square method.

[0018] In a preferred embodiment, the specific implementation of S21 in the step two is as follows: in the back electromotive force signal collection condition, the controller keeps the inverter PWM output, but adopts the minimum duty cycle mode or the blanking strategy, and collects the three-phase terminal voltage by using the built-in phase voltage sensor of the motor controller or through the software reconstruction of the DC bus voltage and the inverter switch state; at the same time, the rotor position information is synchronously collected by using the high-precision rotary transformer.

[0019] In a preferred embodiment, the step four uses a sliding window FFT instead of a full-size FFT to calculate F_THD.

[0020] In a preferred embodiment, the normalized boundary values (*_min, *_max) in the step six are determined by a bench accelerated aging test.

[0021] In a preferred embodiment, the weight function in the step seven has the expression:

[0022] HI_magnet = 1 - (w1 * F_THD_norm + w2 * ΔLd_norm)

[0023] wherein w1 and w2 are weight coefficients, and w1 + w2 = 1, and the values thereof are determined by fitting the data of the accelerated demagnetization aging test; the output range of HI_magnet is [0, 1], and 1 represents health and 0 represents complete failure.

[0024] The weight coefficients w1 and w2 are determined by performing an accelerated demagnetization aging test on not less than two same-type samples on a bench, specifically as follows: collecting the F_THD and ΔLd data under different demagnetization degrees, and recording the real performance attenuation degree to normalize the real health index HI_actual; using a least square method or other fitting algorithm to solve the weight values w1 and w2 that make the error of the formula HI_actual≈ 1 - (w1 * F_THD_norm + w2 * ΔLd_norm) minimum.

[0025] In a preferred embodiment, the exponential smoothing method, linear regression or weighted moving average method is used to fit the downward trend of HI_magnet in step eight.

[0026] In a preferred embodiment, step eight further comprises early warning, specifically: when the real-time monitored HI_magnet is lower than the health threshold, or the predicted RUL is lower than the safety value, the controller area network (CAN) bus sends a hierarchical early warning message to the vehicle instrument panel or the cloud monitoring platform.

[0027] The application also provides an electric motor control system, comprising a processor and a memory, the memory storing a computer program, and the processor executing the computer program to realize the method for online evaluation of the health state of the permanent magnet of the electric motor and prediction of the remaining useful life.

[0028] From the above description of the application, compared with the prior art, the application has the following advantages:

[0029] 1. The application calculates the health index (HI_magnet) by fusing the two strongly correlated and complementary characteristic quantities of back electromotive force harmonic distortion (F_THD) and d-axis inductance change (ΔLd), and predicts the remaining useful life (RUL) accordingly. The application significantly improves the evaluation accuracy and robustness, realizes real-time early warning of permanent magnet demagnetization failure, and realizes trend prediction of the remaining useful life.

[0030] 2. Inductive online monitoring: fully utilizing existing vehicle sensor and controller resources, without increasing any hardware cost, realizing real-time embedded evaluation at the vehicle end.

[0031] 3. Optimized signal acquisition strategy: under no-load coasting / light-load constant-speed working conditions, the back electromotive force is acquired through low-interference PWM and filtering technology; under steady-state light-load working conditions, high-frequency signals with optimized parameters are injected to identify the d-axis inductance, which minimizes the impact on system performance while ensuring the quality of the characteristics.

[0032] 4. Robustness design based on cross-validation: introducing three-phase voltage balance check, cross-validating the back electromotive force characteristics, effectively distinguishing permanent magnet demagnetization from winding failure, and avoiding misjudgment.

[0033] 5. Weight self-adaptation based on bench data: scientifically determining the characteristic weight through reliability test data, making the health index more consistent with the actual physical degradation process, and avoiding the limitations of artificial experience setting. BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1 The flowchart of the application.

[0035] Figure 2 The flowchart of the application for remaining useful life prediction and early warning release. DETAILED DESCRIPTION

[0036] The specific embodiments of the present application are described below with reference to the accompanying drawings. In order to fully understand the present application, numerous specific details are described below, but the present application can be implemented without these details. For the known components, methods and processes, the following will not be described in detail.

[0037] The motor permanent magnet health state online evaluation and residual life prediction method refers to Figure 1 , comprising the following steps:

[0038] Step one, state monitoring and signal acquisition working condition judgment

[0039] The motor controller monitors the driving torque instruction and motor speed in real time. When it is judged to meet any of the following working conditions, the corresponding signal acquisition process is triggered.

[0040] 1. Back-EMF signal acquisition working condition: the driving torque instruction is zero and the motor speed is higher than the preset threshold (for example, 500 rpm), that is, the no-load coasting state; or the driving torque instruction is lower than the first threshold (such as 10% rated torque) and the motor speed fluctuation is lower than the second threshold (such as ±2%), that is, the light load constant speed running state.

[0041] 2. d-axis inductance identification working condition: the driving torque instruction is lower than the third threshold (such as 20% rated torque) and the motor speed is stable, that is, the steady-state light load running state.

[0042] Step two, signal acquisition and reconstruction

[0043] 1. Collect back-EMF signal: in the back-EMF signal acquisition working condition, the controller keeps the inverter PWM output, but adopts the minimum duty cycle mode (such as 1%) or a specific blanking strategy to reduce switching noise interference. The three-phase terminal voltage (Ua, Ub, Uc) is collected by using the phase voltage sensor built-in the motor controller (or by collecting the DC bus voltage and inverter switching state for software reconstruction).

[0044] 2. d-axis injection inductance identification signal: in the d-axis inductance identification working condition, the control algorithm superimposes a high-frequency sinusoidal voltage signal or spread spectrum signal with optimized amplitude (such as 5V) and frequency (such as 500Hz) on the d-axis voltage instruction for online identification. The selection of this working condition and the optimization of signal parameters aims to minimize the impact on motor NVH and operating efficiency.

[0045] Step three, signal alignment and feature extraction

[0046] The collected three-phase terminal voltage signals are aligned and coordinate-transformed according to the synchronously collected rotor position information, converted from the stationary three-phase coordinate system (a-b-c) to the coordinate system rotating synchronously with the rotor, and the back-EMF waveform is obtained. The obtained back-EMF waveform is digitally filtered (such as a band-pass filter) to further suppress switching noise and background interference, and the pure signal used for THD calculation is extracted.

[0047] Step four, waveform transformation and F_THD calculation

[0048] 1. Perform fast Fourier transform (FFT) on the aligned back-EMF waveform to obtain its frequency spectrum.

[0049] 2. Calculate the total harmonic distortion rate (F_THD) of the back-EMF waveform.

[0050] Step five, online parameter identification and feature extraction (ΔLd)

[0051] When the motor is in the d-axis inductance identification condition, the d-axis inductance parameter Ld of the motor is identified online in real time by using the high-frequency signal injected by the control algorithm itself or through a model reference adaptive system (MRAS).

[0052] A long-term historical record of the d-axis inductance parameter Ld is established, the change trend is tracked, and the offset ΔLd relative to the initial healthy value is calculated as a characteristic quantity representing the change in magnetic circuit saturation degree.

[0053] Step six, feature normalization and cross-validation

[0054] The characteristic quantities F_THD and ΔLd are normalized to eliminate the influence of dimensions, and the normalized feature vector [F_THD_norm, ΔLd_norm] is obtained.

[0055] The normalized boundary values (*_min, *_max) are determined by the bench accelerated aging test.

[0056] Before health assessment, cross-validation logic is introduced. When F_THD_norm abnormally rises, the system checks whether the collected three-phase voltage instantaneous values are balanced (for example, whether the difference between the effective values of the three-phase voltages exceeds the preset threshold, such as 5%). If the three-phase voltage imbalance exceeds the limit, it is determined that the winding is likely to be faulty, and a winding fault warning is triggered, instead of immediately performing permanent magnet health assessment, thereby avoiding false judgment caused by local failure of the winding.

[0057] Step seven, feature fusion and health assessment

[0058] The normalized feature vector passed through cross-validation is input into the pre-set weight function, and the comprehensive permanent magnet health index HI_magnet is output.

[0059] The expression of the weight function is:

[0060] HI_magnet = 1 - (w1 * F_THD_norm + w2 * ΔLd_norm)

[0061] where w1 and w2 are weight coefficients, and w1 + w2 = 1; their values are determined by fitting the data of the bench aging test, reflecting the importance of different characteristics.

[0062] The output range of HI_magnet is [0, 1], 1 represents health, and 0 represents complete failure.

[0063] Step eight: bench data and weight determination

[0064] The weight coefficients w1 and w2 are determined by performing an accelerated demagnetization aging test on a certain number (such as no less than 2) of same type samples on a bench. Collect F_THD and ΔLd data at different demagnetization levels, and record the real performance decay degree (such as back electromotive force amplitude drop rate) to normalize the real health index HI_actual. Use least squares method and other fitting algorithms to solve the weight values w1 and w2 that minimize the error of the formula HI_actual ≈ 1 - (w1 * F_THD_norm + w2 * ΔLd_norm).

[0065] Step nine: life prediction and early warning

[0066] Continuously record the change of HI_magnet with time or running mileage to form a health index time series.

[0067] Use time series prediction algorithms (such as exponential smoothing, ARIMA model, etc.) to fit the downward trend of HI_magnet, and predict the time length or mileage required for its value to drop to the preset failure threshold, i.e. the remaining useful life (RUL).

[0068] When the real-time monitored HI_magnet is lower than the health threshold, or the predicted RUL is lower than the safety value, send a hierarchical early warning message to the vehicle instrument panel or cloud monitoring platform through the controller area network (CAN) bus.

[0069] The application also provides an electric motor control system, which includes a processor and a memory, the memory stores a computer program, and the processor implements the method for online evaluation of the health state of the permanent magnet of the electric motor and prediction of the remaining life when executing the computer program.

[0070] The following is a specific implementation case of the application.

[0071] I. Preset parameters

[0072] Counter EMF signal acquisition condition judgment threshold: torque = 0 and speed > 600 r / min, or torque < 5% rated torque and speed fluctuation < ± 2%.

[0073] d-axis inductance identification condition judgment threshold: torque < 10% rated torque and speed stable.

[0074] Counter EMF THD calculation range: 2nd to 50th harmonic.

[0075] Inductance parameter identification method: high-frequency signal injection, parameters: d-axis, 500Hz, 5V amplitude, using spread spectrum modulation.

[0076] Health index failure threshold HI_fail: 0.3.

[0077] Health warning threshold HI_warn: 0.6.

[0078] Weight coefficient (determined by bench test fitting): w1 = 0.6, w2 = 0.4.

[0079] Three-phase voltage unbalance threshold: 5%.

[0080] II. Health assessment process

[0081] 1. Real-time data acquisition

[0082] Counter EMF acquisition period: When the vehicle enters the counter EMF signal acquisition condition, the inverter uses a low interference PWM mode, and synchronously acquires three-phase voltage and rotor position.

[0083] d-axis inductance identification period: When the vehicle enters the d-axis inductance identification condition, the control algorithm (injects high-frequency signal according to optimized parameters), extracts the response component from the phase current through the filter, and calculates the current value of Ld in real time.

[0084] 2. Feature processing

[0085] (1) Calculate the total harmonic distortion rate F_THD after coordinate system transformation and FFT:

[0086]

[0087] : RMS of counter EMF fundamental.

[0088] : RMS of the nth harmonic.

[0089] N: The highest harmonic number to be considered.

[0090] Taking N = 50, the calculation result F_THD_current = 10.15%.

[0091] (2) Calculate the offset percentage of current Ld_current and new vehicle state initial Ld_initial to obtain ΔLd:

[0092]

[0093] Ld_current: the current real-time online identified d-axis inductance value; the identification result is Ld_current = 0.55 mH.

[0094] Ld_initial: the initial d-axis inductance reference value, when the vehicle is powered on for the first time and the system judges that the motor is in a healthy state (normal temperature, no fault code), the Ld is measured multiple times and the average value is stored in the MCU control board EEPROM; Ld_initial = 0.5 mH.

[0095] The calculation result ΔLd = 10%.

[0096] (3) Normalize F_THD and ΔLd:

[0097]

[0098]

[0099] F_THD_current: the current calculated total harmonic distortion rate original feature value; ΔLd_current: the current calculated Ld and the offset percentage of the initial Ld of the new vehicle state original feature value.

[0100] *_min and *_max: normalization boundary values, the boundary values are determined by the bench aging test, representing the reasonable range of the feature from "complete health" to "serious failure".

[0101] Determined by bench test:

[0102] F_THD range: about 3% when healthy, about 20% when failed. Therefore, set F_THD_min = 3, F_THD_max = 20.

[0103] ΔLd range: about 0% when healthy, about 25% when failed. Therefore, set ΔLd_min = 0, ΔLd_max = 25.

[0104] Then the normalized result is:

[0105] F_THD_norm = (10.15-3) / (20-3) = 0.42;

[0106] ALd norm = (10 - 0) / (25 - 0) = 10 / 25 = 0.40.

[0107] After calculating F THD norm, the system checks the three-phase voltage unbalance degree in this acquisition cycle. Calculate the three-phase voltage effective value Ua_rms, Ub_rms, Uc_rms, if

[0108]

[0109] Determine that the voltage is unbalanced, this time F THD feature is invalid, trigger the "suspected winding fault, please check" warning, and do not update HI_magnet temporarily.

[0110] 3、Health index calculation

[0111] Call the weight function to calculate the current health index: HI_magnet = 1 - (0.6 * F_THD_norm + 0.4 * ALd_norm); Calculate HI_magnet = 1 - (0.6 * 0.42 + 0.4 * 0.4) = 0.59.

[0112] 4、Result judgment

[0113] The calculated HI_magnet = 0.59 is lower than the health warning threshold 0.6, and the system sends a prompt message to the instrument through the CAN bus: "motor permanent magnet performance moderate attenuation, suggest attention".

[0114] Three, life prediction process (refer to Figure 2 )

[0115] 1、History data record and management

[0116] The system opens a storage area in the MCU control board EEPROM to continuously record the health index HI_magnet calculated each time and its corresponding cumulative running mileage Mileage. In order to save storage space, "circular queue" or "equal interval mileage record" strategy can be used.

[0117] History record table

[0118]

[0119] 2、Trend prediction algorithm (one section index smoothing method)

[0120] Exponential smoothing method is used for trend fitting and prediction, and the calculation formula is:

[0121]

[0122] St: smoothed prediction value at current mileage point t; St-1: smoothed prediction value at last mileage point t-1; Xt: actual observation value at current mileage point t (i.e. HI_magnet); a: smoothing factor, value range (0, 1).

[0123] The last four data points are demonstrated as follows, smoothing factor a = 0.3, and set the initial smoothed value St-1 equal to the first actual observation value.

[0124] Mileage (km) Actual HI Xt Smoothed predicted HI St 45 0.73 0.73 Initial value 50 0.65 0.706 55 0.61 0.638 60 0.59 0.604

[0125] The smoothed prediction value St at current mileage point (0.6 million km) is obtained as 0.604.

[0126] 3. Residual useful life (RUL) calculation

[0127] After obtaining the trend line, the mileage required for the health index to drop to the preset failure threshold HI_fail is predicted.

[0128] Define the failure threshold: HI_fail = 0.3 (when the health index is lower than this value, the permanent magnet is considered to have failed).

[0129] Calculate the trend slope: use the last two smoothed prediction values to calculate the current health index decline slope a

[0130]

[0131] The calculated a is -0.0068 (decrease rate per 10,000 km).

[0132] Calculate the residual life (RUL):

[0133]

[0134] To make the prediction more stable, where HI_current uses the current trend value St, not the instantaneous observation value Xt (the instantaneous observation value Xt will contain various random noise and short-term interference).

[0135] The calculated RUL is 44.7 million km.

[0136] 4. Warning release

[0137] The system compares the predicted RUL value with the preset safety threshold, and releases the corresponding level of warning information through the CAN bus.

[0138] Predicted RUL > 10 million km: normal state, no warning needed.

[0139] ​5 million km < Predicted RUL ≤ 10 million km: Level 1 warning (hint). A dashboard prompt "Suggest to check motor system when next maintenance" is given.

[0140] 1 million km < Predicted RUL ≤ 5 million km: Level 2 warning (warning). A dashboard warning "Motor permanent magnet performance degradation, suggest to plan maintenance" is given.

[0141] Predicted RUL ≤ 1 million km: Level 3 warning (serious). A dashboard pop-up warning "Motor system needs immediate overhaul!" is given immediately, and the motor peak power output is limited.

[0142] According to the above calculation, the current health index trend indicates that the permanent magnet will fail after about 44.7 million kilometers, and the state is normal, without warning.

[0143] The above is only a specific embodiment of the present application, but the design concept of the present application is not limited thereto, and any non-essential modification of the present application using this concept shall be deemed to be an infringement of the protection scope of the present application.

Claims

1. A method for on-line evaluation of health state and prediction of residual life of permanent magnet of electric machine, characterized in that, Comprise the following steps: Step one, state monitoring and signal acquisition working condition judgment, including back electromotive force signal acquisition working condition and d-axis inductance identification working condition; Step two, signal acquisition and reconstruction: S21, in the back electromotive force signal acquisition working condition, collect back electromotive force signal, including collecting three-phase terminal voltage and collecting rotor position information;S22, in the d-axis inductance identification working condition, control algorithm superimposes a high-frequency sinusoidal voltage signal or spread spectrum signal on the d-axis voltage instruction; Step three, signal alignment and feature extraction: S31, the collected three-phase terminal voltage signal is aligned and coordinate transformed according to the synchronous collected rotor position information, which is converted from the stationary three-phase coordinate system to the coordinate system synchronous with the rotor, to obtain the back electromotive force waveform;S32, the obtained back electromotive force waveform is digitally filtered to extract the pure signal for THD calculation; Step four, waveform transformation and F_THD calculation: the aligned back electromotive force waveform is subjected to fast Fourier transform to obtain its frequency spectrum;Calculate the total harmonic distortion rate F_THD of the back electromotive force waveform; Step five, online parameter identification and feature extraction: S51, extract the d-axis inductance parameter Ld of the motor in the d-axis inductance identification working condition;S52, establish a long-term historical record of the d-axis inductance parameter Ld, track its change trend, calculate its offset ΔLd relative to the initial health value, as a characteristic quantity representing the change of magnetic circuit saturation degree; Step six, feature normalization and cross-validation: S61, normalize the characteristic quantities F_THD and ΔLd to eliminate the influence of dimension, and obtain the normalized feature vector: [F_THD_norm, ΔLd_norm];S62, introduce cross-validation logic: when F_THD_norm abnormally rises, the system checks whether the collected three-phase voltage instantaneous value is balanced, if the three-phase voltage imbalance degree is out of limit, it is judged as winding fault;If balanced, go to step seven; Step seven, feature fusion and health assessment: input the normalized feature vector through cross-validation into the pre-set weight function, and output the comprehensive permanent magnet health index HI_magnet; Step eight, life prediction: fit the descending trend of HI_magnet, predict the time length or mileage required for its value to drop to the pre-set failure threshold, that is, the remaining useful life RUL.

2. The method of claim 1, wherein the method further comprises: The step one specifically comprises: S11, the drive torque instruction is zero and the motor speed is higher than a pre-set threshold, that is, the no-load coasting state;Or the drive torque instruction is lower than the first threshold and the motor speed fluctuation is lower than the second threshold, that is, the light load constant speed running state, then enter the back electromotive force signal acquisition working condition;S12, the drive torque instruction is lower than the third threshold and the motor speed is stable, that is, the steady-state light load running state, then enter the d-axis inductance identification working condition.

3. The method of claim 1 or 2, wherein: Simplified model reference adaptive or recursive least squares method is used for d-axis inductance identification.

4. The method of claim 1, wherein the method further comprises: The specific implementation of S21 in the second step is as follows: in the condition of back electromotive force signal acquisition, the controller keeps the PWM output of the inverter, adopts the minimum duty cycle mode or the blanking strategy, acquires the three-phase terminal voltage by using the phase voltage sensor built in the motor controller or through software reconstruction by acquiring the DC bus voltage and the inverter switch state; at the same time, the rotor position information is synchronously acquired by using a high-precision rotary transformer.

5. The method of claim 1, wherein: The fast Fourier transform in the fourth step is a sliding window FFT or a full-size FFT.

6. The method of claim 1, wherein the method further comprises: determining the health state of the permanent magnet of the electric machine based on the magnetic field distribution. The normalized boundary value in step six ( _min, The value of _max was determined by accelerated aging tests on a bench.

7. The method of claim 1, wherein: The weight function in the seventh step has an expression as follows: HI_magnet = 1 - (w1 F_THD_norm + w2 ΔLd_norm) wherein w1 and w2 are weight coefficients, and w1 + w2 = 1, and the values are determined by fitting of bench aging test data; the output range of HI_magnet is [0, 1], 1 represents health, and 0 represents complete failure.

8. The method of claim 7, wherein: The weight coefficients w1 and w2 are determined by accelerated demagnetization aging tests on not less than 2 same type samples on a test bench, specifically as follows: collect F_THD and ALd data under different demagnetization degrees, and record the real performance attenuation degree to normalize the real health index HI_actual; use the least square fitting algorithm to solve the weight values w1 and w2 that make the error of the formula HI_actual ≈ 1 - (w1 F_THD_norm + w2 ALd_norm) minimum.

9. The method of claim 1, wherein: In the eighth step, the exponential smoothing method, linear regression or weighted moving average method is used to fit the downward trend of HI_magnet.

10. The method of claim 1, wherein: The eighth step further includes early warning, specifically: when the real-time monitored HI_magnet is lower than the health threshold, or the predicted RUL is lower than the safety value, the controller local area network bus sends hierarchical early warning information to the vehicle instrument panel or the cloud monitoring platform.

11. An electric machine control system comprising a processor and a memory, characterized by: The memory stores a computer program, and the processor implements the motor permanent magnet health state online evaluation and residual life prediction method according to any one of claims 1 to 10 when executing the computer program.

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