Electric vehicle control method and system

Abstract

The invention relates to the technical field of electric vehicle control, in particular to an electric vehicle control method and system. The method comprises the following steps: acquiring background control terminal authority of the electric power intelligent inspection electric vehicle, extracting motor operation state data and analyzing current fluctuation change to obtain working condition conversion current fluctuation data, then performing equivalent fitting of motor permanent magnet demagnetization intensity, and analyzing a proportional relation between the motor permanent magnet demagnetization intensity and motor output torque loss; designing a motor field weakening control strategy by combining the demagnetization intensity and the output torque loss data, and developing motor control firmware based on the strategy; and finally, the firmware is sent to the electric vehicle control terminal, and the electric vehicle control method is executed. The electric vehicle control technology is optimized, so that the electric vehicle control technology is more perfect.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle control, and particularly to an electric vehicle control method and system. Background Art

[0002] Electric vehicles for power intelligent inspection often operate in places with relatively harsh road conditions. The motor needs to frequently switch between working conditions such as acceleration, deceleration, high torque, and high-speed constant power. The operating state and control strategy of the electric vehicle's motor have a direct impact on its power performance, endurance ability, and battery life. In order to improve the intelligence level of electric vehicles, researchers have begun to focus on how to improve the overall performance of electric vehicles by optimizing the motor control strategy. Permanent magnet synchronous motors are widely used due to their high efficiency and high reliability. The demagnetization phenomenon of permanent magnets will cause a loss of the motor output torque during long-term operation, thereby affecting the power performance of electric vehicles. Therefore, the control of permanent magnet demagnetization has become a major challenge in the motor control system. Researchers have begun to explore how to adjust the motor control strategy in real time based on the motor operating state data to reduce the impact of permanent magnet demagnetization on the power output of electric vehicles. However, there is a problem in a traditional electric vehicle control method that the analysis of permanent magnet demagnetization and output torque loss of the motor is inaccurate, resulting in a large motor control error. Summary of the Invention

[0003] Based on this, it is necessary to provide an electric vehicle control method and system to solve at least one of the above technical problems.

[0004] To achieve the above object, an electric vehicle control method, the method includes the following steps: Step S1: Obtain the background control terminal permission of the electric vehicle for power intelligent inspection; extract the motor operating state between complex road conditions of the electric vehicle based on the background control terminal permission, and then perform an analysis of the motor current fluctuation change to obtain the working condition conversion current fluctuation change data; Step S2: Perform an equal amount fitting of the permanent magnet demagnetization intensity of the motor according to the working condition conversion current fluctuation change data to obtain the equal amount fitting data of the permanent magnet demagnetization intensity; analyze the proportional relationship of the motor output torque loss based on the equal amount fitting data of the permanent magnet demagnetization intensity to obtain the proportional relationship of the output torque loss; Step S3: Design a motor field weakening control strategy according to the equal amount fitting data of the permanent magnet demagnetization intensity and the proportional relationship of the output torque loss to obtain the motor field weakening control strategy; design a motor control firmware based on the motor field weakening control strategy to obtain the motor control firmware; send the motor control firmware to the electric vehicle control terminal to execute the electric vehicle control method.

[0005] Preferably, step S1 includes the following steps: Step S11: Obtain the background control terminal permission of the electric vehicle for power intelligent inspection; Step S12: Extract the motor operating state among complex road conditions of the electric vehicle based on the background control terminal permissions, and obtain the motor operating state data among complex road conditions of the electric vehicle; Step S13: Analyze the motor operating condition conversion frequency state of the motor operating state data among complex road conditions of the electric vehicle to obtain the motor operating condition conversion frequency state data; Step S14: Analyze the motor current fluctuation change of the motor operating condition conversion frequency state data to obtain the condition conversion current fluctuation change data.

[0006] Preferably, step S2 includes the following steps: Step S21: Analyze the continuity mutation intensity of the condition conversion current fluctuation change data to obtain the current continuous mutation intensity data; Step S22: Perform equal amount fitting of the demagnetization intensity of the permanent magnet of the motor according to the current continuous mutation intensity data to obtain the equal amount fitting data of the demagnetization intensity of the permanent magnet; Step S23: Perform non-linear regression analysis on the equal amount fitting data of the demagnetization intensity of the permanent magnet to obtain the non-linear regression data of the demagnetization intensity; Step S24: Analyze the proportional relationship of the output torque loss of the motor based on the non-linear regression data of the demagnetization intensity to obtain the proportional relationship of the output torque loss.

[0007] Preferably, step S21 includes the following steps: Step S211: Calculate the variance of the relative width of the fluctuation peak between different condition conversions of the condition conversion current fluctuation change data to obtain the variance of the relative width of the fluctuation peak between different condition conversions; Step S212: Calculate the increasing slope of the current peak width between different condition conversions of the condition conversion current fluctuation change data according to the variance of the relative width of the fluctuation peak to obtain the increasing slope of the current peak width between different condition conversions; Step S213: Perform an increasing inflection point continuity local range analysis based on the increasing slope of the current peak width and the variance of the relative width of the fluctuation peak to obtain the current increasing inflection point continuity local range; Step S214: Perform multi-scale decomposition of the current mutation acceleration according to the current increasing inflection point continuity local range to obtain the multi-scale decomposition data of the mutation acceleration; Step S215: Analyze the continuity mutation intensity based on the current increasing inflection point continuity local range and the multi-scale decomposition data of the mutation acceleration to obtain the current continuous mutation intensity data.

[0008] Preferably, step S22 includes the following steps: Step S221: Deduce the reverse magnetic field intensity based on the current continuous mutation intensity data to obtain the reverse magnetic field intensity data; calculate the temperature rise rate ratio based on the current continuous mutation intensity data to obtain the temperature rise rate ratio; Step S222: Analyze the non-uniformity of the reverse magnetic field spatial distribution for the reverse magnetic field intensity data to obtain the reverse magnetic field spatial distribution non-uniformity data; Step S223: Identify the distortion distribution of the reverse magnetomotive potential energy for the reverse magnetic field intensity data according to the reverse magnetic field spatial distribution non-uniformity data to obtain the reverse magnetomotive potential energy distortion distribution data; Step S224: Calculate the equal difference of the reverse magnetic flux density distribution for the reverse magnetomotive potential energy distortion distribution data to obtain the reverse magnetic flux density distribution equal difference data; Step S225: Analyze the thermal-magnetic demagnetization coupling strength according to the temperature rise rate ratio and the reverse magnetic flux density distribution equal difference data to obtain the thermal-magnetic demagnetization coupling strength data; Step S226: Fit the demagnetization strength of the motor permanent magnet equally according to the thermal-magnetic demagnetization coupling strength data to obtain the permanent magnet demagnetization strength equal fitting data.

[0009] Preferably, step S225 includes the following steps: Obtain the temperature safety margin of the permanent magnet basic coercivity; identify the spatial temperature overlimit increment gradient for the temperature safety margin of the permanent magnet basic coercivity according to the temperature rise rate ratio to obtain the spatial temperature overlimit increment gradient data; Analyze the non-linear weakening convergence of the permanent magnet basic coercivity according to the spatial temperature overlimit increment gradient data to obtain the non-linear weakening convergence data of the coercivity; Analyze the acceleration variance of the weakening index for the non-linear weakening convergence data of the coercivity to obtain the acceleration variance of the coercivity weakening index; Analyze the thermal-magnetic demagnetization coupling strength according to the acceleration variance of the coercivity weakening index and the reverse magnetic flux density distribution equal difference data to obtain the thermal-magnetic demagnetization coupling strength data.

[0010] Preferably, step S24 includes the following steps: Step S241: Map the weakening gradient of the air-gap magnetic field based on the non-linear regression data of the demagnetization strength to obtain the air-gap magnetic field weakening gradient data; Step S242: Analyze the stage dynamic evolution trend for the air-gap magnetic field weakening gradient data to obtain the air-gap magnetic field stage weakening evolution trend data; Step S243: Analyze the linear drift of the back electromotive force based on the air-gap magnetic field stage weakening evolution trend data to obtain the back electromotive force linear drift data; Step S244: Analyze the proportional relationship of motor output torque loss based on the data of the weakening evolution trend of the air-gap magnetic field stage and the linear drift data of the back electromotive force, and obtain the proportional relationship of output torque loss.

[0011] Preferably, step S3 includes the following steps: Step S31: Conduct logical learning on the proportional relationship of output torque loss to obtain proportional learning data of torque loss; Step S32: Perform convolution processing on the equal-fitting data of the permanent magnet demagnetization intensity to obtain convolution data of equal-fitting of demagnetization intensity; Step S33: Design the field weakening control strategy of the motor according to the proportional learning data of torque loss and the convolution data of equal-fitting of demagnetization intensity to obtain the field weakening control strategy of the motor; Step S34: Design the motor control firmware based on the field weakening control strategy of the motor to obtain the motor control firmware; send the motor control firmware to the electric vehicle control terminal to execute the electric vehicle control method.

[0012] Preferably, step S33 includes the following steps: Step S331: Conduct differential growth rate analysis on the proportional learning data of torque loss to obtain differential growth rate data of torque loss; Step S332: Perform intensity segmented response fitting on the differential growth rate data of torque loss according to the convolution data of equal-fitting of demagnetization intensity to obtain segmented demagnetization coupling response data; Step S333: Conduct derivation processing on the direction offset of the magnetic flux vector of the segmented demagnetization coupling response data to obtain derivation data of the magnetic flux vector offset; Step S334: Perform backtracking processing on the dynamic boundary of the motor voltage according to the derivation data of the magnetic flux vector offset to obtain backtracking control data of the voltage dynamic boundary; Step S335: Design the field weakening control strategy of the motor according to the derivation data of the magnetic flux vector offset and the backtracking control data of the voltage dynamic boundary to obtain the field weakening control strategy of the motor.

[0013] Preferably, the present invention also provides an electric vehicle control system for executing the above-mentioned electric vehicle control method. The electric vehicle control system includes: A current fluctuation change analysis module, configured to obtain the background control terminal permission of the electric power intelligent inspection electric vehicle; extract the motor operating state among complex road conditions of the electric vehicle based on the background control terminal permission, and then conduct analysis on the motor current fluctuation change to obtain working condition conversion current fluctuation change data; An output torque loss analysis module, configured to perform equal quantity fitting of the demagnetization intensity of the motor permanent magnet according to the current fluctuation data during working condition conversion, so as to obtain the equal quantity fitting data of the demagnetization intensity of the permanent magnet; and perform analysis of the proportional relationship of the output torque loss of the motor based on the equal quantity fitting data of the demagnetization intensity of the permanent magnet, so as to obtain the proportional relationship of the output torque loss. A field weakening control strategy design module, configured to design a field weakening control strategy for the motor according to the equal quantity fitting data of the demagnetization intensity of the permanent magnet and the proportional relationship of the output torque loss, so as to obtain the field weakening control strategy for the motor; design motor control firmware based on the field weakening control strategy for the motor, so as to obtain the motor control firmware; and send the motor control firmware to an electric vehicle control terminal to execute an electric vehicle control method.

[0014] The beneficial effects of the present invention are as follows. By obtaining the background control terminal permission of the electric power intelligent inspection electric vehicle, the operation state data of the motor can be accurately extracted, especially the current fluctuation under complex road conditions. This data collection process provides a basis for subsequent motor performance analysis and can monitor the working condition conversion of the electric vehicle in different road conditions in real time. By analyzing the current fluctuation change of the motor, the working state of the motor under different load and speed conditions can be effectively identified, thereby providing key data support for optimizing motor control and ensuring the stable and efficient operation of the electric vehicle under various complex working conditions. Based on the current fluctuation data during working condition conversion, equal quantity fitting of the demagnetization intensity of the motor permanent magnet is performed, and the demagnetization situation of the permanent magnet in the long-term operation of the motor can be accurately evaluated. The fitting data of the demagnetization intensity provides an effective prediction basis for the output torque loss of the motor. By deeply analyzing the relationship between the demagnetization intensity of the permanent magnet and the torque loss of the motor, potential performance degradation problems of the motor can be identified in advance, and thus a basis for subsequent control strategy optimization can be provided. Such data analysis helps to achieve refined management of motor control, reduce unnecessary energy loss, and improve the long-term operation performance of the motor. Designing a field weakening control strategy for the motor according to the proportional relationship between the demagnetization intensity of the permanent magnet and the output torque loss can effectively reduce the performance loss caused by motor demagnetization. By implementing the field weakening control strategy, the current fluctuation of the motor can be balanced, the working efficiency of the motor can be optimized, and the torque loss caused by demagnetization can be reduced. In addition, the motor control firmware designed based on this strategy can adjust the working state of the motor in real time to achieve dynamic control, so as to maintain the high efficiency of the electric vehicle under different driving environments. Finally, sending this control firmware to the electric vehicle control terminal can ensure that the electric vehicle has better power output and energy efficiency performance under complex working conditions, and improve the overall performance of the electric vehicle. Therefore, the present invention optimizes a traditional electric vehicle control method, solves the problem that the traditional electric vehicle control method has inaccurate analysis of the demagnetization of the motor permanent magnet and the output torque loss, resulting in large motor control errors, improves the accuracy of the analysis of the demagnetization of the motor permanent magnet and the output torque loss, and reduces the motor control error. Description of the Drawings

[0015] Figure 1 It is a schematic diagram of the step flow of an electric vehicle control method; Figure 2 It is Figure 1 a schematic diagram of the detailed implementation steps of step S2 in Figure 3 It is Figure 1 a schematic diagram of the detailed implementation steps of step S3 in Detailed Implementation Manner

[0016] Please refer to Figures 1 to 3 for an electric vehicle control method, the method includes the following steps: Step S1: Obtain the permission of the background control terminal of the electric power intelligent inspection electric vehicle; extract the motor operating state among complex road conditions of the electric vehicle based on the background control terminal permission, and then perform an analysis of the motor current fluctuation change to obtain the working condition conversion current fluctuation change data; Step S2: Perform an equal amount fitting of the motor permanent magnet demagnetization intensity according to the working condition conversion current fluctuation change data to obtain the equal amount fitting data of the permanent magnet demagnetization intensity; analyze the proportional relationship of the motor output torque loss based on the equal amount fitting data of the permanent magnet demagnetization intensity to obtain the proportional relationship of the output torque loss; Step S3: Design a motor field weakening control strategy according to the equal amount fitting data of the permanent magnet demagnetization intensity and the proportional relationship of the output torque loss to obtain the motor field weakening control strategy; design a motor control firmware based on the motor field weakening control strategy to obtain the motor control firmware; send the motor control firmware to the electric vehicle control terminal to execute the electric vehicle control method.

[0017] In the embodiment of the present invention, referring to Figure 1 described above, it is a schematic diagram of the step flow of an electric vehicle control method of the present invention. In this example, the electric vehicle control method includes the following steps: Step S1: Obtain the permission of the background control terminal of the electric power intelligent inspection electric vehicle; extract the motor operating state among complex road conditions of the electric vehicle based on the background control terminal permission, and then perform an analysis of the motor current fluctuation change to obtain the working condition conversion current fluctuation change data; In the embodiment of the present invention, a vehicle control bus node is accessed from a power intelligent inspection electric vehicle control platform through a CAN communication protocol with a master-slave structure. The access permission is verified in the form of an identity authentication code and a control permission parameter packet. The multi-source redundant permission table in the control terminal is read to perform a 16-bit hash check to confirm the control level. After confirming the permission of the background control terminal, the data in the input / output data buffer of the motor controller during the vehicle operation is directly read through the system call method. Under complex road conditions, typical scenario data including uphill, downhill, constant speed, acceleration, deceleration, and frequent start / stop between different road sections is collected. The current value, speed, temperature, voltage, and PWM duty cycle of the motor under each type of scenario are cumulatively sampled at a sampling interval of 500 ms and synchronized with time stamps. The Savitzky-Golay filter is used to denoise the original current sampling data. Then, the processed current data is divided into different working condition conversion sections. For each conversion section, the current fluctuation change amount is calculated. The differential sliding window method is used to calculate the fluctuation gradient change rate between adjacent samples before and after, and the root mean square slope of the current change curve within the section is constructed. Finally, the working condition conversion current fluctuation change data is extracted and stored in the working condition feature database in a time series structure.

[0018] Step S2: Perform an equivalent fitting of the permanent magnet demagnetization strength of the motor according to the working condition conversion current fluctuation change data to obtain the equivalent fitting data of the permanent magnet demagnetization strength; analyze the proportional relationship of the motor output torque loss based on the equivalent fitting data of the permanent magnet demagnetization strength to obtain the proportional relationship of the output torque loss; In the embodiment of the present invention, the above-mentioned working condition conversion current fluctuation change data is used as the input to quantitatively analyze the permanent magnet demagnetization phenomenon that occurs in the motor under specific road conditions. The specific operations include first locating the mutation characteristic points. The mutation interval is judged by setting a threshold that the current fluctuation change amount is greater than 1.2 A / s. Then, cross-analysis is performed on the fluctuation change value within the mutation interval and the temperature data. The samples with a fluctuation slope greater than 5 A / s and a temperature rise exceeding 20 °C / min are marked as potential demagnetization trigger points. Subsequently, according to the experimental curve of the influence of different temperatures on the remanence density in the permanent magnet magnetic property data table, an equivalent fitting framework is constructed by combining the current change amplitude in the time domain. The cubic interpolation method is used to reconstruct the discontinuous sample points. Then, piecewise linear fitting analysis is performed on the three-dimensional data set of temperature-current-remanence density. Finally, the equivalent fitting data of the permanent magnet demagnetization strength is generated. Subsequently, based on the comparison between the fitting data and the initial working condition of the motor calibrated output torque, by measuring the difference between the current input and the output torque under the same load, the torque loss rate caused by demagnetization is calculated. The equivalent regression method is used to map the demagnetization strength value and the torque loss correspondingly and perform a linear residual analysis. The proportional relationship parameters of the output torque loss are extracted and written into the loss mapping table in a vector format.

[0019] Step S3: Design the field-weakening control strategy of the motor according to the equal-fitting data of the permanent magnet demagnetization intensity and the proportional relationship of the output torque loss, and obtain the field-weakening control strategy of the motor; design the motor control firmware based on the field-weakening control strategy of the motor, and obtain the motor control firmware; send the motor control firmware to the electric vehicle control terminal to execute the electric vehicle control method.

[0020] In the embodiment of the present invention, taking the equal-fitting data of the permanent magnet demagnetization intensity and the proportional relationship of the output torque loss in step S2 as the core input, a field-weakening control strategy based on the adjustment of the flux control boundary is designed. First, read the relationship threshold between the d-axis current and the flux linkage control in the standard vector control model, and combine the torque loss data to lower the control limit of the d-axis current. During the process of adjusting the Id limit from -30A to -80A, collect the changes in the output torque and voltage utilization rate, extract the optimal field-weakening control interval parameters, and then use the discrete voltage limit backstepping method to correct the voltage boundary in the field-weakening interval. Take the control point with a voltage utilization rate higher than 90% but not in the overvoltage state as the control boundary inflection point, perform central difference processing on it to construct the minimum voltage margin map, and at the same time judge the flux weakening rate based on the convolution data of the equal-fitting of the demagnetization intensity. On this basis, reconstruct the mapping of the voltage boundary and the flux linkage evolution path, map the field-weakening control behaviors under different working conditions in a stepped and segmented manner, form a field-weakening control strategy rule table of the motor, embed this rule table in the parameter block of the motor control firmware in HEX format, and then burn the firmware file into the main control ECU of the electric vehicle through the SPI interface. After the update is successful, load the new control logic through the main control startup redirection method to complete the final execution of the electric vehicle control method.

[0021] Step S1 includes the following steps: Step S11: Obtain the permission of the background control terminal of the electric power intelligent inspection electric vehicle; Step S12: Extract the motor operating state between complex road conditions of the electric vehicle based on the permission of the background control terminal, and obtain the motor operating state data between complex road conditions of the electric vehicle; Step S13: Analyze the motor condition conversion frequency state of the motor operating state data between complex road conditions of the electric vehicle, and obtain the motor condition conversion frequency state data; Step S14: Analyze the motor current fluctuation change of the motor condition conversion frequency state data, and obtain the condition conversion current fluctuation change data.

[0022] In the embodiments of the present invention, first, connect to the control management interface of the main control platform of the electric vehicle for intelligent inspection through the Ethernet remote communication method. Use the SHA-256 hash algorithm for permission identity verification of the background control terminal. Send a 16-byte permission authentication request packet containing a control permission identification field, a controller ID number, and a timestamp to the control port. After verification by the permission management module of the main control system, return a 32-byte authentication result and write it into the log recording system. The system will layer by layer verify the legality of the control request according to the preset permission code index table. After the permission verification passes, call the underlying driver interface function of the motor controller to enable the monitoring mode of the vehicle motor control system, and initialize the read-only permission configuration of all motor controller data buffers. Set the cache reading period to 50 milliseconds, and continuously capture the data related to the operating state and enter the next processing flow. After confirming the successful access of the permission, based on the working condition data acquisition instruction issued by the main control platform, drive the electric vehicle in turn on five types of typical complex road conditions including a 3-degree uniform uphill slope, a 5-degree steep slope, an urban congested road, a circular ramp, and a hilly section with undulations in the actual test environment. The driving distance for each road condition is set to 3 kilometers. During the acquisition process, use a three-axis inertial measurement unit to collect the vehicle acceleration and angular velocity, and combine the mileage signal output by the wheel encoder to determine the actual working condition switching position. The sampling period is uniformly set to 20 milliseconds. At each moment, collect the phase currents Ia, Ib, Ic, the bus voltage Vdc, the rotational speed N, the controller temperature Tctrl, the load torque Tload, and the PWM duty cycle D output by the motor controller. All data is written into the structured data buffer in sequence according to the timestamp order, and CRC-16 is used for transmission integrity verification. Divide the data under different road conditions into multiple section label annotation areas, and finally form the motor operating state data among the complex road conditions of the electric vehicle. After reading the above motor operating state data, first perform basic preprocessing, including performing fundamental wave extraction processing on the amplitude of the phase current, using the fast Fourier transform FFT method to extract the main frequency components in the 0–500 Hz interval to distinguish the frequency conversion characteristics of each road condition. Then, identify the state jump intervals in different time periods by setting thresholds. When it is determined that the mean change of the phase current exceeds 3 A and the motor speed change rate is greater than 200 rpm / s among 30 consecutive sampling points, it is determined as a working condition conversion event. At the same time, verify and match it in combination with the event points where the vehicle acceleration is greater than 1.5 m / s² or less than -1.5 m / s². Use the K-means clustering algorithm to cluster and segment different frequency sections. Define below 1 Hz as the slow conversion state, 1 Hz~5 Hz as the medium conversion state, and above 5 Hz as the fast conversion state. Each conversion frequency value is taken as the frequency corresponding to the main frequency peak value in the changing section. Finally, label all the conversion frequency tags in the form of a time axis to form the motor working condition conversion frequency state data.First, analyze the conversion events one by one based on the frequency status data in step S13, extract the change values of the phase currents Ia, Ib, and Ic in the corresponding time period of each conversion section, and synthesize the three-phase currents into an equivalent current using the three-axis normalization method. , then calculate the current change rate ΔIe / Δt between adjacent sampling points through the five-point central difference method, thereby constructing a current change rate curve. Define the fluctuation change event as the area where the current rate changes by more than 4 A / s within 0.1 s. Calculate the range within each fluctuation section to obtain the peak amplitude of the fluctuation. Map the fluctuation change values under all conversion frequency states according to the section number and time index, and perform Z-score normalization processing on the fluctuation intensity data to form the working condition conversion current fluctuation change data.

[0023] Step S2 includes the following steps: Step S21: Analyze the continuity mutation intensity of the working condition conversion current fluctuation change data to obtain the current continuous mutation intensity data; Step S22: Perform equal amount fitting of the motor permanent magnet demagnetization intensity according to the current continuous mutation intensity data to obtain the equal amount fitting data of the permanent magnet demagnetization intensity; Step S23: Perform nonlinear regression analysis on the equal amount fitting data of the permanent magnet demagnetization intensity to obtain the nonlinear regression data of the demagnetization intensity; Step S24: Analyze the proportional relationship of the motor output torque loss based on the nonlinear regression data of the demagnetization intensity to obtain the proportional relationship of the output torque loss.

[0024] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes: Step S21: Analyze the continuity mutation intensity of the working condition conversion current fluctuation change data to obtain the current continuous mutation intensity data; In the embodiment of the present invention, the working condition conversion current fluctuation data obtained in step S14 is segmented according to the time stamp order, and the length of each segment is 100 sampling points. The sampling period is fixed at 20 milliseconds. The difference sequence of each segment of data is constructed, and the current change slope sequence is extracted by the first-order central difference method. The mutation recognition threshold is set to ±3 A / ms. When the change slope exceeds this threshold, the mutation starting point is recorded, and whether the mutation continues is determined through a continuous time window. The continuity judgment window is set to 5 consecutive sampling points. If the change slope remains near the set threshold within this window, it is marked as a continuous mutation event. In each continuous mutation event interval, the current peak change amount ΔI and the corresponding time span Δt of this change are calculated. ΔI / Δt is used as the mutation intensity scalar, and it is normalized by combining the mean ratio before and after the mutation to generate a mutation intensity index with a unified dimension. This index is defined as the current continuous mutation intensity. The mutation intensity data of all events is constructed into a current continuous mutation intensity vector, and each piece of data corresponds to its working condition label, mutation position index, mutation amplitude, and time span. Finally, the current continuous mutation intensity data is obtained.

[0025] Step S22: Perform equivalent fitting of the motor permanent magnet demagnetization intensity and the like according to the current continuous mutation intensity data to obtain the equivalent fitting data of the permanent magnet demagnetization intensity; In the embodiment of the present invention, first, the current continuous mutation intensity data obtained in step S21 is read, and each mutation intensity is attributed and mapped in turn. A five-dimensional feature vector is constructed based on multi-dimensional working condition parameters such as the road condition type where the vehicle is located, the motor load torque, the temperature, and the controller PWM duty cycle. The mutation intensity is matched with this five-dimensional feature vector. According to the current response characteristics caused by the permanent magnet flux in the permanent magnet synchronous motor theory during the demagnetization process, using the statistical equivalent mapping method, current mutation response templates corresponding to different demagnetization degrees are set in a neodymium iron boron permanent magnet with a magnetic energy product of 380 kJ / m³, a Curie temperature of 580 °C, a remanence of 1.2 T at room temperature, and a thickness of 4.5 mm. The mutation intensity vector is matched according to the template interval and mapped to a preset demagnetization level interval. The interval distribution is 21 levels from 0% to 40%. The nearest neighbor interpolation method is used to continuously fit the mutation intensity between the demagnetization levels to obtain the percentage value of the permanent magnet demagnetization intensity corresponding to each mutation event of the motor. Finally, it is obtained that each row of data records the mutation event number, the current mutation intensity value, the corresponding demagnetization intensity level, the working condition type label, and the time index.

[0026] Step S23: Perform non-linear regression analysis on the equivalent fitting data of the permanent magnet demagnetization intensity to obtain the non-linear regression data of the demagnetization intensity; In the embodiment of the present invention, regression modeling is performed on the demagnetization intensity fitting data constructed in step S22. First, abnormal data points are removed, including samples with demagnetization levels exceeding reasonable physical boundaries (less than 0 or greater than 50%), and high-amplitude mutation points with a duration of less than 0.05 seconds. After standardization, a non-linear regression analysis method is used to establish the mathematical relationship between the mutation intensity and the demagnetization level. A cubic spline function is selected as the basic fitting curve configuration, and the least squares method is used for parameter optimization. Local curvature adjustment is performed within each mutation level interval to reduce the fitting residuals. The regression objective function takes the demagnetization level as the dependent variable and the current mutation intensity as the independent variable. The L-BFGS-B boundary constraint method is used to perform constraint convergence processing on the function parameters during the optimization iteration process. The iteration stop condition is that the convergence rate of the objective function is less than 1e-6. The final fitting curve is output in the form of a three-segment piecewise cubic function, and the goodness of fit of each fitting interval is calculated. The interval with R² greater than 0.92 is taken as the effective fitting region. The regression results are output in a structured form, including the function coefficients, interval range, goodness of fit, and residual vector of each curve segment.

[0027] Step S24: Analyze the proportional relationship of the motor output torque loss based on the non-linear regression data of the demagnetization intensity to obtain the proportional relationship of the output torque loss.

[0028] In the embodiment of the present invention, based on the non-linear regression data of the demagnetization intensity output in step S23, an equal-proportion analysis relationship between the permanent magnet demagnetization intensity and the motor output torque loss is further constructed. According to the correspondence between the back electromotive force formula of the permanent magnet synchronous motor and the integral value of the actual current waveform, in an electric drive system with a rated torque of 180 Nm, a rated speed of 3500 rpm, a rated voltage of 310 V, and a rated current of 45 A, the actual maximum output torque values corresponding to different demagnetization levels are measured and recorded as 180 Nm, 174 Nm, 165 Nm, 151 Nm, 138 Nm, 123 Nm, 110 Nm, 94 Nm, 82 Nm at demagnetization levels of 0%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40% respectively. The original data set between the torque loss rate and the demagnetization intensity is extracted from these data points. With the demagnetization intensity as the horizontal axis and the actual torque drop ratio as the vertical axis, a linear piecewise fitting method is used to establish the proportional factors for each interval, which are respectively defined as 0–10%, 10–20%, 20–30%, 30–40%. The proportional constants for each segment are calculated as 1.1%, 1.4%, 1.7%, 1.9% per demagnetization percentage point, and a torque loss ratio function table is constructed to obtain the proportional relationship of the output torque loss.

[0029] Step S21 includes the following steps: Step S211: Calculate the variance of the relative width of the peak fluctuations between different operating condition conversions for the data of the current fluctuations during operating condition conversion, to obtain the variance of the relative width of the peak fluctuations between different operating condition conversions; Step S212: Calculate the increasing slope of the current peak width between different operating condition conversions for the data of the current fluctuations during operating condition conversion according to the variance of the relative width of the peak fluctuations, to obtain the increasing slope of the current peak width between different operating condition conversions; Step S213: Conduct an analysis of the local range of the continuity of the increasing inflection point based on the increasing slope of the current peak width and the variance of the relative width of the peak fluctuations, to obtain the local range of the continuity of the increasing inflection point of the current; Step S214: Conduct a multi-scale decomposition of the current mutation acceleration according to the local range of the continuity of the increasing inflection point of the current, to obtain the data of the multi-scale decomposition of the mutation acceleration; Step S215: Conduct an analysis of the intensity of the continuous mutation based on the local range of the continuity of the increasing inflection point of the current and the data of the multi-scale decomposition of the mutation acceleration, to obtain the data of the intensity of the continuous mutation of the current.

[0030] In the embodiments of the present invention, first, based on the three-phase current waveform data of the motor recorded during the complex working condition switching process of the electric vehicle, all peak points are extracted within the entire sampling period. The current peak extraction threshold is set to 1.5 times the average value of the base current. The peak detection operation is performed on every 200 sampling points by the sliding window method. The adjacent pseudo-peaks are removed by the constraint that the peak spacing is greater than 20 points. Then, the half-width between each adjacent pair of peaks is used as the peak width index, and the average value of all peak widths within each working condition segment is calculated. The average peak widths of each working condition interval are defined as W1, W2, W3, etc. The ratio of the standard deviation to the mean of the average peak widths of all working condition intervals is calculated to obtain the relative width variance of the fluctuating peaks between each working condition transition segment. This variance is defined as the standard deviation divided by the average value, representing the relative change degree of the peak widths of the current waveforms under different working conditions. The sampling segments are sorted by the working condition number, and a one-dimensional relative width variance sequence is formed for subsequent slope and range analysis. Based on the relative width variance sequence of the fluctuating peaks obtained in S211, the difference in the width variances corresponding to two adjacent working condition transition segments is defined as the peak width change amount per unit working condition switching. Then, the peak width increasing slope is calculated by dividing this change amount by the difference in the working condition numbers (always 1). Further, a three-point smoothing process is performed on the slopes of every three consecutive working condition transition segments to eliminate the mutation error caused by the sharp changes in the data. A time series curve is constructed for the processed slope sequence and used as a reference for judging the increasing trend. The changing trend of this increasing slope sequence will be used to judge the position of the obvious energy transition inflection point in the subsequent current waveform. The parts of all increasing segments with a slope greater than the set threshold of 0.8 are marked as strong increasing segments, and a sequence mapping table is established in cooperation with the time series index for the range positioning analysis of the increasing inflection points. The current peak width increasing slope data obtained in step S212 and the relative width variance sequence of the fluctuating peaks obtained in step S211 are read. The continuous segments with a slope greater than 0.8 are divided into sub-intervals, and the length of each sub-interval is 50 sampling points. The range of the current peak width is calculated within each sub-interval, that is, the maximum value minus the minimum value is used as the range index. At the same time, a sliding range analysis window centered on this range is constructed, the window length is fixed at 100 sampling points, and the window step size is 20 sampling points. The continuity of the range sequence is extracted by using the change situation of the range within each sub-interval of the window. If the change directions of the ranges of three consecutive sub-intervals remain the same and the difference is greater than 3A, it is determined that there is an increasing inflection point continuity feature within this window. Then, the corresponding time series position of this segment is extracted, and the peak position of the inflection point curvature is extracted by calculating the second-order difference in the region where the range changes violently. The local current change rate and its increment amplitude at the inflection point are calculated in combination with the slope change amount. Finally, the local range result data of the increasing inflection point continuity is generated, which includes the start and end position indexes, the range size, the duration, and the average slope information.Based on the continuous local extreme difference data of the current increasing inflection points obtained in step S213, the current time series signal is segmented into multiple independent window segments, each segment corresponding to an inflection point extreme difference interval. After performing maximum-minimum normalization, a multi-scale decomposition operation is carried out. The Daubechies-4 (db4) wavelet in discrete wavelet transform is selected as the decomposition basis function, and the decomposition level is set to 5 layers. The current acceleration curves in different frequency bands are reconstructed from the wavelet decomposition coefficients of each layer, and the first-order derivatives in the signal of each frequency band are extracted respectively as acceleration indicators. At the same time, the positions, amplitudes and durations of acceleration mutation points are extracted in each decomposition frequency band. After normalizing the amplitudes of the acceleration mutation points in all frequency bands, they are aligned with the corresponding extreme difference section positions to form a multi-scale dataset of mutation acceleration. The above process is performed for all inflection point extreme difference regions, the mutation acceleration indicators of each frequency band are recorded, and the cross-correlation coefficients between frequency bands are calculated. The multi-scale components with high mutation intensity, concentrated frequency bands and cross-correlation coefficients greater than 0.7 are marked as the effective mutation acceleration decomposition results. Combining the continuous local extreme difference data of current increasing inflection points and the multi-scale decomposition data of mutation acceleration obtained in steps S213 and S214, a mutation intensity analysis model is constructed using an index fusion criterion. First, the local extreme difference is used as the reference amplitude feature, and the acceleration values of each frequency band of acceleration decomposition are used as the time change rate features. The fusion mutation intensity index is calculated by the normalized linear weighted average method, and the weight distribution ratio is 0.6 for the extreme difference feature and 0.4 for the acceleration feature. Then, the sliding window mean smoothing is performed on the fused mutation intensity result, and the window length is set to 3 sampling points. Finally, this mutation intensity index is used as the output data of the current continuous mutation intensity, which includes the mutation starting position, mutation intensity value, mutation duration and the corresponding decomposition layer index information, forming the final current continuous mutation intensity data sequence for the demagnetization intensity fitting analysis.

[0031] Step S22 includes the following steps: Step S221: Deduce the reverse magnetic field intensity based on the current continuous mutation intensity data to obtain the reverse magnetic field intensity data; calculate the temperature rise rate ratio based on the current continuous mutation intensity data to obtain the temperature rise rate ratio; Step S222: Analyze the non-uniformity of the reverse magnetic field spatial distribution of the reverse magnetic field intensity data to obtain the non-uniformity data of the reverse magnetic field spatial distribution; Step S223: Identify the distortion distribution of the reverse magnetomotive potential energy of the reverse magnetic field intensity data according to the non-uniformity data of the reverse magnetic field spatial distribution to obtain the distortion distribution data of the reverse magnetomotive potential energy; Step S224: Calculate the equal difference of the reverse magnetic flux density distribution for the distortion distribution data of the reverse magnetomotive potential energy to obtain the equal difference data of the reverse magnetic flux density distribution; Step S225: Analyze the thermal-magnetic demagnetization coupling strength based on the temperature rise rate ratio and the reverse magnetic flux density distribution equal difference data to obtain the thermal-magnetic demagnetization coupling strength data; Step S226: Perform an equivalent fitting of the demagnetization strength of the motor permanent magnet according to the thermal-magnetic demagnetization coupling strength data to obtain the equivalent fitting data of the demagnetization strength of the permanent magnet.

[0032] In the embodiment of the present invention, based on the current continuous mutation intensity data obtained in step S215, an inverse relationship is established between the mutation intensity and the equivalent winding parameters of the motor. The equivalent current-magnetic field mapping method is used to deduce the reverse magnetic field intensity corresponding to the mutation point. On the basis that the number of turns of the motor stator winding is N = 36 turns, the equivalent length of the stator magnetic circuit is L = 0.15 m, and the winding cross-sectional area is A = 0.0025 m², the mutation intensity is used as the basic current excitation signal with the current transient amplitude change ΔI = 8 A / 2 ms per unit time. Using Ampere's circuital law and the principle of magnetic field superposition, the equivalent reverse magnetic field intensity is calculated. Combining the timing position of the mutation position within the winding phase sequence, it is converted into a spatial magnetic flux change reference point. The reverse magnetic field intensity is expressed in units of A / m. Then, combining the duration of the mutation intensity and the stator winding resistance R = 0.18 ohm, the equivalent Joule heat accumulation Q = I²Rt is calculated, and the instantaneous temperature rise rate ΔT / Δt is calculated based on the average power change within the time interval Δt = 2 ms corresponding to each mutation point. By comparing the average temperature change in the steady-state intervals before and after the mutation, the thermocouple data within 2 s before and after the mutation is set for average value difference analysis. The temperature rise rate ratio is defined as the ratio of the mutation point to the steady-state average. Finally, the reverse magnetic field intensity data and the temperature rise rate ratio are obtained for subsequent thermal-magnetic interaction linkage determination. The reverse magnetic field intensity data obtained in step S221 is spatially mapped according to the principle of symmetrical distribution of the three-phase windings. The mutation point magnetic field values corresponding to the A, B, and C phase windings are respectively extracted at the three equal division positions of the motor circumference, forming a magnetic field space vector sequence with 12 equal divisions within 360°. The radius of each vector is set to 50 mm based on the winding center, and the sampling angle interval is 30°. A two-dimensional vector diagram is constructed on the circumferential plane. By calculating and normalizing the magnetic field intensity difference between any two adjacent angle points, the discrete standard deviation of the magnetic field distribution within the entire circumference is calculated. Further, the angle segment with the largest standard deviation change within the entire circumference range is extracted, and a linear fit is performed on the magnetic field intensity difference within this segment to obtain the non-uniformity slope index. If the absolute value of this slope is greater than the set threshold of 0.6, it is determined that there is a non-uniformity characteristic of the reverse magnetic field spatial distribution in this area, and non-uniformity data including the angle segment number, non-uniformity slope, maximum magnetic field difference, and average magnetic field intensity of this segment is output.Based on the non-uniformity data of the reverse magnetic field spatial distribution in step S222, calculate the magnetic flux circulation intensity distribution in each non-uniformity section. After synthesizing the magnetomotive forces of the three-phase windings into an equivalent magnetomotive force direction vector, perform a rotating coordinate system transformation. In the transformed coordinate system, project the magnetomotive force intensity according to the 120° angle between the windings to establish a polar coordinate magnetomotive force intensity distribution diagram. Then, calculate the difference in the annular magnetomotive force distribution at 12 equally spaced measurement points set on an equal-diameter circle, extract the region with strong asymmetric magnetomotive force distortion where three consecutive points appear, perform central difference calculation on the magnetomotive force change gradient in this region and take its second derivative to approximately represent the distortion degree, and extract the position and amplitude of the peak distortion point as the magnetomotive force distortion distribution index. If the distortion values of two or more adjacent angular points are greater than the set threshold of 12 A·m, mark it as a distortion region and output the start and end angles of distortion, the average magnetomotive force value, the peak distortion amplitude, and the region area as the reverse magnetic potential energy distortion distribution data. Based on the reverse magnetic potential energy distortion distribution data in step S223, construct the magnetic flux density distribution field in the corresponding region, select the magnetic flux path loop corresponding to the distortion region, estimate the magnetic flux density per unit area through the change value of the magnetic flux per unit area of the winding based on Faraday's law of electromagnetic induction. Set the stator lamination thickness to 0.35 mm and the area sampling for each section to 20 mm × 20 mm, calculate the change amount of the magnetic flux density in the unit region within the motor magnetic circuit through convolution integral, use the difference between the magnetic flux densities of adjacent regions as the arithmetic difference index, and after equally dividing the entire circle of angles, obtain the magnetic flux density difference sequence for each equal division section. Calculate the average value and standard deviation of the arithmetic increment of this sequence. If there are more than 4 consecutive increasing or decreasing differences with the same direction in this arithmetic sequence, it is considered that there is a stable reverse magnetic flux density gradient distribution, and output the gradient distribution direction, the difference mean value, the maximum difference, and the extreme point position as the reverse magnetic flux density distribution arithmetic difference data. Combine the temperature rise rate ratio obtained in step S221 with the reverse magnetic flux density distribution arithmetic difference data obtained in step S224 for joint analysis, perform alignment operation on the time axis, set the maximum time offset not exceeding 50 ms as the matching condition, perform coupling calculation on the successfully matched temperature rise rate ratio and the corresponding magnetic flux density gradient, define the coupling strength as the temperature rise rate ratio multiplied by the magnetic flux density difference amplitude, and construct the thermal-magnetic combined excitation intensity index in this way. Set the coupling analysis window width to 200 ms, perform weighted average processing on the data in each matching window, with the proportion of the temperature rise rate ratio being 0.55 and the proportion of the magnetic flux density difference being 0.45. Perform trend recognition and extreme value extraction on the processing results of all sliding windows, extract the peak point, duration, and peak appearance time point of the coupling strength, and record the mean value and maximum value of each section of the coupling index as the thermal-magnetic interaction demagnetization coupling strength data.Based on the thermal-magnetic demagnetization coupling strength data obtained in step S225, the demagnetization strength fitting operation is performed in combination with the characteristic parameters of the permanent magnet material. The linear segment fitting method is used to process the residual magnetism change of the demagnetization curve of the NdFeB permanent magnet material under different temperatures and external reverse magnetic fields in a piecewise linear function manner. The equivalent reverse magnetic field strength derived from the actual highest temperature point and the reverse magnetic flux density in the coupling strength peak section is interpolated and fitted to the demagnetization residual magnetization point of the material. The demagnetization amount is fitted for each coupling strength peak event, and the demagnetization intensity output unit is T. The multi-segment fitting results are weighted averaged, and the weight is set to the product of the coupling strength and the duration. In this way, the motor permanent magnet demagnetization intensity value corresponding to each thermal-magnetic combined impact event is output, and finally the permanent magnet demagnetization intensity equal fitting data is formed.

[0033] Step S225 includes the following steps: Obtaining the temperature safety margin of the permanent magnet's basic coercive force; performing spatial temperature over-limit increment gradient identification on the temperature safety margin of the permanent magnet's basic coercive force according to the temperature rise rate ratio, and obtaining spatial temperature over-limit increment gradient data; Based on the spatial temperature over-limit increment gradient data, the nonlinear weakening convergence analysis of the temperature safety margin of the permanent magnet's basic coercive force is carried out to obtain the nonlinear weakening convergence data of the coercive force; The variance analysis of the acceleration of the weakening exponential is carried out on the convergence data of the nonlinear weakening of the coercive force, and the variance of the acceleration of the coercive force weakening exponential is obtained; The thermal-magnetic demagnetization coupling intensity data is obtained by analyzing the thermal-magnetic demagnetization coupling intensity based on the acceleration variance of the coercive force weakening index and the reverse magnetic flux density distribution.

[0034] In the embodiment of the present invention, the temperature safety margin of the basic coercivity of the permanent magnet is obtained. The permanent magnet used in the permanent magnet synchronous motor is selected as the sintered neodymium iron boron material of N42H grade. The coercivity of this material at room temperature of 25°C is 931 kA / m. Under the condition of temperature increase, its coercivity shows a non-linear downward trend. By referring to the temperature-coercivity test curve provided by the material manufacturer and performing piecewise interpolation, the coercivity change data at every 5°C temperature rise in the range of 25°C to 120°C is obtained. Based on this change data, the critical safety line of the system temperature rise is set at 95°C, and a temperature safety margin sequence of the basic coercivity is constructed, that is, the difference between the actual coercivity and the demagnetization critical coercivity at each temperature point, forming a coercivity safety margin table corresponding to the temperature. This table is used for subsequent mapping relationship analysis with the temperature rise rate ratio. On this basis, the identification operation of the spatial temperature over-limit increment gradient is carried out. Based on the temperature rise rate ratio data obtained in the previous step, temperature sampling points are selected at equal intervals in the inner cavity of the motor stator. The distance between each sampling point and the center plane of the permanent magnet is set at 3 mm, and the sampling frequency is 500 Hz. The peak value of the temperature rise rate ratio of each sampling point is extracted by using the sliding window method with a time window of 200 ms. The ratio of the local maximum value to the mean value of each point in the temperature rise rate ratio sequence is compared, the abnormal sudden rise points greater than 2.0 are extracted and the temperature rise slope difference of each point is calculated. Based on the comparison of the physical position coordinates between the points, the gradient change value of the temperature rise rate ratio between two adjacent temperature measurement points is calculated. The point pairs with a gradient change exceeding 0.15 / K are marked as spatial temperature over-limit increment units. The entire spatial temperature measurement area is meshed, the triangular meshing method is used to establish the temperature measurement space field, the Lagrange interpolation method is used to construct the temperature rise rate contour map, and the area with the largest contour density is extracted to form the spatial temperature over-limit increment gradient data. This data includes key parameters such as coordinate index, local maximum gradient of the temperature rise rate, regional range, and temperature increment peak value. First, according to the temperature-coercivity safety margin mapping curve constructed in step one, the spatial temperature over-limit increment gradient data is matched point by point. In each grid unit, according to the corresponding temperature value, it is mapped to the basic coercivity safety margin table to obtain the coercivity decrease amount corresponding to the current temperature. Then, a spatial field of the coercivity decrease rate is constructed with the grid as the unit, the local coercivity change rate of the center point of each grid is calculated, and the second-order difference operation is performed on the change rate sequence to judge whether its change trend tends to be stable. If the difference result of the coercivity change rate of the center points of three consecutive layers of grids fluctuates less than the preset convergence threshold of 0.03 kA / m, it is determined that this point enters the coercivity weakening convergence area. Finally, the coercivity non-linear weakening convergence data is obtained, including parameter information such as spatial distribution index, weakening value mean, fluctuation range, and convergence slope.In the process of performing the accelerated variance analysis of the coercivity weakening index, the exponential difference calculation is carried out on the time series of the above coercivity non-linear weakening convergence data. The time span of each convergence region is set to 200 ms, and the degree of coercivity weakening in each region within this time window is extracted. An exponential change sequence is constructed according to the ratio of the current value to the initial value. The variance of the exponential change sequence is calculated, and it is analyzed whether the time increment change rate shows an accelerating trend. The third-order difference is used to fit the exponential change rate to judge whether there is a critical transition point in the accelerating trend. If the variance increment exceeds the set threshold of 1.5×10³ (kA / m)² / s², it is defined as the accelerating region of the weakening index, and the exponential slope, fluctuation range and duration of this region are extracted to form the coercivity weakening index accelerated variance data for subsequent coupling analysis operations. Finally, in the process of analyzing the demagnetization coupling strength of the thermal-magnetic effect, the equal difference data of the reverse magnetic flux density distribution obtained in step S224 and the coercivity weakening index accelerated variance data in the current step are subjected to time-domain synchronization processing. The maximum time offset tolerance is set to ±30 ms, and the coupling strength is calculated for each pair of synchronized data in the window alignment manner. The magnetic flux density equal difference value is used as the transverse magnetic excitation variable, and the coercivity weakening acceleration variance is used as the longitudinal thermal weakening index. The weighted average of their products is calculated as the initial value of the coupling strength. The weight of the magnetic flux density in the weighting coefficient is set to 0.65, and the weight of the coercivity weakening index accelerated variance is set to 0.35. Then, the moving average and peak extraction operations are performed on the whole-region data to form the thermal-magnetic effect demagnetization coupling strength data. The output parameters include data such as the coupling peak intensity, action time interval, magnetic flux directionality and thermal weakening response index.

[0035] Step S24 includes the following steps: Step S241: Based on the non-linear regression data of the demagnetization intensity, perform the mapping of the air-gap magnetic field weakening gradient to obtain the air-gap magnetic field weakening gradient data; Step S242: Perform the phased dynamic evolution trend analysis on the air-gap magnetic field weakening gradient data to obtain the phased weakening evolution trend data of the air-gap magnetic field; Step S243: Based on the phased weakening evolution trend data of the air-gap magnetic field, perform the back electromotive force linear drift analysis to obtain the back electromotive force linear drift data; Step S244: Based on the phased weakening evolution trend data of the air-gap magnetic field and the back electromotive force linear drift data, perform the analysis of the proportional relationship of the motor output torque loss to obtain the proportional relationship of the output torque loss.

[0036] In the embodiments of the present invention, based on the non-linear regression data of the demagnetization intensity of the permanent magnet obtained, the spatial coordinates of each regression point and the corresponding demagnetization intensity values are extracted. Taking the motor radial profile coordinate system as the reference system, a Cartesian polar coordinate transformation rule is set with the magnetic pole boundary as the starting point. For the air gap position corresponding to each magnetic pole, the magnetic field intensity is collected at intervals of 0.1 mm in the radial direction starting from the surface of the permanent magnet, and the maximum distance of the sampling points is set to 1.5 mm. The differential operation is performed on the magnetic field intensity obtained at each sampling point within a unit time to calculate the gradient value of the magnetic field intensity in the radial direction. Then, the gradient value and the non-linear regression data of the demagnetization intensity are subjected to mapping and superposition processing according to the spatial mapping relationship to construct a two-dimensional air gap magnetic field weakening distribution map. Further, the Laplace difference method is used to calculate the degree of change of the magnetic field gradient around each sampling point, thereby forming air gap magnetic field weakening gradient data, which includes content such as sampling point position index, magnetic flux density gradient value, local magnetic field difference value, pole pitch normalized position coordinates, and gradient peak displacement direction. By setting the continuous sampling time period to 2000 ms and grouping the air gap magnetic field weakening gradient data according to the time period, a time series evolution vector is constructed using indicators such as the gradient mean value, gradient range, and local minimum distribution density of each group of data. The third-order moving average filter is applied to the evolution vector, and the transfer frequency and the change amplitude of the central position of the gradient extreme points within each time window are calculated. The trend line fitting method is used to fit the time transfer trajectory of the extreme points, and the second-order difference of the fitting curve slope is used to determine whether there are stage change inflection points. Further, the entire time series is divided into multiple evolution stages according to the inflection point distribution. Statistical parameters such as the local magnetic field gradient decreasing rate, evolution stable time interval, and change rate range are extracted within each stage, thereby obtaining air gap magnetic field stage weakening evolution trend data, which includes content such as evolution stage number, duration of each stage, change directionality index, average decreasing rate, and range of range. Using the air gap magnetic field stage weakening evolution trend data obtained in step S242, by synchronously collecting the terminal electromotive force waveforms of the drive inverter output voltage and the motor winding terminal voltage, the sampling frequency is set to 10 kHz, the integral method is used to restore the back electromotive force waveform, and the periodic regression analysis is performed on the time interval between the zero crossing point and the peak point of the waveform, and then the peak back electromotive force voltage and its corresponding period parameters within each air gap magnetic field weakening evolution stage are extracted. The change trend of the average back electromotive force is calculated within each evolution stage, the change trend is linearly fitted, and the fitting slope is extracted as the linear drift amount of this stage. The difference in the back electromotive force change between each stage is compared to determine whether it shows a linear drift trend. Further, by performing time alignment processing on the drift trend and the magnetic field gradient change trend, back electromotive force linear drift data is formed, which includes content such as the average back electromotive force of each stage, linear drift direction, drift speed, maximum drift amplitude, and waveform period stability index.The back electromotive force linear drift data obtained in step S243 and the air-gap magnetic field stage weakening evolution trend data obtained in step S242 are coupled and analyzed. First, the time synchronization points of the back electromotive force peak change and the air-gap magnetic field gradient change are extracted in each evolution stage to construct a voltage-magnetic gradient relationship sequence. In this relationship sequence, based on the air-gap magnetic field gradient change value corresponding to the unit back electromotive force change, a torque influence factor sequence is constructed. By analyzing the actual load torque change situation corresponding to the motor in this stage, the average value of the iq current component in each time window is extracted from the torque current feedback data of the vector control system, the torque drop value is calculated and a proportional operation is performed with the back electromotive force drop value, so as to obtain the equal-proportion relationship value between the output torque loss and the air-gap magnetic field weakening, and finally form the output torque loss equal-proportion relationship data. This data includes information such as the equal-proportion coefficient value, the action stage number, the back electromotive force change amplitude, the torque drop rate, and the stage duration, and is used to comprehensively judge the direct impact of the air-gap magnetic field weakening process on the performance of the electric vehicle drive system and its controllability analysis.

[0037] Step S3 includes the following steps: Step S31: Perform logical learning on the output torque loss equal-proportion relationship to obtain torque loss equal-proportion learning data; Step S32: Perform convolution processing on the permanent magnet demagnetization intensity equal-fitting data to obtain demagnetization intensity equal-fitting convolution data; Step S33: Design a motor field weakening control strategy based on the torque loss equal-proportion learning data and the demagnetization intensity equal-fitting convolution data to obtain a motor field weakening control strategy; Step S34: Design motor control firmware based on the motor field weakening control strategy to obtain motor control firmware; send the motor control firmware to the electric vehicle control terminal to execute the electric vehicle control method.

[0038] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes: Step S31: Perform logical learning on the output torque loss equal-proportion relationship to obtain torque loss equal-proportion learning data; In the embodiment of the present invention, by constructing a multi-dimensional vector feature matrix that outputs an equal-proportion relationship of torque loss, using a discrete point distribution law learning method based on an adaptive piecewise least squares fitting algorithm, the output torque loss data is divided into multiple sub-intervals according to the operating condition sequence. In each interval, sliding window convolution is applied for local feature extraction. Combining with the first-order difference trend analysis method, the derivative value of each group of output torque loss values is extracted and a trend label is generated. Then, the piecewise fitting accuracy optimized by the Bayesian information criterion is used to perform feature correlation measurement processing through the change rate of the output torque loss with respect to the demagnetization degree of the permanent magnet, so as to extract the equal-proportion response interval of the torque loss with respect to the demagnetization degree of the motor and archive it as an equal-proportion learning data set. The length of the data interval is set to 50 sets of sampling points, the difference window is set to 5 points, and the convolution kernel size is set to 3.

[0039] Step S32: Perform convolution processing on the equal-fitting data of the permanent magnet demagnetization intensity to obtain the convolution data of the equal-fitting of the demagnetization intensity. In the embodiment of the present invention, based on the equal-fitting data of the permanent magnet demagnetization intensity, the non-equidistant discrete data is uniformly transformed to the standard-spacing coordinate axis by using the orthogonal polynomial interpolation method. Subsequently, one-dimensional fast Fourier transform is performed on the interpolated data to remove the high-frequency components in the noise. Then, the bilateral filtering function is used for edge-preserving smoothing processing to ensure that the mutation points and edge trends are retained. After that, one-dimensional convolution calculation is performed on the processed data sequence. The convolution kernel used is a weight vector with a weighted gradient attenuation coefficient distribution. The convolution length is taken as 15 points and the step size is 1 point. The sliding average and local maximum value sequences are output for each group of convolution results. By comparing the change in the standard deviation of the convolved signal and the original demagnetization data, the effective convolution response region is screened, and the demagnetization intensity convolution response set is constructed. Finally, the convolution data of the equal-fitting of the demagnetization intensity is output, where the orthogonal interpolation uses the cubic Legendre polynomial basis, and the filter parameters are set to a spatial radius of 2 and an intensity radius of 10.

[0040] Step S33: Design the field weakening control strategy of the motor according to the equal-proportion learning data of the torque loss and the convolution data of the equal-fitting of the demagnetization intensity to obtain the field weakening control strategy of the motor. In the embodiment of the present invention, the torque loss equal-proportion learning data in step S31 and the demagnetization intensity equal-fitting convolution data in step S32 are used as input vectors, and the change trends of the two types of data are jointly fitted through a two-way difference analysis method. First, an output torque loss gradient map is constructed and mapped to the peak and valley regions of the demagnetization intensity convolution response sequence. By constructing a local minimum energy coupling path, a dynamic response mapping model is established based on the maximum coupling gradient point pair of the two types of data. An inter-segment response increasing matching algorithm based on continuous inflection point function recognition is used to identify the critical coupling point between demagnetization and torque response. Finally, a demagnetization-torque field weakening response model is established through dynamic window regression fitting, and this model is used to derive reverse control parameters to generate a voltage-speed adjustment factor group for different demagnetization degrees. This factor group includes the upper limit value of the voltage, the field weakening adjustment ratio, the excitation frequency parameter, and the duty cycle setting value under the current load state, totaling 12 sets of field weakening control strategy parameters.

[0041] Step S34: Design a motor control firmware based on the motor field weakening control strategy to obtain the motor control firmware; send the motor control firmware to the electric vehicle control terminal to execute the electric vehicle control method.

[0042] In the embodiment of the present invention, based on the field weakening control strategy parameter group generated in step S33, a control parameter configuration table for the target motor is constructed, and the reference voltage input channel of the PWM modulation module is directly configured through the control instruction register. In the control firmware design, a real-time control flowchart with a state machine as the core is adopted. The voltage regulation logic under different working conditions is defined by setting state transition conditions. The state transition basis includes indicators such as current load mutation rate, voltage threshold trigger, and temperature critical warning. Four control states are set to correspond to conventional voltage regulation, the start of field weakening control, the field weakening enhancement mode, and the demagnetization limit protection mode respectively. The output waveform control logic based on triangular carrier PWM modulation technology is integrated in the firmware. The sensor input data such as the motor speed, current, and voltage are periodically read. Parameter matching is performed every 10 ms and state jump checks are executed. The PWM duty cycle output value is refreshed every 20 ms to ensure the smoothness of the transition and the accuracy of voltage regulation response during the speed regulation process. Finally, the firmware is uploaded to the Flash storage area of the main control chip of the electric vehicle control terminal through the SPI bus and instruction loading and operation startup are performed.

[0043] Step S33 includes the following steps: Step S331: Perform differential growth rate analysis on the torque loss equal-proportion learning data to obtain torque loss differential growth rate data; Step S332: Perform intensity segmented response fitting on the torque loss differential growth rate data according to the demagnetization intensity equal-fitting convolution data to obtain segmented demagnetization coupling response data; Step S333: Perform derivation processing on the segmented demagnetization coupling response data to obtain the flux vector offset derivation data; Step S334: Perform backstepping processing on the dynamic boundary of the motor voltage based on the flux vector offset derivation data to obtain the voltage dynamic boundary backstepping control data; Step S335: Design the field weakening control strategy of the motor based on the flux vector offset derivation data and the voltage dynamic boundary backstepping control data to obtain the field weakening control strategy of the motor.

[0044] In the embodiments of the present invention, the five-point sliding difference method is used to perform first-order derivative difference processing on the torque loss equal-proportion learning data. By setting the data interval of each group to 5 ms, the numerical growth amounts between each sample point are extracted according to the time series, and a difference growth rate sequence is constructed with the difference between the growth amount and the previous data point. The moving average method is used in this process to eliminate the interference of local abnormal fluctuations. Extreme value elimination and three-times standard deviation de-dispersion processing are performed on each group of difference results. After elimination, the effective data length is 96% of the original data. Then, a local growth rate subsequence is constructed with a sliding window of a fixed length of 30, and the ratio of the maximum growth rate to the average growth rate within each window is calculated to evaluate the degree of instantaneous change mutation, thereby forming a composite difference growth rate feature vector including difference growth rate, window mean, range factor, and mutation coefficient. This vector group is the torque loss difference growth rate data, and the processed data satisfies the continuous time domain distribution law and has a segmental response trend structure. Taking the torque loss difference growth rate data obtained in step S331 as the input, combined with the demagnetization intensity and other equal-fitting convolution data, a segmental response fitting operation is performed. First, the entire time series is divided into intervals according to the convolution response peak and trough index positions in the demagnetization intensity data. The length of each interval is controlled between 100 and 150 data points. Within each interval, the least squares fitting process is performed on the difference growth rate data using the polynomial regression method, and the polynomial order is 3. When the fitting residual is less than 0.02, the current segment model is retained; otherwise, a correction factor is introduced to re-weight the convolution data weights. Then, the envelope extraction is performed on the fitting curve of each section, the offset trend between the upper and lower envelope lines is calculated, and the response increment ratio of each section of the fitting function under the same demagnetization degree is statistically analyzed. This ratio is linearly related to the convolution response intensity and is used to define the demagnetization response coupling weight of this section. Finally, the fitting residual distribution characteristics and response coupling gradient factors are extracted within all sections as the feature set output to form the segmental demagnetization coupling response data. Based on the segmental demagnetization coupling response data formed in step S332, the flux direction angle interpolation method is used to deduce the change trend of the main flux vector direction offset of the motor. First, the flux direction under the rated state of the motor is used as the reference direction to set the 0-degree reference point. The motor phase current and magnetomotive force intensity values corresponding to the maximum coupling response points are extracted within each demagnetization response section. The relative flux synthesis vector direction is calculated using these values. According to the equivalent projection relationship of the flux vectors in the three-phase symmetric system, the vector included angle cosine method is used to calculate the current flux direction offset angle, and the offset angle is stored in degrees. The B-spline interpolation is performed on the entire flux angle sequence to generate a smooth angle change curve. Then, the first-order derivative of the curve is processed to extract the flux offset speed, and the offset mutation points higher than 15 degrees / second are marked. Parameters such as the average offset angle, maximum offset rate, and offset acceleration range are statistically analyzed and combined to form the flux vector offset amount deduction data.Based on the derived data of the flux vector offset obtained in step S333, the boundary value of the motor back-electromotive force is solved by reversely solving the change trend of the flux direction offset angle, corresponding to the voltage dynamic control limit, and the boundary of the motor voltage control space is pushed back by time inversion. Specifically, the offset angle mutation point is taken as the starting point, and it is traced back to the stable range of the flux direction. By back-pushing the voltage extreme point, duty cycle threshold and bus voltage value at that time, a voltage-flux boundary coupling map is constructed. The map is used to determine the upper and lower limit values of the voltage adjustment range allowed under different flux offsets. The boundary value is set based on preventing entering the magnetic saturation zone and maintaining the back-electromotive force safety range. Finally, the voltage dynamic boundary control factor is calculated, including the voltage upper limit, voltage lower limit, current limit gain coefficient, voltage response time constant, voltage climb rate parameter and the adjustment step corresponding to each offset angle, to form the voltage dynamic boundary pushback control data. The flux vector offset derivation data obtained in step S333 and the voltage dynamic boundary pushback control data obtained in step S334 are jointly analyzed and processed. By constructing a two-dimensional flux-voltage regulation matrix model, a bidirectional interpolation operation is performed according to the relationship between the flux offset angle and the voltage upper and lower limits, so as to output the corresponding voltage regulation strategy under the given flux change rate condition. The strategy includes the PWM duty cycle change range, the voltage gain adjustment curve, the weak magnetic entry threshold and the weak magnetic release threshold criterion. The gradient descent optimization algorithm is further used to perform local search optimization on each strategy parameter to ensure that the optimal energy consumption control strategy is obtained under the minimum flux offset rate. Subsequently, the strategy mode for switching between different working points is defined in the form of a state transfer mapping table. The forward control priority and the backward control redundant path rules are combined to form a motor weak magnetic control strategy. The strategy is used for the real-time control logic loading and feedback calibration standard formulation of the weak magnetic segment in the actual firmware.

[0045] The present invention also provides an electric vehicle control system for executing the electric vehicle control method as described above, the electric vehicle control system comprising: The current fluctuation analysis module is used to obtain the background control terminal authority of the electric power intelligent inspection electric vehicle; based on the background control terminal authority, the motor operating state of the electric vehicle under complex road conditions is extracted, and then the motor current fluctuation analysis is performed to obtain the current fluctuation change data of the working condition conversion; The output torque loss analysis module is used to perform equal-amount fitting of the motor permanent magnet demagnetization intensity according to the current fluctuation change data of the working condition conversion, and obtain the equal-amount fitting data of the permanent magnet demagnetization intensity; based on the equal-amount fitting data of the permanent magnet demagnetization intensity, perform proportional relationship analysis of the motor output torque loss, and obtain the proportional relationship of the output torque loss; The weak magnetic control strategy design module is used to design the motor weak magnetic control strategy according to the equal amount fitting data of the permanent magnet demagnetization intensity and the proportional relationship of the output torque loss, so as to obtain the motor weak magnetic control strategy; design the motor control firmware based on the motor weak magnetic control strategy to obtain the motor control firmware; send the motor control firmware to the electric vehicle control terminal to execute the electric vehicle control method.

[0046] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the widest scope consistent with the principles and novel features invented herein.

Claims

1. An electric vehicle control method, characterized in that, It includes the following steps: Step S1: Obtain the permission of the background control terminal of the electric vehicle for intelligent power inspection; Based on the permission of the background control terminal, extract the motor operating state among complex road conditions of the electric vehicle, and then analyze the motor current fluctuation change to obtain the current fluctuation change data of working condition conversion; Step S2: Perform an equivalent fitting of the demagnetization intensity of the motor permanent magnet based on the current fluctuation change data of working condition conversion to obtain the equivalent fitting data of the demagnetization intensity of the permanent magnet; Analyze the proportional relationship of the motor output torque loss based on the equivalent fitting data of the demagnetization intensity of the permanent magnet to obtain the proportional relationship of the output torque loss; Step S3: Design the field weakening control strategy of the motor according to the equivalent fitting data of the demagnetization intensity of the permanent magnet and the proportional relationship of the output torque loss to obtain the field weakening control strategy of the motor; Design the motor control firmware based on the field weakening control strategy of the motor to obtain the motor control firmware; Send the motor control firmware to the control terminal of the electric vehicle to execute the electric vehicle control method.

2. The electric vehicle control method according to claim 1, wherein Step S1 includes the following steps: Step S11: Obtain the permission of the background control terminal of the electric vehicle for intelligent power inspection; Step S12: Extract the motor operating state among complex road conditions of the electric vehicle based on the permission of the background control terminal to obtain the motor operating state data among complex road conditions of the electric vehicle; Step S13: Analyze the motor working condition conversion frequency state of the motor operating state data among complex road conditions of the electric vehicle to obtain the motor working condition conversion frequency state data; Step S14: Analyze the motor current fluctuation change of the motor working condition conversion frequency state data to obtain the current fluctuation change data of working condition conversion.

3. The electric vehicle control method according to claim 1, wherein Step S2 includes the following steps: Step S21: Analyze the continuity mutation intensity of the current fluctuation change data of working condition conversion to obtain the current continuous mutation intensity data; Step S22: Perform an equivalent fitting of the demagnetization intensity of the motor permanent magnet according to the current continuous mutation intensity data to obtain the equivalent fitting data of the demagnetization intensity of the permanent magnet; Step S23: Perform a non-linear regression analysis on the equivalent fitting data of the demagnetization intensity of the permanent magnet to obtain the non-linear regression data of the demagnetization intensity; Step S24: Analyze the proportional relationship of the motor output torque loss based on the non-linear regression data of the demagnetization intensity to obtain the proportional relationship of the output torque loss.

4. The electric vehicle control method according to claim 3, characterized in that, Step S21 includes the following steps: Step S211: Calculate the variance of the relative width of the fluctuation peak between different working condition conversions of the current fluctuation change data of working condition conversion to obtain the variance of the relative width of the fluctuation peak between different working condition conversions; Step S212: Calculate the increasing slope of the current peak width between different working condition conversions of the current fluctuation change data of working condition conversion according to the variance of the relative width of the fluctuation peak to obtain the increasing slope of the current peak width between different working condition conversions; Step S213: Perform an increasing inflection point continuity local range analysis based on the increasing slope of the current peak width and the variance of the relative width of the fluctuation peak to obtain the current increasing inflection point continuity local range; Step S214: Perform a multi-scale decomposition of the current mutation acceleration according to the current increasing inflection point continuity local range to obtain the multi-scale decomposition data of the mutation acceleration; Step S215: Analyze the continuity mutation intensity based on the local extreme difference of the current increasing inflection point continuity and the multi-scale decomposition data of the mutation acceleration to obtain the current continuous mutation intensity data.

5. The electric vehicle control method according to claim 4, characterized in that, Step S22 includes the following steps: Step S221: Deduce the reverse magnetic field intensity based on the current continuous mutation intensity data to obtain the reverse magnetic field intensity data; calculate the temperature rise rate ratio based on the current continuous mutation intensity data to obtain the temperature rise rate ratio. Step S222: Analyze the non-uniformity of the reverse magnetic field spatial distribution of the reverse magnetic field intensity data to obtain the reverse magnetic field spatial distribution non-uniformity data. Step S223: Identify the distortion distribution of the reverse magnetomotive potential of the reverse magnetic field intensity data according to the reverse magnetic field spatial distribution non-uniformity data to obtain the reverse magnetomotive potential distortion distribution data. Step S224: Calculate the equal difference of the reverse magnetic flux density distribution for the reverse magnetomotive potential distortion distribution data to obtain the reverse magnetic flux density distribution equal difference data. Step S225: Analyze the thermal-magnetic demagnetization coupling intensity according to the temperature rise rate ratio and the reverse magnetic flux density distribution equal difference data to obtain the thermal-magnetic demagnetization coupling intensity data. Step S226: Fit the equal amount of the permanent magnet demagnetization intensity according to the thermal-magnetic demagnetization coupling intensity data to obtain the equal amount fitting data of the permanent magnet demagnetization intensity.

6. The electric vehicle control method according to claim 5, characterized in that Step S225 includes the following steps: Obtain the temperature safety margin of the permanent magnet basic coercivity; identify the spatial temperature overlimit increment gradient of the temperature safety margin of the permanent magnet basic coercivity according to the temperature rise rate ratio to obtain the spatial temperature overlimit increment gradient data. Conduct a coercivity non-linear weakening convergence analysis on the temperature safety margin of the permanent magnet basic coercivity based on the spatial temperature overlimit increment gradient data to obtain the coercivity non-linear weakening convergence data. Conduct a weakening index acceleration variance analysis on the coercivity non-linear weakening convergence data to obtain the coercivity weakening index acceleration variance. Analyze the thermal-magnetic demagnetization coupling intensity according to the coercivity weakening index acceleration variance and the reverse magnetic flux density distribution equal difference data to obtain the thermal-magnetic demagnetization coupling intensity data.

7. The electric vehicle control method according to claim 6, wherein Step S24 includes the following steps: Step S241: Map the weakening gradient of the air-gap magnetic field based on the non-linear regression data of the demagnetization intensity to obtain the weakening gradient data of the air-gap magnetic field. Step S242: Analyze the stage dynamic evolution trend of the weakening gradient data of the air-gap magnetic field to obtain the stage weakening evolution trend data of the air-gap magnetic field. Step S243: Conduct an analysis of the linear drift of the back electromotive force based on the stage weakening evolution trend data of the air-gap magnetic field to obtain the linear drift data of the back electromotive force. Step S244: Analyze the proportional relationship of the motor output torque loss based on the stage weakening evolution trend data of the air-gap magnetic field and the linear drift data of the back electromotive force to obtain the proportional relationship of the output torque loss.

8. The electric vehicle control method according to claim 1, wherein Step S3 includes the following steps: Step S31: Conduct logical learning on the proportional relationship of the output torque loss to obtain the proportional learning data of the torque loss. Step S32: Conduct convolution processing on the equal amount fitting data of the permanent magnet demagnetization intensity to obtain the equal amount fitting convolution data of the demagnetization intensity. Step S33: Design the field-weakening control strategy of the motor based on the torque loss equal-proportion learning data and the demagnetization intensity equal-fitting convolution data to obtain the field-weakening control strategy of the motor; Step S34: Design the motor control firmware based on the field-weakening control strategy of the motor to obtain the motor control firmware; send the motor control firmware to the electric vehicle control terminal to execute the electric vehicle control method.

9. The electric vehicle control method according to claim 8, wherein, Step S33 includes the following steps: Step S331: Perform differential growth rate analysis on the torque loss equal-proportion learning data to obtain torque loss differential growth rate data; Step S332: Perform intensity segmented response fitting on the torque loss differential growth rate data according to the demagnetization intensity equal-fitting convolution data to obtain segmented demagnetization coupling response data; Step S333: Perform magnetic flux vector direction offset derivation on the segmented demagnetization coupling response data to obtain magnetic flux vector offset derivation data; Step S334: Perform motor voltage dynamic boundary backstepping based on the magnetic flux vector offset derivation data to obtain voltage dynamic boundary backstepping control data; Step S335: Design the field-weakening control strategy of the motor based on the magnetic flux vector offset derivation data and the voltage dynamic boundary backstepping control data to obtain the field-weakening control strategy of the motor.

10. An electric vehicle control system, characterized in that, For executing the electric vehicle control method as described in claim 1, the electric vehicle control system includes: A current fluctuation change analysis module, configured to obtain the background control terminal permission of the electric power intelligent inspection electric vehicle; extract the motor operating state among complex road conditions of the electric vehicle based on the background control terminal permission, and then perform motor current fluctuation change analysis to obtain working condition conversion current fluctuation change data; An output torque loss analysis module, configured to perform equal-fitting of the motor permanent magnet demagnetization intensity according to the working condition conversion current fluctuation change data to obtain permanent magnet demagnetization intensity equal-fitting data; analyze the equal-proportion relationship of the motor output torque loss based on the permanent magnet demagnetization intensity equal-fitting data to obtain the equal-proportion relationship of the output torque loss; A field-weakening control strategy design module, configured to design the field-weakening control strategy of the motor according to the permanent magnet demagnetization intensity equal-fitting data and the equal-proportion relationship of the output torque loss to obtain the field-weakening control strategy of the motor; design the motor control firmware based on the field-weakening control strategy of the motor to obtain the motor control firmware; send the motor control firmware to the electric vehicle control terminal to execute the electric vehicle control method.

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