Motor performance test method and system

Abstract

The invention relates to the technical field of motor performance testing, in particular to a motor performance testing method and system. The method comprises the following steps: firstly, collecting a motor current state of a new energy vehicle under sudden stop and sudden start conditions, and carrying out missing value filling and spectrogram conversion to obtain a current state spectrogram; the current divergence intensity is analyzed based on the spectrogram, and the output torque performance attenuation ratio under the time dimension is simulated; and finally, motor performance mechanism evaluation is completed according to the attenuation ratio. According to the invention, the motor performance test technology is optimized, so that the motor performance test technology is more perfect.

Description

Technical Field

[0001] This invention relates to the field of motor performance testing technology, and in particular to a method and system for testing motor performance. Background Technology

[0002] The emergency stop and start-up state of new energy vehicle motors is a critical operating scenario, during which parameters such as motor current waveform, torque output, and efficiency undergo drastic changes. Failure to accurately capture motor performance data under these transient conditions will directly lead to distorted motor performance evaluations, consequently affecting the optimization of vehicle control algorithms and safety performance assessments. Therefore, the need for in-depth research and testing of motor transient characteristics is increasingly urgent. Against this backdrop, it is crucial to systematically collect current data from new energy vehicle motors under emergency stop and start-up experimental conditions and construct accurate current models using missing value imputation techniques. Furthermore, spectral analysis methods can deeply analyze the current divergence intensity, thereby revealing the characteristics of motor performance degradation. Finally, based on these analytical results, a reasonable motor performance evaluation mechanism can be developed to support engineering practice, enabling more refined motor design and control, thereby improving the overall performance and safety of new energy vehicles.

[0003] In summary, traditional motor performance testing methods suffer from low accuracy and a lack of a complete performance testing technology roadmap. Summary of the Invention

[0004] Therefore, it is necessary to provide a method and system for testing motor performance to solve at least one of the above-mentioned technical problems.

[0005] To achieve the above objectives, a method for testing motor performance includes the following steps: Step S1: Collect the motor current status under the emergency stop and start test conditions of new energy vehicles, fill in the missing values ​​of the motor current status, and output the current missing value filled status; perform spectrum diagram conversion on the current missing value filled status to obtain the current status spectrum diagram. Step S2: Analyze the current divergence intensity based on the current state spectrum diagram; simulate the output torque performance attenuation ratio in the time dimension based on the current divergence intensity to obtain the output torque performance attenuation ratio. Step S3: Evaluate the motor performance mechanism based on the output torque performance attenuation ratio to output motor performance mechanism data.

[0006] Preferably, the present invention also provides a motor performance testing system for performing the motor performance testing method described above, the motor performance testing system comprising: The spectrum conversion module is used to collect the motor current status under the test conditions of emergency stop and start of new energy vehicles, fill in the missing values ​​of the motor current status, and output the current missing filling status; the spectrum conversion of the current missing filling status is performed to obtain the current status spectrum. The attenuation simulation module is used to analyze the current divergence intensity based on the current state spectrum diagram; and to simulate the output torque performance attenuation ratio in the time dimension based on the current divergence intensity, so as to obtain the output torque performance attenuation ratio. The mechanism evaluation module is used to evaluate the motor performance mechanism based on the output torque performance attenuation ratio, and output motor performance mechanism data.

[0007] The beneficial effect of this invention lies in its ability to comprehensively understand the real-time performance of a motor in extreme operating environments by collecting the motor current state under emergency stop and start test conditions of new energy vehicles. However, in actual data acquisition, due to noise, signal interference, or sensor failure, current state data may be missing. By filling in the missing values ​​of the motor current state, the integrity and accuracy of the data are ensured, laying a good foundation for subsequent analysis. Using spectrum conversion, the current state after missing value filling can be transformed into frequency domain features, facilitating further analysis and visualization, and providing a deeper understanding of the motor's operating state. This processing method not only improves the reliability of the data but also provides a more comprehensive basis for diversified motor performance evaluation. By analyzing the current state spectrum, the current divergence intensity can be studied in depth. This indicator effectively reflects the stability and reliability of the motor under dynamic operating conditions. The greater the divergence intensity, the greater the transient fluctuations in the motor during operation, corresponding to higher energy loss and efficiency reduction. Simulating the output torque performance attenuation ratio over time based on the current divergence intensity can quantify how motor performance changes with operating conditions. This attenuation ratio not only provides important reference data for motor design optimization but also offers theoretical support for fault diagnosis, condition monitoring, and predictive maintenance, helping to ensure the safety and economy of electric vehicles. Evaluating the motor performance mechanism based on the attenuation ratio of output torque performance provides a scientific basis for maintaining the motor's long-term performance. This evaluation allows for a deeper study of the motor's operating mechanism, aging characteristics, and loss factors, thereby identifying key factors affecting motor performance. The acquired motor performance mechanism data not only helps engineers identify potential fault risks but also provides effective suggestions for subsequent motor design improvements and debugging schemes. Furthermore, this evaluation result can positively promote the improvement of the overall performance of electric vehicles, optimizing the system's power performance and providing an important basis for formulating reasonable operation and maintenance strategies. Therefore, this invention is an optimization of a traditional motor performance testing method, solving the problems of low accuracy and lack of a complete performance testing technical route in traditional methods, thus improving the accuracy of motor performance testing and providing a complete performance testing technical route. Attached Figure Description

[0008] Figure 1 A schematic diagram illustrating the steps of a motor performance testing method; Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S2. Detailed Implementation

[0009] Please see Figure 1A method for testing the performance of an electric motor, the method comprising the following steps: Step S1: Collect the motor current status under the emergency stop and start test conditions of new energy vehicles, fill in the missing values ​​of the motor current status, and output the current missing value filled status; perform spectrum diagram conversion on the current missing value filled status to obtain the current status spectrum diagram. In this embodiment of the invention, during an emergency stop and start test of a new energy vehicle, a three-phase current sensor installed on the stator side of the motor continuously collects transient current data of the motor within a speed range of 120km / h to 140km / h at a sampling frequency of 2kHz. During the experiment, the amplitude and phase information of the current are recorded in each interval from emergency stop to restart. After discretizing the collected data, a method based on cubic spline interpolation is used to recover the missing sampling points in the current sequence. The recovered data sequence is then recombined in chronological order to form a current missing filling state. Subsequently, a fast Fourier transform algorithm is used to convert the current missing filling state into frequency domain conversion, transforming the time domain current signal into spectral domain amplitude distribution data. A current state spectrum diagram is output within the spectral range of 0Hz to 2000Hz to reflect the energy distribution characteristics of the current signal.

[0010] The specific process for restoring the point values ​​of missing sampling points in a current sequence and then recombining the restored data sequence in temporal order to form a current missing filling state includes: First, the continuously acquired three-phase motor current signal is discretized according to the sampling period Δt to form a discrete current sequence. ,in This represents the motor current value corresponding to the nth sampling point in a discrete current sequence indexed by sampling point number n. It can also be understood as the representation of the current signal in the discrete time domain, where n is the sampling point index. Δt is the theoretical sampling time interval, taken as 0.5ms. It represents the time position corresponding to the nth sampling point in the discrete sampling sequence. It is not a continuous variable, but a discrete time marker determined by the sampling frequency. To recover the current amplitude at a given time, when some sampling points are missing due to sensor packet loss or noise shielding, a cubic spline interpolation model is constructed. The core interpolation function can be expressed as a piecewise cubic polynomial: ,in For the first Cubic spline interpolation function over a time interval and This represents the timestamps corresponding to two adjacent valid sampling points. , , , This represents the coefficient parameters of the spline function for the i-th segment. For any time point to be interpolated within the interval; and by applying a function continuity condition. The continuity condition of the first derivative and the continuity condition of the second derivative At the same time, combined with the natural boundary constraint S′′( )=0,S′′( If ) = 0, solve for the spline coefficients corresponding to each interval. , , , To ensure the smoothness and physical consistency of the interpolation curve across the entire time axis; based on this, the missing sampling points are evaluated using the interpolation function to obtain the current recovery value at the missing location. ,in Indicates missing sampling points The current value obtained at the point of recovery; The index represents the missing sampling point, used to locate the specific location of the missing data in the time series; This represents the discrete time corresponding to the k-th sampling point. In summary, the engineering meaning of the above formula can be expressed as: when the motor current data at the k-th sampling time is missing, its time position can be determined... and the cubic spline interval function containing that time point. The current recovery value at that moment is obtained by performing an evaluation on the above. This enables continuous reconstruction of the current sequence, providing stable and complete input data for subsequent spectrum analysis; Among them, the continuity condition of the function Used to constrain cubic spline functions within adjacent time intervals at interval connection points. The function values ​​are the same at each interval, ensuring that the current amplitude calculated from the previous and next intervals remains consistent at that moment. This avoids sudden changes or discontinuous jumps in the current amplitude in the reconstructed current waveform, thus ensuring the amplitude continuity of the recovered current sequence in the time domain; first derivative continuity condition. This is used to constrain the rate of change of current in adjacent intervals to remain consistent at the connection point. In a physical sense, this derivative corresponds to the slope of the current change with time. This condition can prevent the interpolation curve from having a broken corner at the connection point and ensure that the dynamic change trend of the recovery current matches the actual motor current response. Continuity condition of the second derivative This is used to further constrain the curvature continuity of adjacent intervals at the connection point. This condition corresponds to the smooth transition of the current change acceleration, so that the interpolated current waveform still maintains a smooth change under strong dynamic conditions such as sudden stop and start, and avoids the generation of non-physical high-frequency oscillation components. The above three types of continuity constraints work together to ensure that the current sequence recovered based on cubic spline interpolation meets the engineering physical consistency requirements in terms of amplitude, change trend and change smoothness, providing a continuous, stable and abrupt artifact-free input signal for subsequent spectrum analysis.

[0011] Step S2: Analyze the current divergence intensity based on the current state spectrum diagram; simulate the output torque performance attenuation ratio in the time dimension based on the current divergence intensity to obtain the output torque performance attenuation ratio. In this embodiment of the invention, the amplitude growth curve of the current rising phase is extracted from the obtained current state spectrum diagram within the same time window. The energy density sequence within the amplitude-frequency variation interval is subjected to discrete difference operation to calculate the rate of change of energy growth rate in the frequency rising segment. The variance analysis method of the difference sequence is used to obtain the variance of the spectrum energy growth rate. The current divergence intensity is quantified by multiplying the variance value by the frequency growth rate ratio. After obtaining the current divergence intensity data, the current divergence intensity is matched and interpolated to the time point of the torque output experiment based on the time axis. Linear fitting is performed at each time point to generate the torque attenuation in the form of a time series. The torque output change trend in the time dimension is simulated by cumulative integration, and the output torque performance attenuation ratio is calculated.

[0012] Step S3: Evaluate the motor performance mechanism based on the output torque performance attenuation ratio to output motor performance mechanism data; In this embodiment of the invention, the obtained output torque performance attenuation ratio is used to perform feature learning on the motor performance data throughout the entire experimental cycle. A statistical analysis method based on the K-means clustering algorithm is used to calculate the Euclidean distance in the time dimension and experimental cycle dimension with the attenuation ratio as the main feature vector. Several feature clusters are formed by minimizing the distance, and the torque performance attenuation ratio feature clustering data is obtained. Then, the feature mean of different groups is mapped to the actual torque output time curve of the motor for analysis. The motor performance mechanism correlation curve is generated based on the difference between clusters. The data distribution law corresponding to motor thermal attenuation, electromagnetic loss and mechanical inertia is extracted from the curve, and the motor performance mechanism data is output in quantitative form.

[0013] The experimental conditions for collecting data on the emergency stop and start of new energy vehicles refer to the following: after the new energy vehicle motor stops within the range of 120km / h to 140km / h, the new energy motor is quickly started within 1 second and continuously accelerated to 140km / h, and then this is continued for 5 seconds. This process is repeated 8 times.

[0014] refer to Figure 2The aforementioned step S2 includes the following steps: Step S21: Extract the current-constantly-rising state waveform from the current state spectrum diagram; In this embodiment of the invention, the obtained current state spectrum is divided into equally spaced partitions within the frequency range of 0Hz to 2000Hz according to a frequency resolution of 2Hz. The amplitude sequence within each partition is subjected to amplitude equalization to eliminate the influence of abrupt noise. In the equalized spectrum, a threshold determination method is used to find the frequency band intervals with continuously monotonically increasing amplitudes. The monotonically increasing condition is defined as the amplitude increase within 5 consecutive sampling points being greater than 2% of the amplitude of the previous sampling point. The continuous frequency bands that meet the condition are extracted as the current continuously rising state waveform. After extraction, the data within the adjacent 5-point window is smoothed using the moving average method to obtain a smooth rising waveform sequence. The waveform formed by this sequence with frequency as the horizontal axis and amplitude as the vertical axis is the state waveform data, which is used for subsequent divergence calculations.

[0015] In another embodiment, firstly, based on the current state spectrum obtained in step S1, the time range of each emergency start process is precisely divided in the spectrum with time as the horizontal axis, frequency as the vertical axis, and current amplitude as the color intensity. The start and end times of the emergency start are determined by the timestamps recorded by the emergency start control commands. All time frame spectrum data within this time interval are extracted to form an emergency start spectrum sub-graph. The frequency bandwidth range corresponding to the fundamental frequency of the motor's three-phase current is determined in the emergency start spectrum sub-graph. The frequency range within ±10% of the fundamental frequency is taken as the fundamental frequency bandwidth. The amplitudes of each time frame within this bandwidth are arranged in chronological order to obtain a discrete sequence of current amplitude changes with time within the fundamental frequency bandwidth. This discrete sequence is then processed. A first-order difference operation is performed, and continuous time periods with difference results greater than 0 are marked. Time periods with a continuous length of not less than 20 time frames and all difference results are non-negative are defined as current continuous rising intervals. Within each continuous rising interval, the amplitude at the fundamental frequency is used as the main reference value, and the amplitudes at the first three lower harmonic frequencies are read to form a multi-dimensional amplitude time series. These amplitude time series are uniformly marked as state waveforms with continuous current rising. During the state waveform extraction process, a moving average operation with a moving average window length of 5 time frames is used for the amplitude of each time frame to reduce the influence of random fluctuations. After extraction, a set of state waveform data covering all emergency start cycles and all continuous rising time periods is obtained.

[0016] Step S22: Analyze the current divergence intensity based on the stated state waveform; In this embodiment of the invention, the rate of change of amplitude from zero to maximum point within each time slice is extracted from the state waveform sequence obtained in step S21. The rise rate between adjacent sampling frequency points is calculated using the discrete time difference method, and the standard deviation between adjacent rates is calculated using the rate change sequence to obtain the rise rate variance. Subsequently, the rate sequence and amplitude sequence are multiplied to obtain power spectral density distribution data. The power increment factor is calculated using the cumulative difference value of the power spectral density distribution data. The current divergence intensity is calculated by multiplying the rate of change and the power increment factor. The divergence intensity of each frequency band is arranged in time order to form a time divergence curve, and the current divergence intensity data for thermal demagnetization calculation is output.

[0017] In another embodiment, after obtaining the state waveform of continuously rising current, each state waveform is considered as a multi-dimensional time series, where each time point contains amplitude data of the fundamental frequency and three lower harmonic frequencies. First, time normalization is performed on each state waveform, scaling the time index to the interval between 0 and 1, and dividing the amplitude of each frequency component by the maximum amplitude of that waveform to obtain an amplitude-normalized state waveform. A first-order difference operation is performed on the normalized state waveform to calculate the amplitude change between adjacent time points. The amplitude change at each time point is divided by the corresponding time interval length to obtain the amplitude change rate sequence of each frequency component. The mean and variance of the amplitude change rate sequence of each frequency component are calculated; the mean represents the overall rising speed, and the variance represents the degree of rising fluctuation. Then, a frequency-weighted superposition method is used for the amplitude change rates of all frequency components, with weights allocated according to the reciprocal of the frequency, maximizing the weight of the fundamental frequency. The harmonic frequency weights decrease sequentially, thus obtaining the comprehensive amplitude change rate at each time point. The comprehensive amplitude change rate sequence is recorded as the current rise rate benchmark data. Based on this benchmark data, a current divergence intensity index is further constructed. For each time point, the proportion of each frequency component amplitude to the total amplitude is calculated. These proportions are differiated from the proportions at the initial time. The difference result is the energy redistribution of each frequency component. The energy increment of higher harmonic frequency components and the energy decrement of fundamental frequency components are then weighted and synthesized to obtain the quantized value of current energy transfer from low frequency to high frequency. This quantized value is multiplied by the comprehensive amplitude change rate to form a current divergence intensity value that reflects the combined effect of amplitude growth rate and spectral diffusion. This value is calculated for all time points within each state waveform. The obtained current divergence intensities are arranged in chronological order to form a current divergence intensity sequence, providing input data for subsequent thermal demagnetization weakening gradient estimation.

[0018] Step S23: Estimate the thermal demagnetization weakening gradient of the motor permanent magnet based on the current divergence intensity, and then perform weakening deduction in the time dimension to obtain the weakening evolution data of the motor permanent magnet; In this embodiment of the invention, the thermal energy increment index in the stator winding of the motor is calculated based on the current divergence intensity data. The energy accumulation integration method is used to integrate the divergence intensity over the time axis to generate a thermal energy increment sequence. The temperature rise rate is then fitted to the thermal energy increment sequence using a nonlinear least squares method to obtain thermal energy temperature rise rate data. Subsequently, the basic electromagnetic parameters of the motor's permanent magnets, including basic coercivity parameters and basic remanence characteristics, are obtained. The thermal energy increment index and thermal energy temperature rise rate are input into the coercivity attenuation calculation process. The coercivity attenuation with temperature rise is simulated using a linear decreasing coefficient method to obtain coercivity attenuation data. Simultaneously, a proportional... The scaling method applies the thermal energy increment index to the basic remanent magnetization characteristics to generate magnetic flux density reduction data. The coercivity decay data and the magnetic flux density reduction data are merged and summed to obtain electromagnetic force reduction data. Based on this reduction data, the thermal demagnetization weakening gradient of the motor permanent magnet is calculated. Then, the gradient data is divided into time series and a weakening time series sample is established with a time interval of 1 second. The inflection point of the weakening cycle is analyzed by the least squares trend fitting method to determine the range of weakening cycle change. The weakening acceleration relationship is derived by using the trend change slope. The weakening evolution data of the motor permanent magnet is calculated by accumulating over time.

[0019] In another embodiment, after obtaining the current divergence intensity sequence, the current divergence intensity in each emergency start cycle is first integrated to calculate the cumulative divergence from the start of the emergency start to any time point. The cumulative divergence is then linearly mapped to the empirical coefficients of copper and iron losses in the motor stator windings to obtain the thermal energy increment index at that time point. The thermal energy increment index is then subtracted from time to obtain the rate of change of thermal energy increment over time. A nonlinear temperature rise rate function is then constructed based on the thermal energy increment index and its rate of change. The heat capacity, thermal conductivity, and heat dissipation area parameters of the motor stator and rotor materials are introduced through a lookup table to correlate the thermal energy increment with temperature change, obtaining the temperature rise rate data of the motor's internal temperature over time. Based on this temperature rise rate data, the basic electromagnetic parameters of the motor's permanent magnet material are obtained, including the basic parameters of coercivity and remanence density at room temperature. The temperature-time curve is input into the permanent magnet parameter degradation relationship, and the temperature range is divided into segments using a linear segmentation method. Within several small intervals, the coercivity and remanence density are gradually attenuated according to the known slope of temperature, yielding coercivity attenuation data and magnetic flux density reduction data. For each time point, the corresponding coercivity attenuation value and magnetic flux density reduction value are calculated. Then, combining the stator slot number, pole pair number, and air gap length parameters of the motor, and utilizing the relationship between electromagnetic force and magnetic flux density, a reduction analysis of the electromagnetic force at each time point is performed, calculating the electromagnetic force reduction data. This reduction data is normalized on the time axis to obtain the thermal demagnetization weakening gradient of the motor's permanent magnet. Using this thermal demagnetization weakening gradient as a time function, and employing a discrete-time progression method with a time step of 1ms, starting from the start of the emergency start, the equivalent magnetic properties of the permanent magnet are updated at each time step according to the current weakening gradient, and the updated magnetic property parameters are recorded, forming the weakening evolution data of the motor's permanent magnet. This weakening evolution data covers the entire emergency stop and start test process, reflecting the gradual weakening process of the permanent magnet's magnetic properties in the time dimension.

[0020] Step S24: Simulate the output torque performance attenuation ratio in the time dimension based on the weakened evolution data to obtain the output torque performance attenuation ratio.

[0021] In this embodiment of the invention, based on the weakening evolution data obtained in step S23, the rate of change of the air gap magnetic field amplitude is calculated on the time coordinate with a time step of 1 second. The gradual weakening data of the air gap magnetic field is obtained through differential operation. The magnetic field amplitude weakening data is substituted into the back electromotive force calculation formula and the back electromotive force reduction index is calculated using the proportional reduction method. Then, the back electromotive force reduction index and the gradual weakening data of the air gap magnetic field are correlated to obtain the degree of limitation of the field weakening propagation speed. Subsequently, the back electromotive force reduction index, the air gap magnetic field weakening data and the degree of limitation of the field weakening propagation speed are subjected to time superposition analysis. The torque output change in each time period is calculated by integral summation. Finally, the output torque performance attenuation ratio is simulated in the time dimension to obtain the output torque performance attenuation ratio data.

[0022] In another embodiment, after obtaining the weakening evolution data, the instantaneous values ​​of the equivalent magnetic flux density and coercivity of the permanent magnet are first read at each time step. These instantaneous values ​​are normalized with the reference magnetic flux density and reference coercivity at the initial moment to obtain the equivalent weakening coefficient of the air gap magnetic field and the demagnetization resistance coefficient of the magnetic circuit. The weakening coefficient is introduced into the motor magnetic circuit calculation. Under the condition of known stator winding turns, root mean square value of phase current and number of pole pairs, the air gap magnetic flux density weakening coefficient is proportionally correlated with the theoretical back electromotive force. At each time step, the reduction ratio of the back electromotive force relative to the initial state is calculated. This reduction ratio is defined as the back electromotive force reduction index. The back electromotive force reduction index is compared with the target back electromotive force of the field weakening control in the motor control strategy to obtain the degree of field weakening expansion limitation. By comparing the difference between the current back electromotive force and the maximum allowable back electromotive force, the field weakening control retention is determined at each time step. For different margins, the time intervals during which the margin gradually decreases are recorded as the field weakening and speed expansion restriction intervals. Within this interval, the effective electromagnetic torque output of the motor in the high-speed range is reduced according to the degree of restriction. The data on the gradual weakening of the air gap magnetic field and the degree of field weakening and speed expansion restriction are input into the electromagnetic torque calculation relationship. The instantaneous output torque value is calculated for each time step, and the theoretical output torque before thermal demagnetization is used as the reference torque. The instantaneous output torque of each time step is compared with the reference torque to obtain the output torque performance attenuation ratio in the time dimension. During the attenuation ratio calculation process, the ratios of each time step are archived and sorted according to the emergency stop and start cycle number, so that each cycle corresponds to a complete attenuation ratio time sequence. Finally, output torque performance attenuation ratio data covering all emergency stop and start cycles and indexed by time is formed, providing input data for subsequent motor performance mechanism evaluation steps.

[0023] Step S22 includes: Calculate the rate of change of current from zero to peak value based on the state waveform; Calculate the variance of the rising slope of the state waveform based on the rate of change of velocity; The current power spectral density is fitted based on the variance of the rising slope and the rate of change of velocity to obtain the power spectral density during the current rising phase, and then the power increase factor of the power spectral density is calculated. The current divergence intensity is analyzed based on the rate of change of velocity and the power increase factor.

[0024] In this embodiment of the invention, discrete point data of the current amplitude over time are extracted from the state waveform sequence of continuously rising current with a sampling point interval of Δt=5ms. The starting point is defined as the first effective sampling point with an amplitude close to zero, and the ending point is defined as the peak value of the current rising segment. The instantaneous current change rate is calculated by dividing the amplitude difference between adjacent sampling points by the time interval. All instantaneous change rates are arranged in chronological order to form a velocity change rate sequence. In the velocity change rate sequence, the mean smoothing of 5 consecutive sampling points is performed using the sliding window method to eliminate high-frequency jitter interference, resulting in smooth velocity change rate data. This calculation process is repeated throughout the continuous rising phase to cover the entire time segment of the state waveform. Finally, the velocity change rate sequence data of the current from zero to peak value is derived for the calculation of the rising slope variance.

[0025] A frequency domain mapping relationship is established based on the velocity change rate data and the rise slope variance data. First, the velocity change rate sequence is subjected to a fast Fourier transform according to the time sampling interval to obtain the frequency domain amplitude distribution. Then, the rise slope variance value is used to perform exponential weighted smoothing on the frequency domain amplitude to reduce the influence of local spikes. The smoothed frequency domain result is defined as the power spectral density data of the current rising stage. After obtaining the power spectral density data, the energy ratio of adjacent frequency components is calculated by point-by-point difference. The cumulative ratio of each frequency is compared with the basic energy density in the same frequency domain to obtain the power increment series, which describes the energy growth relationship of the power spectral density with frequency. This series is the power increment data.

[0026] Under the time correspondence, the obtained velocity change rate sequence and power increase factor sequence are synchronized and aligned. The velocity change rate and the corresponding power increase factor at each time point are multiplied to form the energy growth rate sequence. The cumulative value of current divergence intensity is obtained by integrating and summing the energy growth rate sequence throughout the rising phase. Then, each cumulative value of divergence intensity is mapped to its corresponding time coordinate using linear interpolation to form the current divergence intensity time series. Finally, the current divergence intensity data is output. This data is used to reflect the degree of energy instability of the current signal during the rising process and to provide quantitative input for subsequent thermal demagnetization weakening gradient estimation.

[0027] It is important to note that by performing discrete-time difference calculations on the amplitude change of the current from zero to peak value in a continuously rising current waveform, the instantaneous rate of change of the current signal is quantified in the form of a rate of change, thus describing the dynamic rising characteristics of the current signal in a time-continuous manner. This rate of change characterizes the speed of the current response and the dynamic gradient characteristics of the energy input during the rising process. The discrete fluctuation amplitude within the time series is analyzed using the rate of change sequence. The stability and random fluctuation of the current rate of change are reflected by calculating the variance of the rising slope. A large variance of the rising slope indicates strong non-uniformity and instability in the current rising process; therefore, this variance becomes an important parameter for judging the energy disturbance distribution within the waveform. The rate of change and the variance of the rising slope are then transformed and fused in the frequency domain. The power spectral density of the current rising stage is obtained by smoothing and weighting the energy distribution in the frequency space. The power increment factor is then calculated based on the power spectral density change characteristics. The calculation of the power increment factor reflects the progressive growth law of current energy in the frequency dimension. This process transforms the time-domain waveform characteristics into frequency-domain energy characteristics. Based on the synchronous calculation of the relationship between the rate of change of velocity and the power increase factor over time, the energy divergence trend during the current rise process is quantified by the time integration of the energy growth rate sequence to form the current divergence intensity. The current divergence intensity is used to describe the degree of imbalance between energy accumulation and release in the current waveform throughout the entire rise phase.

[0028] The logical relationship between these steps is as follows: by establishing a speed index for the dynamic change of current, the statistical characteristics of the speed change fluctuation are extracted to form the rising slope variance. The results of the first two steps are used to extend the analysis from the time domain to the frequency domain to reveal the energy distribution law and calculate the power increase factor. Then, the speed change and energy growth characteristics are integrated and deduced in the time dimension to finally obtain the current divergence intensity. The four steps constitute a closed-loop logical chain from dynamic change in the time domain to energy analysis in the frequency domain and then back to divergence quantification in the time dimension.

[0029] Step S23, which estimates the thermal demagnetization weakening gradient of the motor permanent magnet based on the current divergence intensity, includes: The thermal energy increment index is evaluated based on the current divergence intensity to obtain the thermal energy increment index; the thermal energy increment index is fitted with a nonlinear temperature rise rate to output the thermal energy temperature rise rate. Obtain the basic electromagnetic parameters of the permanent magnet of the motor, including basic coercivity parameters and basic remanence characteristics; Based on the thermal energy increment index and thermal energy temperature rise rate, the coercivity fundamental parameter in the basic electromagnetic parameters is simulated to obtain coercivity decay data. Based on the thermal energy increment index and thermal energy temperature rise rate, the basic remanent magnetization characteristics in the basic electromagnetic parameters are simulated by a proportional magnetic induction intensity decrease, and magnetic induction intensity decrease data is obtained. Electromagnetic force reduction analysis was performed based on coercivity attenuation data and magnetic flux density decrease data to obtain electromagnetic force reduction data. The thermal demagnetization weakening gradient of the permanent magnet in the motor is estimated based on the electromagnetic force reduction data.

[0030] In this embodiment of the invention, after obtaining the current divergence intensity time series, it is divided into equally spaced 1-second time windows on the time axis. The divergence intensity data within each time window is numerically integrated to obtain the energy accumulation per unit time. The ratio of the energy accumulation to the width of the time window is defined as the thermal energy increment rate. The thermal energy increment rate of each time window is standardized to obtain the thermal energy increment index. In order to determine the temperature response of the permanent magnet under the thermal energy input condition, the thermal energy increment index sequence is input into the nonlinear least squares fitting process and a third-order polynomial function is used for curve fitting. By comparing the fitting residuals and minimizing them, the fitting coefficients are calculated and the instantaneous temperature rise rate corresponding to the time is derived. The temperature rise rate data is output as the thermal energy temperature rise rate data. All calculations are performed in a continuous time series manner to maintain the temporal consistency of thermal energy changes.

[0031] Before the experiment, the room temperature magnetization curve of the permanent magnet of the motor was measured using a magnetic performance testing device. The initial coercivity value Hc0 and remanence Br0 were extracted from the hysteresis loop. Hc0 and Br0 were used as representative values ​​of the basic electromagnetic parameters. At the same time, the magnetic performance stability state at an ambient temperature of 25℃ was recorded. The basic parameters were then compared and corrected with the electromagnetic constants specified in the motor design specifications to confirm the accuracy of the basic coercivity parameters and the basic remanence characteristics. The parameters obtained in this stage were used as the initial input data for the subsequent thermal demagnetization weakening gradient calculation.

[0032] Based on the obtained thermal energy increment index and thermal energy temperature rise rate, the thermal energy increment index is multiplied by the time weighting factor corresponding to the temperature rise rate to obtain the energy temperature rise weighted value. This weighted value is then multiplied with the basic coercivity parameter to perform a decay operation to simulate the decay process of coercivity as the temperature rises. Specifically, the operation involves cumulative calculation on the thermal energy increment sequence with a time step of 1 second. The coercivity decay data is kept continuous in the time dimension by using linear interpolation to generate coercivity decay data. This data has a time axis correspondence for subsequent electromagnetic force analysis.

[0033] Based on the obtained thermal energy increment index and thermal energy temperature rise rate, the magnetic induction intensity decrease of the basic remanent magnetization characteristics is calculated proportionally. The thermal energy increment index and the basic remanent magnetization intensity Br0 are proportionally calculated to obtain the remanent magnetization decrease rate. Then, the temperature rise rate and the remanent magnetization decrease rate are weighted and averaged to generate a time-continuous magnetic induction intensity decrease sequence. The magnetic induction intensity decrease sequence is smoothed by the piecewise difference method to remove high-frequency noise, and the magnetic induction intensity decrease data is output. This data reflects the change in magnetic field strength of the permanent magnet under continuous temperature rise.

[0034] The coercivity attenuation data and the magnetic flux density decrease data were matched one-to-one under the time correspondence. The coercivity value at each time point was multiplied by the magnetic flux density value at the corresponding time point to obtain the instantaneous value sequence of electromagnetic force. The instantaneous value sequence was accumulated and integrated to obtain the total change of electromagnetic force over the entire experimental period. Then, the rate of change of electromagnetic force with time was calculated by differential calculation. The standard deviation of the rate of change was used as an indicator of the degree of electromagnetic force reduction to obtain electromagnetic force reduction data. This data is used to reflect the weakening trend of electromagnetic coupling ability of the permanent magnet of the motor under the influence of heat.

[0035] The calculated electromagnetic force reduction data is normalized in chronological order to limit its value range to between 0 and 1. Then, the rate of change of the reduction data per unit time is calculated, and the derivative of the rate of change is used as the thermal demagnetization weakening gradient of the permanent magnet of the motor. The gradient sequence is divided into time partitions with 1-second intervals to establish a gradient change time series. The continuous variation law of the gradient is analyzed by the moving average method and local discrete anomalies are removed, thus obtaining the thermal demagnetization weakening gradient data of the permanent magnet. This data serves as the core parameter reflecting the dynamics of the motor's magnetic field decay and is used in the subsequent weakening deduction process.

[0036] It is important to note that by integrating the current divergence intensity over time and calculating the energy density, a quantitative relationship for the transfer of current energy to heat energy is established. This allows for the calculation of the degree of heat energy accumulation per unit time, which is then standardized into a heat energy increment index. The rate of temperature change with energy accumulation is then obtained by nonlinear fitting of this index, thus yielding the heat energy temperature rise rate. This rate is used to quantitatively describe the response characteristics between the internal heat energy input and temperature rise of the motor.

[0037] The energy temperature rise weighted value is calculated by using the multiplicative relationship between the thermal energy increment index and the thermal energy temperature rise rate. The weighted result is then used to perform time series decay calculation with the basic coercivity parameter to generate coercivity decay data. This decay data represents the decreasing trend of magnetization maintenance ability of the internal microstructure of the permanent magnet after being affected by temperature, and is used to characterize the change law of anti-demagnetization ability during thermal degradation.

[0038] The thermal energy increment index and thermal energy temperature rise rate are applied to the basic remanent magnetization characteristics to generate magnetic induction intensity decrease data in a proportionally decreasing form. This data is used to reflect the degree of reduction in magnetization intensity of permanent magnets under thermal disturbance and is an important time series parameter describing the magnetic flux decay law. The physical consistency of the remanent magnetization change process is ensured by the principle of proportional decrease.

[0039] Electromagnetic coupling calculations are performed on the same time coordinate with coercivity decay data and magnetic flux density decrease data to obtain the trend of electromagnetic force change. The instantaneous change of electromagnetic force is obtained by using differential and integral methods, and electromagnetic force reduction data is defined as a metric parameter for the overall electromagnetic performance degradation of permanent magnet over time. This step forms a direct mapping between thermal demagnetization process and magnetic field force change.

[0040] The electromagnetic force reduction data were normalized and the time derivative was calculated. The rate of change of the derivative was used to characterize the thermal demagnetization weakening gradient of the permanent magnet. The gradient results were organized into a continuous time sequence and the weakening rate accumulated over time was analyzed. This weakening gradient was used to represent the dynamic change intensity of the motor magnetic field decay and marked the level of the continuous influence of thermal decay on the magnetic properties of the permanent magnet.

[0041] The logical relationship between these steps is as follows: starting from the divergence intensity, a correspondence between thermal energy and time energy accumulation is established to form the basis of thermal energy input, providing the electromagnetic baseline of the permanent magnet in the un-attenuated state; the coercive thermal attenuation trend is calculated using thermal energy data; the remanence decrease is proportionally mapped based on the same energy characteristics; and the electromagnetic force change law is formed by integrating the first two attenuation amounts. Then, the temporal change of the electromagnetic force is further transformed into a quantitative weakening gradient, realizing a continuous physical logic chain from energy accumulation to thermal attenuation and then to the degree of magnetic weakening.

[0042] Step S23 involves performing a weakening deduction over time, which includes: The thermal demagnetization weakening gradient of the permanent magnet of the motor is divided into time series using a preset timestamp to obtain the thermal demagnetization weakening time series. The weakening trend periodic inflection point was analyzed by performing a weakening trend periodic inflection point analysis on the thermal demagnetization weakening time series; Based on the weakening periodic inflection point, the weakening acceleration relationship is summarized in the thermal demagnetization weakening time series to output the weakening acceleration relationship. Based on the weakening acceleration relationship, the gradual weakening state in the time dimension is predicted, and then the weakening evolution data of the permanent magnet of the motor is obtained by performing weakening deduction in the time dimension.

[0043] In this embodiment of the invention, the obtained thermal demagnetization weakening gradient data of the permanent magnet of the motor is set with a time starting point of t0 and a time stamp sequence is generated at a fixed time interval Δt=1s. Each time stamp is bound to its corresponding weakening gradient value to form a time data pair. The gradient values ​​are arranged according to the time sequence to construct a weakening gradient time curve to ensure a strict correspondence between the gradient data in the time dimension. Then, the gradient change amplitude in adjacent time periods is differentially calculated to obtain the gradient change rate in the time period. The gradient change rate sequence is then appended to the original gradient time sequence to form an extended thermal demagnetization weakening time sequence. This time sequence contains three sets of data: time stamp, weakening gradient, and gradient change rate, which facilitates the maintenance of time continuity and numerical controllability during subsequent periodic analysis.

[0044] A sliding window approach was used to detect trends in the thermal demagnetization weakening time series to determine the periodic inflection points of the weakening trend. The second-order difference of the gradient change rate was calculated within a 10-second sliding window, and the point where the sign of the second-order difference changed was defined as a local trend inflection point. Then, the periodic weakening behavior was identified by statistically analyzing the time intervals between adjacent inflection points over the entire time series. The moving average method was further used to filter local inflection points, and inflection points with a change amplitude of less than 20% of the global mean within five adjacent time periods were removed to eliminate pseudo-periods. The set of retained inflection points obtained by this method is the weakening periodic inflection point data. As the experiment progressed, each inflection point marked a stage of magnetic property inflection. Finally, the weakening periodic inflection point data was output for subsequent acceleration relationship induction.

[0045] The obtained weakening periodic inflection point data are remapped to the thermal demagnetization weakening time series. The ratio of the weakening gradient change rate to the time interval length is calculated between two adjacent inflection points to obtain the local weakening rate series. Regression analysis is performed on all local weakening rate series, and the slope of the linear fit between the rate and the time interval length is calculated by the least squares method. This slope is defined as the weakening acceleration factor. Then, a weakening acceleration factor time series is established in chronological order. The fluctuation of the acceleration factor is smoothed by the moving average method to generate a continuous weakening acceleration curve. This curve is output as the weakening acceleration relationship, which is used to describe the gradual increase in the gradient change rate caused by the thermal demagnetization process over time.

[0046] Based on the output weakening acceleration relationship, the weakening gradient is extrapolated and predicted in the time dimension. The weakening acceleration curve is matched with the thermal demagnetization weakening time series, and the weakening gradient change trend in the future time window is calculated by time interpolation. The predicted gradient values ​​of each time period are superimposed sequentially to form a progressive weakening state sequence. This sequence constructs a continuous weakening process curve with time as the horizontal axis and gradient value as the vertical axis. In order to ensure the stability of the extrapolation, the error is gradually accumulated by moving integral in each prediction interval to form a complete weakening evolution sequence within the time range. The sequence data is converted into a standardized form and output as the weakening evolution data of the motor permanent magnet. This data describes the dynamic change process of the magnetic properties gradually decaying over time.

[0047] It is important to note that the analysis of the periodic inflection point of the weakening trend refers to identifying the data fluctuation trend of the established thermal demagnetization weakening time series, detecting the inflection point position by calculating the second difference value of the gradient change rate, thereby identifying the periodic turning point characteristics of the thermal demagnetization gradient on the time scale, and further screening out the main periodic inflection points that reflect the changes in the actual magnetic properties. This step defines the stage change nodes in the weakening process and is an important process for distinguishing between continuous degradation and stage jumps.

[0048] The weakening acceleration relationship induction refers to calculating the rate of change of the weakening gradient in adjacent intervals with the periodic inflection point as a reference, obtaining the acceleration factor reflecting the increasing degree of the weakening trend through linear regression, and then establishing a continuous weakening acceleration curve, so that the rate of change of the time series is transformed from a discrete state to a continuous dynamic relationship. This step is used to quantify the acceleration effect of gradient change over time in the thermal demagnetization process, and is a key link from trend discovery to trend expression.

[0049] Finally, the gradual weakening state in the time dimension is predicted. The subsequent weakening extrapolation in the time dimension refers to extrapolating the gradual change of magnetic properties in the future time period based on the weakening acceleration curve. The predicted value of the weakening gradient at each moment is obtained by calculating the time integral of the acceleration relationship, and a weakening evolution curve that accumulates over time is formed. This curve reflects the time process of magnetic property decay of permanent magnets under continuous thermal stress. This step defines the dynamic extrapolation process of thermal demagnetization and is the final stage for realizing the calculation of the time evolution of magnetic properties.

[0050] Step S24 includes: Based on the weakening evolution data, the gradual weakening of the air gap magnetic field in the time dimension is deduced, and the gradual weakening data of the air gap magnetic field is obtained. The back electromotive force reduction index is estimated based on the data of gradual weakening of the air gap magnetic field. The degree of limitation on the weak magnetic spread rate can be inferred from the back electromotive force reduction index and the data on the gradual weakening of the air gap magnetic field. The output torque performance attenuation ratio is simulated over time based on the back EMF reduction exponent, the gradual weakening data of the air gap magnetic field, and the degree of limitation of the weak magnetic spread rate, in order to obtain the output torque performance attenuation ratio.

[0051] In this embodiment of the invention, based on the output weakening evolution data, the thermal demagnetization weakening gradient value corresponding to each time node is multiplied point-to-point with the initial value of the motor stator magnetic flux density to obtain the change ratio of the air gap magnetic field at different times. The instantaneous amplitude of the air gap magnetic field is obtained by multiplying this magnetic field ratio with the motor air gap length and the permeability constant of the permanent magnet. The rate of decrease of the air gap magnetic field amplitude between consecutive time points is calculated using the difference method throughout the entire time series, thereby forming a sequence curve of the air gap magnetic field decreasing with time. Then, the moving average filter is applied to the sequence to reduce sampling jitter and maintain trend continuity. The smooth data obtained is the gradual weakening data of the air gap magnetic field. This data records the change process of the air gap magnetic flux with the weakening trend of the permanent magnet at a time step of 1 second, which is used for subsequent back electromotive force estimation.

[0052] The decrease in back electromotive force (EMF) as the magnetic field amplitude decays is calculated using the obtained data on the gradual weakening of the air gap magnetic field. At each time point, the instantaneous amplitude of the air gap magnetic field is compared with the rated magnetic flux constant of the motor design to obtain the normalized flux factor. This flux factor is then multiplied by the corresponding data of the number of turns and speed of the motor winding to obtain the back EMF amplitude. The natural logarithm of the back EMF amplitude is taken on the time series and linearly fitted. The absolute value of the fitting slope is defined as the back EMF reduction exponent. To ensure the accuracy of the calculation, a stepwise regression calculation with a moving fitting window length of 10s is used to obtain a time-continuous back EMF reduction exponent sequence, which reflects the rate characteristic of the decrease in back EMF over time.

[0053] The calculated back EMF reduction index is combined with the data on the gradual weakening of the air gap magnetic field to infer the degree of limitation of field weakening propagation. In each time interval, the ratio of the corresponding back EMF reduction index to the decrease in air gap magnetic field is taken as the parameter input, and the product of the two is calculated to obtain the voltage margin attenuation coefficient. This coefficient reflects the degree of degradation of the field weakening propagation condition of the motor control system. Then, the average value of the change of the voltage margin attenuation coefficient in the continuous time interval is obtained by using the difference method to obtain the trend of the degree of limitation of field weakening propagation in the time dimension. This trend data is output as a sequence of the degree of limitation of field weakening propagation, which is used to describe the limiting trend of output capability under field weakening conditions.

[0054] The calculation is performed using three sets of time series data: back EMF reduction index, asymptotic weakening of the air gap magnetic field, and limitation of field weakening propagation speed. In each time period, the back EMF reduction index is multiplied by the air gap magnetic field weakening value to obtain the flux attenuation rate. This flux attenuation rate is then multiplied by the limitation of field weakening propagation speed to obtain the torque attenuation. The torque attenuation across all time periods is integrated to obtain the cumulative torque loss. The cumulative loss is then compared to the rated torque to calculate the output torque performance attenuation ratio. To ensure time resolution, the integration step size is 1 second, and the integration interval covers the entire experimental period. The calculation results are output as the output torque performance attenuation ratio data, indexed by time, reflecting the torque output variation law of the motor under the thermal demagnetization and field weakening coupling effects.

[0055] It is important to note that: There is a direct electromagnetic energy transfer chain relationship between the back EMF reduction index, the data on the gradual weakening of the air gap magnetic field, and the limitation of the field weakening expansion rate and the motor output torque. The gradual weakening of the air gap magnetic field reflects the continuous decay of the magnetic flux density of the permanent magnet, which reduces the induced magnetic flux in the stator winding of the motor, thereby reducing the basic amount of Lorentz force generated in electromagnetic interaction. The back EMF reduction index reflects the rate of decrease of the armature reaction voltage after the magnetic flux weakens. When the back EMF weakens, the induced component in the drive current decreases, making it impossible for the motor to maintain the original magnetomotive force balance under load. At the same time, the limitation of the field weakening expansion rate reflects the decreased ability of the motor to maintain voltage balance by reducing magnetic flux under high-speed conditions, resulting in a decrease in stator terminal voltage utilization and a weakening of magnetic energy conversion efficiency. All three factors together cause the electromagnetic energy conversion rate of the motor to decrease over time, the effective component of the electromagnetic torque to decrease, and ultimately manifest as a gradual decay of the output torque.

[0056] The back EMF reduction index is calculated by using the coupling relationship between magnetic flux density and rotation frequency based on the decrease of the air gap magnetic field. The back EMF reduction index is obtained by fitting the rate of decay of the back EMF over time. This index quantifies the time decay rate of the motor induced voltage and is an important parameter connecting the change of magnetic field energy and the change of electrical output capability.

[0057] Inferring the degree of limitation in field weakening propagation speed refers to calculating the degree of decrease in field weakening propagation speed capability caused by voltage margin decay, using the back EMF reduction index and air gap magnetic field weakening data as references. By comparing the ratio between the change in back EMF and the weakening of the magnetic field, the degree to which the motor's speed capability is limited under field weakening propagation speed conditions is obtained. This operation defines the functional constraint relationship between the weakened magnetic field and the voltage response, reflecting the control limitation trend of the system in the field weakening region.

[0058] Simulating the output torque performance attenuation ratio over time involves comprehensively calculating three time series: the back electromotive force reduction exponent, the gradual weakening data of the air gap magnetic field, and the limitation of the magnetic weakening propagation speed. Within each time period, the quantitative change of torque with electromagnetic decay is analyzed, and the output torque performance attenuation ratio over time is obtained. This operation defines the quantitative correspondence between thermal weakening, magnetic decay, and torque degradation, and is the execution link in the weakening deduction to realize the transformation of energy change into mechanical output attenuation.

[0059] Step S3 includes the following steps: Step S31: Perform feature learning based on the output torque performance attenuation ratio to obtain the torque performance attenuation ratio feature; Step S32: Perform cluster analysis on the torque performance attenuation ratio characteristics to obtain attenuation ratio characteristic cluster data; Step S33: Evaluate the motor performance mechanism based on the attenuation ratio feature clustering data to output motor performance mechanism data.

[0060] In this embodiment of the invention, the attenuation ratio in the obtained output torque performance attenuation ratio time series data is standardized by using a time interval Δt=1s as the sampling interval. The numerator of the standardization formula is set as the difference between the attenuation ratio at the current time and the attenuation ratio at the initial time, and the denominator is set as the attenuation ratio at the initial time, so that the calculation result is mapped in the interval of 0 to 1, forming a standardized feature sequence in the time dimension. The time derivative of this sequence, i.e., the attenuation rate data, is jointly constructed with the attenuation ratio sequence to construct a two-dimensional feature matrix. Principal component analysis is performed on this matrix to extract the main variance dimension, thereby obtaining the core feature vector representing the torque attenuation mode. This core feature vector is the torque performance attenuation ratio feature. Subsequently, a time series corresponding index is established to maintain the continuity of the data structure.

[0061] Using the torque performance attenuation ratio feature obtained in step S31 as the input dataset, distance measurement is calculated on the feature vectors. Euclidean distance is used to define the similarity between samples in the feature space. All samples are iteratively divided according to the distance matrix. The initial number of center points is set to 5, and the number of iterations is set to 10. In each iteration, the minimum distance between the sample and its center point is calculated and the sample is reassigned. The center point is updated by the mean and the iteration is repeated until the center position deviation of all feature vectors in two consecutive calculations is less than 1%, which is used as the termination condition for cluster stability. The feature mean of each cluster is output to form attenuation ratio feature clustering data. This data contains the clustering results of different attenuation trends in the time series.

[0062] The attenuation ratio feature clustering data obtained in step S32 is compared with the motor operating physical parameters recorded in the experiment. Statistical analysis is performed on the distribution of average attenuation rate, attenuation amplitude, and duration of each cluster. Correspondence is established between these statistics and three types of operating indicators: motor temperature, current fluctuation, and magnetic flux density change. The correlation strength between each group of features is calculated using the Pearson correlation coefficient. Linear regression fitting is performed on the feature group with a correlation coefficient greater than 0.7 to extract the dominant factors affecting torque attenuation. By superimposing the occurrence frequency of the dominant factors with the corresponding time intervals, a time distribution matrix of the motor performance mechanism is generated. The matrix is ​​structured and output as motor performance mechanism data. This data quantitatively reflects the physical mechanism of torque attenuation and the dominant stage of different mechanisms within the operating cycle.

[0063] The present invention also provides a motor performance testing system for performing the motor performance testing method described above, the motor performance testing system comprising: The spectrum conversion module is used to collect the motor current status under the test conditions of emergency stop and start of new energy vehicles, fill in the missing values ​​of the motor current status, and output the current missing filling status; the spectrum conversion of the current missing filling status is performed to obtain the current status spectrum. The attenuation simulation module is used to analyze the current divergence intensity based on the current state spectrum diagram; and to simulate the output torque performance attenuation ratio in the time dimension based on the current divergence intensity, so as to obtain the output torque performance attenuation ratio. The mechanism evaluation module is used to evaluate the motor performance mechanism based on the output torque performance attenuation ratio, and output motor performance mechanism data.

[0064] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method of testing the performance of an electric machine, characterized in that, The method comprises the following steps: Step S1: collecting motor current states under the new energy vehicle emergency stop and emergency start experimental condition, and filling in the missing values of the motor current states, and outputting the current missing filling state; Converting the current missing filling state into a frequency spectrum diagram to obtain a current state frequency spectrum diagram; Step S2: analyzing the current divergence intensity according to the current state frequency spectrum diagram; Simulating the output torque performance decay ratio in the time dimension according to the current divergence intensity to obtain the output torque performance decay ratio; Step S3: evaluating the motor performance mechanism according to the output torque performance decay ratio to output the motor performance mechanism data.

2. The method of claim 1, wherein, The new energy vehicle emergency stop and emergency start experimental condition refers to that the new energy vehicle motor is stopped in the interval of 120km / h to 140km / h, then the new energy motor is started rapidly within 1s and continuously accelerated to 140km / h, and the speed is maintained for 5s, and the above process is repeated for 8 times.

3. The method of claim 1, wherein, Step S2 comprises the following steps: Step S21: extracting the state waveform of the current continuous rise in the current state frequency spectrum diagram; Step S22: analyzing the current divergence intensity according to the state waveform; Step S23: estimating the thermal demagnetization weakening gradient of the motor permanent magnet according to the current divergence intensity, and then performing weakening deduction in the time dimension to obtain the weakening evolution data of the motor permanent magnet; Step S24: simulating the output torque performance decay ratio in the time dimension based on the weakening evolution data to obtain the output torque performance decay ratio.

4. The method of claim 3, wherein, Step S22 comprises: calculating the speed change rate of the current from zero to peak value based on the state waveform; calculating the rising slope variance of the state waveform according to the speed change rate; fitting the current power spectral density according to the rising slope variance and the speed change rate to obtain the power spectral density of the current rising stage, and then calculating the power increment multiple of the power spectral density; analyzing the current divergence intensity according to the speed change rate and the power increment multiple.

5. The method of claim 3, wherein, The estimation of the thermal demagnetization weakening gradient of the motor permanent magnet according to the current divergence intensity in step S23 comprises: evaluating the thermal energy increment index according to the current divergence intensity to obtain the thermal energy increment index; fitting the thermal energy increment index to output the thermal energy temperature rise rate; obtaining the basic electromagnetic parameters of the motor permanent magnet, including the coercive force basic parameter and the basic residual magnetism characteristic; performing coercive force decay simulation on the coercive force basic parameter in the basic electromagnetic parameter according to the thermal energy increment index and the thermal energy temperature rise rate to obtain the coercive force decay data; performing proportional magnetic induction intensity drop simulation on the basic residual magnetism characteristic in the basic electromagnetic parameter based on the thermal energy increment index and the thermal energy temperature rise rate to obtain the magnetic induction intensity drop data; performing electromagnetic force reduction analysis based on the coercive force decay data and the magnetic induction intensity drop data to obtain the electromagnetic force reduction data; estimating the thermal demagnetization weakening gradient of the motor permanent magnet according to the electromagnetic force reduction data.

6. The method of claim 3, wherein, The weakening deduction in the time dimension in step S23 comprises: dividing the thermal demagnetization weakening gradient of the motor permanent magnet into a time sequence using a preset time stamp to obtain a thermal demagnetization weakening time sequence; Performing weakening trend periodicity inflection point analysis on the thermal demagnetization weakening time sequence to obtain weakening periodicity inflection points; Performing weakening acceleration relationship induction on the thermal demagnetization weakening time sequence based on the weakening periodicity inflection points to output weakening acceleration relationships; According to the weakening acceleration relationships, predicting the progressive weakening state in the time dimension, and then performing weakening deduction in the time dimension to obtain weakening evolution data of the motor permanent magnet.

7. The method of claim 6, wherein, Step S24 includes: Performing progressive air gap magnetic field weakening deduction in the time dimension based on the weakening evolution data to obtain air gap magnetic field progressive weakening data; According to the air gap magnetic field progressive weakening data, estimating back electromotive force reduction indexes; According to the back electromotive force reduction indexes and the air gap magnetic field progressive weakening data, inferring the degree of limitation of field weakening speed expansion; According to the back electromotive force reduction indexes, the air gap magnetic field progressive weakening data, and the degree of limitation of field weakening speed expansion, simulating the output torque performance attenuation ratio in the time dimension to obtain the output torque performance attenuation ratio.

8. The method of claim 1, wherein, Step S3 includes the following steps: Step S31: performing feature learning based on the output torque performance attenuation ratio to obtain torque performance attenuation ratio features; Step S32: performing clustering analysis on the torque performance attenuation ratio features to obtain attenuation ratio feature clustering data; Step S33: performing motor performance mechanism evaluation according to the attenuation ratio feature clustering data to output motor performance mechanism data.

9. An electrical machine performance testing system, characterized by, The motor performance test system is used for performing the motor performance test method as claimed in claim 1, and includes: A spectrum conversion module is configured to collect motor current states under a new energy vehicle emergency stop and emergency start test working condition, fill in missing values of the motor current states, output current missing filled states, and convert the current missing filled states into a frequency spectrum diagram to obtain a current state frequency spectrum diagram; An attenuation simulation module is configured to analyze current divergence intensity according to the current state frequency spectrum diagram, simulate an output torque performance attenuation ratio in the time dimension according to the current divergence intensity, and obtain the output torque performance attenuation ratio; A mechanism evaluation module is configured to perform motor performance mechanism evaluation according to the output torque performance attenuation ratio, and output motor performance mechanism data.

Images