New energy automobile motor fault monitoring method and system

Abstract

The invention relates to the technical field of new energy automobile motor fault monitoring, and discloses a new energy automobile motor fault monitoring method and system, and the method comprises the steps: obtaining a switch control signal of an inverter of a new energy automobile drive motor, and starting ultra-high frequency sampling in a time window according to the signal, three-phase current and voltage signals of the motor are collected for analysis, transient and high-frequency pulse signals generated by early insulation degradation of the motor can be effectively captured, and the limitation that a traditional monitoring system cannot recognize weak partial discharge due to insufficient sampling frequency or overlapping with background noise is overcome. By comparing an analysis result with an ideal waveform, transient abnormal characteristics of early insulation degradation can be accurately extracted, and normal transient interference is prevented from being misjudged as a fault.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of new energy vehicle motor fault monitoring, and particularly relates to a new energy vehicle motor fault monitoring method and system. BACKGROUND

[0002] In the power system of a new energy vehicle, the stable and reliable operation of the drive motor is crucial to the overall performance and driving safety of the vehicle. To this end, new energy vehicles are usually equipped with a motor monitoring system that collects key operating data such as stator winding temperature, three-phase current, voltage, and speed through sensors. The on-board control unit continuously processes these data and compares them with the preset health standards. Once the parameters consistently deviate from the normal range, the system will issue an alarm and record the fault code, so that timely measures can be taken. For example, current imbalance or harmonic abnormalities may indicate inter-turn short circuits in the winding, while excessively high temperatures may indicate cooling system failure or aging of the insulation material.

[0003] However, in actual application, the drive motor of a new energy vehicle may have initial weak insulation weaknesses caused by manufacturing defects. Such weaknesses have little impact on overall performance when the motor is initially operating normally, and the data collected by traditional monitoring systems also show that everything is normal. When the vehicle is operated under harsh conditions such as frequent high-power starting and stopping, acceleration and deceleration, for example, as a city logistics vehicle for a large number of short trips, frequent starting, stopping, accelerating, and decelerating can cause the three-phase winding current of the motor to rise sharply, generating strong electromagnetic force and causing rapid fluctuations in winding temperature. SUMMARY

[0004] The present application provides a new energy vehicle motor fault monitoring method and system, aiming to solve the problem that traditional monitoring systems cannot effectively identify and warn of early insulation degradation of the drive motor of a new energy vehicle, especially for complex fault modes such as weak, transient, and easily masked by background noise partial discharge activity and high-impedance intermittent short circuits.

[0005] In a first aspect, to solve the above technical problems, the present application provides a new energy vehicle motor fault monitoring method, comprising: obtaining a switch control signal of an inverter of a new energy vehicle drive motor; According to the switch control signal, the ultra-high frequency sampling in the starting time window is started, and the motor three-phase current and voltage signals are collected and analyzed; After comparing the analysis results with the ideal waveform, the transient abnormal characteristics of early insulation degradation are extracted; obtaining the motor operating conditions corresponding to the transient abnormal characteristics; According to the motor operating conditions, the transient abnormal characteristics are weighted and accumulated for evaluation; According to the result of the cumulative evaluation, an early motor fault warning is issued.

[0006] Preferably, after comparing the analysis result with the ideal waveform, the transient abnormal features of early insulation degradation are extracted, including: In the initial stage of the motor operation, the transient current and voltage waveforms under different working conditions are collected, the waveforms under each working condition are averaged, and the initial health reference baseline waveform is established; For each newly captured transient current and voltage waveform, a preliminary fault judgment is made to ensure that it does not contain obvious burst fault features; If it is determined that the current health state is normal, the corresponding adaptive reference waveform is updated; The instantaneous deviation between the newly captured transient current and voltage waveform and the current adaptive reference waveform is calculated; A filtered deviation signal is generated according to the instantaneous deviation; According to the deviation signal, pulses with an amplitude exceeding a preset instantaneous threshold and a very short duration are identified; For each detected pulse, the peak amplitude, rise time, pulse width, and high-frequency energy content are extracted as pulse features; The pulse features are compared with the pre-established partial discharge feature signature to determine whether it is a real partial discharge event, so as to extract the transient abnormal features of early insulation degradation.

[0007] Preferably, the transient abnormal features are weighted and cumulatively evaluated according to the motor operating conditions, including: The insulation resistance value of the motor winding is monitored; When the insulation resistance value decreases and the decrease rate exceeds a preset decrease threshold, it is determined that the motor insulation material enters an accelerated aging stage; In the accelerated aging stage, the weighting coefficient of the transient abnormal features is dynamically adjusted according to the current motor operating conditions; When the temperature of the motor winding is higher than a preset temperature threshold or the load current of the motor is higher than a preset current threshold, the corresponding weighting coefficient is increased; According to the total operating time of the motor, the reference value of the weighting coefficient is periodically adjusted; Based on the dynamically adjusted weighting coefficient and the reference value, the transient abnormal features are weighted and cumulatively evaluated.

[0008] Preferably, according to the result of the cumulative evaluation, an early motor fault warning is issued, including: according to the result of the cumulative evaluation, the motor fault is preliminarily classified to obtain a fault nature description; According to the driving mode of the current vehicle and the driving habit of the driver, the pre-warning trigger threshold or the duration of pre-warning trigger is dynamically adjusted; When the result of the cumulative evaluation reaches the pre-warning trigger threshold or the duration, a first-level pre-warning information is issued through a vehicle-mounted display screen, voice broadcast or vibration feedback, and the first-level pre-warning information contains a description of the nature of the fault; If the result of the cumulative evaluation continues to rise and is not handled within a preset time, a second-level pre-warning information is issued, and the second-level pre-warning information contains a description of the nature of the fault and a preliminary countermeasure suggestion; According to the current driver identity and historical pre-warning response record, the repetition frequency or intensity of the pre-warning information is adjusted.

[0009] Preferably, the preliminary classification of the motor fault according to the result of the cumulative evaluation to obtain the description of the nature of the fault includes: The transient abnormal feature contained in the cumulative evaluation result is subjected to multi-dimensional analysis to obtain a transient abnormal feature analysis result; The vibration signal collected by the motor shell vibration sensor is obtained; The vibration signal is subjected to frequency spectrum analysis to extract features related to mechanical faults to obtain mechanical fault features; The fusion feature is obtained by fusing the transient abnormal feature analysis result and the mechanical fault feature; the matching result is obtained by matching the fusion feature with a preset composite fault feature library; and the description containing the nature of the composite fault is generated according to the matching result.

[0010] Preferably, the generation of the description containing the nature of the composite fault according to the matching result includes: According to the matching result, the fault type, severity, possible impact and suggested preliminary inspection direction are structured and coded; The identity of the driver currently receiving the pre-warning information is identified; According to the driver identity, a corresponding description strategy is selected from a preset description strategy library, and the description strategy includes a professional term usage level, a detail level and a focus point emphasis; The structured and coded fault information is fused with the description strategy to customize the description of the nature of the composite fault for the current driver group.

[0011] Preferably, the generation of the description containing the nature of the composite fault according to the matching result includes: According to the matching result, the fault type, severity, possible impact and suggested preliminary inspection direction are structured and coded; acquire the current vehicle-mounted display device type and display capability parameters, the display capability parameters including display screen size, resolution, color depth, and supportable font size range; select a corresponding display optimization strategy from a preset display optimization strategy library according to the display device type and the display capability parameters, the display optimization strategy including information priority sorting, text truncation rules, icon or graphical representation conversion rules; fuse the structured coded fault information with the display optimization strategy to generate the composite fault property description customized for the current vehicle-mounted display device.

[0012] Preferably, the early motor fault warning is issued according to the result of the cumulative evaluation, including: acquire the current running state of the vehicle, the running state including vehicle speed, gear, steering angle, brake pedal stroke, and driver's throttle pedal opening degree; determine whether the vehicle is in a high dynamic working condition according to the current running state of the vehicle, the high dynamic working condition including high-speed driving, emergency braking, or sharp turning; if the vehicle is in the high dynamic working condition, temporarily suppress the direct presentation of the warning information, and cache the warning information; when the vehicle is out of the high dynamic working condition or the driver's operation intensity is reduced to a preset safety level, issue a warning according to the cached warning information through an instrument panel indicator light, a central control screen text prompt, or a voice prompt; if the result of the cumulative evaluation indicates that the fault severity reaches the level of immediate parking, then no matter whether the vehicle is in the high dynamic working condition, the highest level warning is issued through forced voice broadcast and vibration feedback, and the vehicle power output is automatically reduced.

[0013] Preferably, the acquisition of the current running state of the vehicle includes: synchronously collect raw data from a wheel speed sensor, a transmission control unit, a steering angle sensor, a brake pedal position sensor, and a throttle pedal position sensor through a vehicle high-speed communication bus at a preset fixed period, and add accurate time stamps to each set of collected data and determine whether the data is abnormal; if so, acquire the abnormal type, and perform data repair based on a predictive filling method of historical data trends or a weighted average method of adjacent valid data.

[0014] In a second aspect, the application provides a new energy vehicle motor fault monitoring system, including: a detection end for acquiring switch control signals of an inverter of the new energy vehicle drive motor; The processing end is configured to start ultra-high frequency sampling within a time window according to the switch control signal, collect motor three-phase current and voltage signals and perform analysis, extract transient abnormal characteristics of early insulation degradation by comparing the analysis result with an ideal waveform, obtain motor operation conditions corresponding to the transient abnormal characteristics, and perform weighted cumulative evaluation on the transient abnormal characteristics according to the motor operation conditions. The early warning end is configured to issue an early fault warning of the motor according to a result of the cumulative evaluation.

[0015] Compared with the prior art, the application can effectively capture transient and high-frequency pulse signals generated by early insulation degradation of the motor by obtaining a switch control signal of an inverter of the motor of the new energy vehicle and starting ultra-high frequency sampling within a time window according to the signal, collecting motor three-phase current and voltage signals and performing analysis, overcoming the limitation that the traditional monitoring system cannot identify weak partial discharge due to insufficient sampling frequency or overlapping with background noise. By comparing the analysis result with an ideal waveform, the transient abnormal characteristics of early insulation degradation can be accurately extracted, and false judgment of normal transient interference as a fault is avoided. Further, the method obtains motor operation conditions corresponding to the transient abnormal characteristics, and performs weighted cumulative evaluation on the transient abnormal characteristics according to the operation conditions, so that the fault evaluation result can fully consider the actual operation state and stress level of the motor under different conditions, improving the accuracy and reliability of fault judgment. Finally, according to the result of the cumulative evaluation, an early fault warning of the motor is issued, early warning is realized at the initial stage of the fault when the performance of the motor has not been significantly affected, valuable time window is provided for timely maintenance and avoidance of serious faults, the problem of difficulty in identifying early, weak and intermittent faults in the prior art is effectively solved, and the operation reliability and safety of the motor of the new energy vehicle are significantly improved. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 is a new energy vehicle motor fault monitoring method flowchart provided by an embodiment of the application; Figure 2 is another new energy vehicle motor fault monitoring method flowchart provided by an embodiment of the application; Figure 3 is another new energy vehicle motor fault monitoring method flowchart provided by an embodiment of the application; Figure 4 is a new energy vehicle motor fault monitoring system structure schematic diagram provided by an embodiment of the application. DETAILED DESCRIPTION

[0017] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present application.

[0018] With reference to Figure 1 The new energy vehicle motor fault monitoring method provided by the embodiments of the present application provides a flow chart. The method comprises the following steps: S1, obtaining a switching control signal of an inverter of a new energy vehicle driving motor; S2, according to the switching control signal, starting ultra-high frequency sampling in a time window, collecting motor three-phase current and voltage signals and performing analysis; S3, after comparing the analysis result with an ideal waveform, extracting transient abnormal characteristics of early insulation degradation; S4, obtaining a motor operating condition corresponding to the transient abnormal characteristics; S5, according to the motor operating condition, performing weighted cumulative evaluation on the transient abnormal characteristics; S6, according to the result of the cumulative evaluation, issuing an early motor fault warning.

[0019] The "new energy vehicle driving motor" mentioned in the present application generally refers to an electric motor used to drive a new energy vehicle to travel. The types of the motor can include permanent magnet synchronous motors, asynchronous motors, etc. These motors convert the direct current of a battery into alternating current through an inverter to drive the motor to run. The "inverter" is a key component in the power system of a new energy vehicle, responsible for controlling the speed and torque of the motor. The switching devices (such as IGBT) inside the inverter generate the required alternating voltage and current waveform through high-frequency switching action. The "switching control signal" is an electrical signal that controls the conduction and turn-off of these switching devices, and its frequency is usually between several kilohertz and tens of kilohertz.

[0020] The new energy vehicle motor fault monitoring method proposed in the present application is focused on the identification and evaluation of transient abnormal characteristics caused by early insulation degradation of the motor. The method first obtains the switching control signal of the inverter of the new energy vehicle driving motor. The switching control signal can be obtained directly from the output port of the inverter control unit, or read through the internal communication bus (such as CAN bus) of the vehicle. For example, an oscilloscope or a high-speed data acquisition card can be directly connected to the PWM signal output end on the inverter control board to obtain the switching control signal in real time.

[0021] Subsequently, according to the switch control signal, ultra-high frequency sampling within the time window is started to collect motor three-phase current and voltage signals and perform analysis. Ultra-high frequency sampling refers to a sampling frequency much higher than the motor fundamental frequency and the inverter switching frequency, usually reaching megahertz level, to ensure that transient pulse signals of nanoseconds or microseconds can be captured. For example, a data acquisition card with high sampling rate (such as 10 MS / s or higher) and high resolution (such as 16 bits or higher) can be used, combined with high-bandwidth current sensors and voltage sensors, to synchronously collect the current and voltage of the motor three-phase winding. The start of the time window can be synchronized with a specific edge (rising or falling) of the inverter switching signal to ensure accurate sampling at or near the switching action, effectively filtering out or distinguishing inverter switching noise.

[0022] Next, after comparing the analysis results with ideal waveforms, the transient abnormal features of early insulation degradation are extracted. Ideal waveforms can be obtained by collecting and averaging under different operating conditions of the motor in a healthy state, serving as a reference benchmark. The deviation between the analysis results and the ideal waveforms, especially the high-frequency components, can indicate potential insulation degradation. For example, signal processing techniques such as wavelet transform, Fourier transform, or Hilbert-Huang transform can be used to perform frequency domain or time-frequency domain analysis on the collected current and voltage signals to identify transient high-frequency pulses related to partial discharge. These pulses usually have extremely short duration, fast rising edge, and specific spectral characteristics.

[0023] Further, the motor operating conditions corresponding to the transient abnormal features are obtained. Motor operating conditions include but are not limited to motor speed, load current, winding temperature, ambient temperature, vehicle speed, accelerator pedal opening, etc. These operating condition information can be obtained through the vehicle's sensor network and control unit. For example, real-time data such as motor speed, torque, current, voltage, temperature, etc. output by the motor controller (MCU) can be read through the CAN bus.

[0024] Then, according to the motor operating conditions, the transient abnormal features are weighted and cumulatively evaluated. Different operating conditions have different effects on insulation aging, for example, harsh conditions such as high temperature, high load, or frequent start-stop can accelerate insulation degradation. Therefore, when evaluating transient abnormal features, different weights need to be given according to the current motor operating conditions. For example, when the motor is in a high load or high temperature state, the detected transient abnormal features should be given higher weights to reflect their greater contribution to insulation degradation. Cumulative evaluation can be achieved by integrating or summing the weighted transient abnormal feature values, reflecting the cumulative effect of insulation degradation.

[0025] Finally, based on the results of the cumulative assessment, an early motor failure warning is issued. When the results of the cumulative assessment reach a pre-set threshold, it indicates that the motor insulation has deteriorated to a certain extent and needs attention. The warning can be implemented in various ways, such as displaying warning information on the vehicle display screen, reminding the driver through voice broadcast, or flashing the instrument panel indicator light. The level of warning can be graded according to the severity of the cumulative assessment results, so that the driver or maintenance personnel can take appropriate measures.

[0026] The new energy vehicle motor fault monitoring method proposed in this application realizes ultra-high frequency sampling of motor three-phase current and voltage signals through accurate synchronization of inverter switch control signals, thereby effectively capturing early insulation deterioration transient abnormal characteristics that are difficult to identify by traditional methods. The method compares the collected signals with ideal waveforms and uses advanced signal processing techniques to extract transient high-frequency pulses related to partial discharge. These pulses usually have extremely short duration, fast rising edge and specific spectral characteristics, which are important indicators of early insulation deterioration.

[0027] Compared with the prior art, the innovation of the application lies in the fine identification of early insulation deterioration characteristics and the weighted cumulative assessment mechanism based on working conditions. Traditional monitoring systems often rely on monitoring of motor macroscopic parameters (such as average or effective values of current, voltage, and temperature), which usually do not change significantly in the early stage of insulation deterioration, resulting in delayed failure warning. By ultra-high frequency sampling, the application can capture nanosecond or microsecond level transient abnormal pulses, which are direct manifestations of partial discharge of small defects in insulation materials and sensitive indicators of early failure.

[0028] Specifically, after comparing the analysis results with ideal waveforms, the transient abnormal characteristics of early insulation deterioration can be extracted in the following way.

[0029] Reference Figure 2 , S3 includes: S31, in the initial stage of the motor running, collect transient current and voltage waveforms under different working conditions, average the waveforms under each working condition, and establish an initial health reference waveform; S32, make a preliminary fault judgment for each newly captured transient current and voltage waveform to ensure that it does not contain obvious burst failure characteristics; S33, if it is determined that the current health state is normal, update the corresponding adaptive reference waveform; S34, calculate the instantaneous deviation between the newly captured transient current and voltage waveform and the current adaptive reference waveform; S35, generate a filtered deviation signal according to the instantaneous deviation; S36, identifying pulses with amplitude exceeding a preset transient threshold and extremely short duration from the deviation signal; S37, extracting peak amplitude, rise time, pulse width and high-frequency energy content as pulse features for each detected pulse; S38, comparing the pulse features with pre-established partial discharge feature signatures to determine whether it is a real partial discharge event, so as to extract the transient abnormal features of early insulation degradation.

[0030] In the initial stage of operation of the motor, a stable and representative initial health reference waveform is established by collecting and averaging the transient current and voltage waveforms under different operating conditions. The reference waveform serves as the starting point for subsequent fault judgment and can reflect the electrical characteristics of the motor in a healthy state.

[0031] Further, a preliminary fault judgment is performed for each newly captured transient current and voltage waveform. The purpose of this step is to exclude obvious burst fault features that may be caused by external interference or transient operation, which are not caused by insulation degradation, so as to ensure that the focus of subsequent analysis is on early insulation degradation. If it is determined that the current waveform is a normal waveform in the current healthy state, it will be used to update the corresponding adaptive reference waveform. This adaptive mechanism enables the reference waveform to be dynamically adjusted as the motor normally ages or the operating environment changes slightly, improving the accuracy and robustness of fault detection.

[0032] Subsequently, the difference between the current motor state and the healthy state can be quantified by calculating the instantaneous deviation between the newly captured transient current and voltage waveforms and the current adaptive reference waveform. Based on the instantaneous deviation, a filtered deviation signal is generated to remove noise and irrelevant low-frequency components and highlight the ultra-high frequency signals related to early insulation degradation.

[0033] On this basis, pulses with amplitude exceeding a preset transient threshold and extremely short duration are identified from the deviation signal. These pulses are usually typical manifestations of partial discharge events. For each detected pulse, its peak amplitude, rise time, pulse width and high-frequency energy content are extracted as pulse features. These features are key parameters for characterizing partial discharge events.

[0034] Finally, the pulse features are compared with pre-established partial discharge feature signatures. The partial discharge feature signatures are established based on a large amount of experimental data and theoretical analysis to distinguish different types of partial discharge events. Through comparison, it can be determined whether the detected pulse is a real partial discharge event, so as to accurately extract the transient abnormal features of early insulation degradation.

[0035] The scheme of the present application can sensitively capture the weak transient anomaly caused by early insulation deterioration by establishing an adaptive health reference waveform and continuously monitoring the instantaneous deviation between the actual operation waveform of the motor and the reference waveform. Specifically, the establishment of the initial reference waveform provides a reliable reference point for subsequent deviation calculation. The adaptive updating mechanism ensures that the reference waveform can reflect the long-term evolution of the normal operation state of the motor, avoiding false positives. By filtering the deviation signal, the high-frequency pulse signal related to early insulation deterioration phenomena such as partial discharge can be effectively separated. Further, the features of these pulses (such as peak amplitude, rise time, pulse width, and high-frequency energy content) are extracted and compared with known partial discharge characteristic signatures, enabling the system to accurately identify real insulation deterioration events rather than other disturbances. This step-by-step refinement allows the transient anomaly characteristics of early insulation deterioration to be accurately and reliably extracted.

[0036] Through the above technical scheme, the present application can accurately extract the transient anomaly characteristics of early insulation deterioration of the motor of the new energy vehicle. Compared with simple waveform comparison, the present application introduces adaptive reference waveform, multi-dimensional pulse feature extraction, and comparison mechanism with partial discharge characteristic signature, significantly improving the sensitivity and accuracy of early fault detection. Especially when the motor insulation material has not yet appeared obvious macroscopic damage, through the fine analysis of weak transient anomaly signal, the potential insulation deterioration trend can be identified earlier, providing more reliable input for subsequent weighted cumulative evaluation and warning, thereby effectively avoiding the sudden damage of the motor caused by insulation failure, prolonging the service life of the motor, and improving the operation safety of the new energy vehicle.

[0037] In some embodiments of the present application described above, a scheme of weighted cumulative evaluation of transient anomaly characteristics according to the operating conditions of the motor is proposed. However, in actual application, the deterioration of the motor insulation material is a complex and dynamic process, and its progress speed is affected by many factors, such as the inherent aging characteristics of the insulation material, the operating temperature, the load current, and the cumulative running time. If the weighted cumulative evaluation mechanism fails to fully consider these dynamic changes, especially fails to identify and respond to the situation that the insulation material enters the accelerated aging stage, it may lead to inaccurate judgment of early insulation deterioration or untimely warning, thereby affecting the reliability of fault monitoring.

[0038] For this purpose, with reference to Figure 3 , S5 comprises: S51, monitoring the insulation resistance value of the motor winding; S52, when the insulation resistance value decreases and the decrease rate exceeds a preset decrease threshold, it is determined that the motor insulation material enters the accelerated aging stage; S53, during the accelerated aging phase, dynamically adjust the weighting coefficients of the transient abnormal features according to the current motor operating conditions; S54, when the temperature of the motor winding is higher than a preset temperature threshold or the load current of the motor is higher than a preset current threshold, increase the corresponding weighting coefficients; S55, periodically adjust the baseline values of the weighting coefficients based on the total operating time of the motor; S56, weight and accumulate the evaluation of the transient abnormal features based on the dynamically adjusted weighting coefficients and the baseline values.

[0039] Specifically, monitoring the insulation resistance value of the motor winding refers to obtaining the insulation resistance data of the motor winding in real time or periodically through a dedicated insulation resistance sensor or by applying a test voltage under certain conditions (such as during shutdown or low-voltage testing). The purpose is to obtain a macroscopic indicator of the insulation state of the motor.

[0040] When the insulation resistance value decreases and the decrease rate exceeds a preset decrease threshold, it is determined that the motor insulation material enters the accelerated aging phase, which means that the deterioration speed of the insulation material significantly accelerates, and the attention to the transient abnormal features should be correspondingly increased. The preset decrease threshold can be set according to factors such as motor type, design life, operating environment, and historical data analysis.

[0041] During the accelerated aging phase, the weighting coefficients of the transient abnormal features are dynamically adjusted according to the current motor operating conditions.

[0042] Specifically, when the temperature of the motor winding is higher than a preset temperature threshold or the load current of the motor is higher than a preset current threshold, the corresponding weighting coefficients are increased. This is because high temperature and high load current are the main stress factors that accelerate insulation aging, and the transient abnormal features detected under these conditions usually indicate more serious potential faults. The preset temperature threshold and current threshold can be determined according to the recommendations of the motor manufacturer, operating experience, or experimental data.

[0043] In addition, the baseline values of the weighting coefficients are periodically adjusted according to the total operating time of the motor. As the motor operating time increases, the insulation material will naturally age, so it is necessary to periodically increase the baseline values of the weighting coefficients to ensure that the sensitivity of the monitoring system to the aging motor remains at an appropriate level. Finally, the transient abnormal features are weighted and accumulated based on the dynamically adjusted weighting coefficients and the baseline values, resulting in more accurate and timely evaluation results.

[0044] The scheme of the present application can accurately identify the key period when the motor insulation material enters the accelerated aging stage by introducing the monitoring of the motor winding insulation resistance value and the judgment of its decline rate. In this critical period, combined with the motor operating conditions (such as temperature and load current), the weighting coefficient of transient abnormal characteristics is dynamically adjusted, so that under the most fragile or most stressed conditions of insulation, any slight transient abnormality can be given a higher weight, so that it can be identified by the cumulative evaluation system earlier. At the same time, by periodically adjusting the baseline value of the weighting coefficient to reflect the natural aging caused by the total running time of the motor, the long-term effectiveness of the evaluation is ensured. This multi-dimensional and adaptive weighted cumulative evaluation mechanism effectively makes up for the limitations of the traditional method that may not respond adequately to the dynamic aging process.

[0045] Through the above technical scheme, the accuracy and timeliness of motor early fault monitoring are significantly improved. By monitoring the insulation resistance in real time and identifying the accelerated aging stage, the system can more accurately focus on the high-risk period, avoiding the problem of over-sensitivity to non-critical periods or inadequate response to critical periods. Dynamic adjustment of the weighting coefficient allows the contribution of transient abnormal characteristics to be reasonably amplified under stress conditions such as high temperature and high load, effectively reducing the false negative rate. In addition, considering the adjustment of the baseline value by the total running time, the monitoring system can adapt to the entire life cycle of the motor, maintaining high sensitivity even in the case of natural aging of the motor. These improvements collectively make the motor early fault warning more reliable and meaningful, providing a solid foundation for predictive maintenance of new energy vehicles.

[0046] In some preferred embodiments, assuming a new energy vehicle motor has accumulated a long running time. The monitoring system continuously monitors the insulation resistance value of its winding. When it is detected that the insulation resistance value has decreased by 10% in the past month, and the decline rate exceeds the preset 5% decline threshold, the system determines that the motor insulation material has entered the accelerated aging stage. In this stage, if the motor is running under high-load conditions such as high-speed driving or climbing, causing the motor winding temperature to reach 130°C (higher than the preset 120°C temperature threshold) or the load current to reach 200A (higher than the preset 180A current threshold), the system will immediately increase the weighting coefficient of the transient abnormal characteristics collected at this time. For example, a transient pulse with an original weight of 1 may be increased to 1.8 under the conditions of accelerated aging and high temperature and high load. At the same time, since the total running time of the motor has reached 100,000 hours, the system will periodically increase the baseline value of the weighting coefficient by 10%. In this way, even weak transient abnormal characteristics will significantly increase their weight in cumulative evaluation after considering multiple factors such as insulation aging status, operating stress, and cumulative running time, thereby accelerating the reaching of the warning threshold and prompting the system to issue an early motor fault warning in a timely manner.

[0047] In some embodiments of the above-mentioned embodiments of the present application, an early motor failure warning is issued according to the results of cumulative evaluation. However, in the process of implementation, if only a single and generalized warning information is issued, the nature and severity of the failure may not be effectively conveyed, and the timeliness, accuracy and user acceptance of the warning may be insufficient due to the failure to consider the actual operating conditions of the vehicle and the individual differences of the driver. In view of this, the present application further proposes a method for issuing an early motor failure warning in a more refined and intelligent manner, aiming to improve the effectiveness of the warning and user experience.

[0048] Preferably, the early motor failure warning is issued according to the results of the cumulative evaluation, comprising: preliminarily classifying the motor failure according to the results of the cumulative evaluation to obtain a failure nature description; According to the driving mode of the current vehicle and the driving habits of the driver, the warning trigger threshold or the duration of the warning trigger is dynamically adjusted; When the results of the cumulative evaluation reach the warning trigger threshold or the duration, a first-level warning information is issued through the vehicle-mounted display screen, voice broadcast or vibration feedback, and the first-level warning information contains the failure nature description; If the results of the cumulative evaluation continue to rise and are not processed within a preset time, a second-level warning information is issued, and the second-level warning information contains the failure nature description and a preliminary countermeasure suggestion; According to the current driver identity and the historical warning response record, the repetition frequency or intensity of the warning information is adjusted.

[0049] Specifically, preliminarily classifying the motor failure means that according to the type, intensity and development trend of the transient abnormal features contained in the results of the cumulative evaluation, combining the preset failure mode library, the potential motor failure is classified into a specific failure type, such as insulation deterioration, partial discharge, early winding short circuit, etc. Thus, the failure nature description containing the failure type, possible causes and potential impact can be obtained.

[0050] Among them, the driving mode can be understood as the current operating state of the vehicle, such as economy mode, sports mode, comfort mode or custom mode, etc.; the driving habits of the driver refer to the driving behavior patterns formed by the driver in the long-term driving process, such as aggressive driving, smooth driving or conservative driving, etc. These habits can be analyzed and modeled through vehicle sensor data (such as throttle pedal opening, brake frequency, steering angle change rate, etc.). Through the comprehensive consideration of these factors, the warning trigger threshold or the duration of the warning trigger can be dynamically adjusted to ensure the timeliness of the warning and avoid unnecessary interference.

[0051] Further, the first-level warning information is usually presented in a relatively mild manner, such as a text prompt on the vehicle display screen or an indicator light on the instrument panel, aiming to remind the driver of the potential failure risk and containing a preliminary failure nature description so that the driver can understand the basic situation of the problem. If the failure condition continues to deteriorate, i.e., the cumulative evaluation result continues to rise, and is not handled within the preset time, a second-level warning information will be issued. The second-level warning information is usually presented in a more forceful and urgent manner, such as voice broadcast or vibration feedback, aiming to prompt the driver to take more active measures, in addition to containing the failure nature description, it also provides preliminary response suggestion, such as "it is recommended to go to the service station for inspection as soon as possible" or "please avoid high-load driving".

[0052] In addition, in order to improve the effectiveness of the warning, the system identifies the identity of the driver currently receiving the warning information, and combines the historical warning response records of the driver to adjust the repetition frequency or intensity of the warning information individually, for example, for the driver who often ignores the warning, the repetition frequency or intensity of the warning can be increased to ensure that he notices the warning information.

[0053] The scheme of the present application classifies the motor failure preliminarily, making the warning information more targeted; dynamically adjusts the warning trigger threshold or the duration of the warning trigger, ensuring that the warning is issued at the most appropriate time, avoiding misjudgment or delay due to premature or late warning; implements differentiated response to failures of different severity through a hierarchical warning mechanism, avoiding overreaction to minor failures while ensuring timely intervention for serious failures; and adjusts the repetition frequency or intensity of the warning information individually, taking into full account the individual differences and historical behavior of the driver, thereby improving the reception efficiency of the warning information and the willingness of the driver to respond. It is the synergistic effect of the above mechanisms that enables the motor early failure warning system to operate more intelligently and efficiently.

[0054] Through the above technical solutions, the present application can provide more refined, situational and personalized motor early failure warning, significantly improving the accuracy and effectiveness of the warning. This not only helps the driver to understand the nature and severity of the potential motor failure in a timely manner, so as to make more reasonable driving decisions or maintenance arrangements, but also effectively reduces the driving risks and user dissatisfaction caused by improper failure warning by avoiding unnecessary interference and improving the acceptance of the warning, thereby improving the overall safety and user experience of new energy vehicles.

[0055] In some embodiments of the present application described above, a motor early failure warning can be issued according to the cumulative evaluation result. Specifically, the motor failure is preliminarily classified according to the cumulative evaluation result, and a failure nature description is obtained, including: perform multi-dimensional analysis on the transient abnormal features contained in the cumulative evaluation result to obtain a transient abnormal feature analysis result; obtain a vibration signal collected by a motor shell vibration sensor; perform spectrum analysis on the vibration signal to extract features related to mechanical faults to obtain mechanical fault features; fuse the transient abnormal feature analysis result and the mechanical fault features to obtain fused features; match the fused features according to a preset composite fault feature library to obtain a matching result; generate a description containing a composite fault property according to the matching result.

[0056] Specifically, multi-dimensional analysis is performed on the transient abnormal features contained in the cumulative evaluation result, aiming to deeply mine the fault information contained in the transient abnormal features from different angles and levels. For example, statistical analysis, time-frequency analysis or wavelet analysis can be performed on parameters such as amplitude, frequency, duration and energy distribution of the transient abnormal features to reveal their variation laws in different time scales and frequency ranges. Thus, a transient abnormal feature analysis result can be obtained, which can more comprehensively reflect the specific forms and development trends of motor insulation degradation.

[0057] Meanwhile, in order to obtain more comprehensive fault information, a vibration signal collected by a motor shell vibration sensor can be obtained. The motor shell vibration sensor is usually installed at a specific position of the motor shell to monitor the mechanical vibration of the motor in real time. These sensors can be accelerometers, speed sensors or displacement sensors, and their selection depends on the vibration frequency range to be monitored and the sensitivity requirements. The collected vibration signal is an important indicator reflecting the internal mechanical state of the motor.

[0058] Further, spectrum analysis can be performed on the obtained vibration signal to extract features related to mechanical faults, thereby obtaining mechanical fault features. Spectrum analysis usually uses methods such as Fast Fourier Transform (FFT) to convert time-domain vibration signals into frequency-domain signals to observe the energy distribution of different frequency components. For example, bearing faults may exhibit specific high-frequency harmonics, rotor imbalance may cause the amplitude of the fundamental frequency and its multiples to increase, and winding looseness may cause impacts at specific frequencies. By identifying these characteristic frequencies and their amplitude changes, it can be effectively determined whether there is a mechanical fault and its type.

[0059] Subsequently, the transient abnormality feature analysis result is fused with the mechanical fault feature to obtain a fused feature. This fusion can be achieved in various ways, such as feature-level fusion, decision-level fusion, or data-level fusion. Feature-level fusion refers to splicing or weighted combination of two different types of feature vectors to form a more comprehensive feature vector. Decision-level fusion refers to making preliminary judgments on the transient abnormality feature and the mechanical fault feature respectively, and then making a comprehensive decision on these judgment results. By fusing fault features of different physical domains, the limitations of a single feature can be compensated for, and the accuracy and robustness of fault diagnosis can be improved.

[0060] On this basis, the fused feature is matched with a preset composite fault feature library to obtain a matching result. The composite fault feature library is a pre-established knowledge base that stores the fused feature patterns corresponding to various known motor fault types (including single faults and composite faults). When a new fused feature is generated, the system compares it with the feature patterns in the library, and determines the most matching fault type through similarity calculation and pattern recognition algorithms (such as support vector machines, neural networks, decision trees, etc.).

[0061] Finally, a description containing the properties of the composite fault is generated according to the matching result. This description not only indicates the type of fault, but also may contain the severity of the fault, possible impacts, and suggested preliminary inspection directions, etc. For example, the matching result may indicate "early-stage partial discharge of insulation accompanied by slight bearing wear", which provides more specific and comprehensive guidance for subsequent fault diagnosis and maintenance.

[0062] The scheme of the present application realizes effective fusion of electrical fault features and mechanical fault features by performing multi-dimensional analysis on the transient abnormality features contained in the cumulative evaluation result, and combining the motor shell vibration signal to extract mechanical fault features. It is precisely due to the fusion of this multi-source heterogeneous data that the system can identify and judge motor faults from a more comprehensive perspective, avoiding misjudgment or missed judgment that may be caused by a single feature. By matching the fused feature with the preset composite fault feature library, the properties of the composite fault can be more accurately identified, thereby providing more instructive information for subsequent early warning and maintenance.

[0063] By the above technical solution, the accuracy and comprehensiveness of early fault classification of the motor can be significantly improved. Traditional fault monitoring methods often focus on a single type of fault feature, such as only focusing on electrical signals or only focusing on vibration signals, which can easily lead to diagnostic blind spots or false positives when facing composite faults or early hidden faults. The present application can more accurately identify the composite fault condition of coexistence of motor insulation degradation and mechanical failure by fusing the transient abnormal feature analysis result and the mechanical fault feature, thereby providing a more refined fault property description. This multi-dimensional, multi-source information fusion strategy effectively compensates for the shortcomings of single monitoring means, provides a more reliable basis for early warning and maintenance decision of new energy vehicle motor faults, and thus improves the safety and reliability of vehicle operation.

[0064] In some embodiments of the present application described above, a description containing composite fault properties is generated according to the matching result. However, in the implementation process, if the generated fault description fails to fully consider the background and needs of the driver receiving the warning information, it may lead to low information transmission efficiency, even cause misunderstanding or unnecessary panic of the driver, thereby affecting the timeliness and effectiveness of fault warning.

[0065] To this end, the present application further proposes to generate a description containing composite fault properties according to the matching result, comprising: According to the matching result, the fault type, severity, possible impact, and suggested preliminary inspection direction are structured coded; Identify the identity of the driver currently receiving the warning information; According to the driver identity, select the corresponding description strategy from the preset description strategy library, the description strategy including the use level of professional terms, the detail level and the focus point; Fuse the structured coded fault information with the description strategy, and customize the description of the composite fault properties for the current driver group.

[0066] Specifically, structured coding refers to converting the original fault information obtained from the matching result, such as "motor winding partial discharge" and "bearing wear aggravation", into a standardized, machine-readable format. This coding can include fault classification code, severity level (e.g., slight, moderate, severe), potential system impact (e.g., power drop, abnormal sound), and suggested preliminary inspection steps (e.g., check motor connection, listen to abnormal sound position). The purpose is to provide a clear and unified data basis for subsequent customized description.

[0067] Among them, the recognition of the driver's identity can be achieved in various ways, such as through the vehicle's smart key recognition, facial recognition of the vehicle's on-board system, fingerprint recognition, or the driver's login information in the vehicle's on-board system. In practical applications, it can also be synchronized with the information of the mobile phone APP bound with the driver to obtain the driver's personal archives, including his driving experience, professional background (e.g., whether he is a professional maintenance personnel), cognitive level of vehicle failure, etc. The purpose is to provide key user portrait information for subsequent selection of appropriate description strategies.

[0068] Specifically, the description strategy library stores a variety of preset strategies for different groups of drivers. For example, for ordinary drivers, the strategy may focus on using everyday language, reducing professional terms, emphasizing the direct impact of the failure on driving safety and vehicle performance, and providing concise and brief suggestions. For professional maintenance personnel or drivers with in-depth knowledge of vehicle technology, the strategy may contain more professional terms, detailed technical parameters, specific locations of failure occurrence, and more in-depth diagnostic recommendations. The focus can be on the urgency of the failure, the cost of repair, the long-term impact on the vehicle, and other dimensions of information that are prioritized for display. The purpose is to ensure that the failure information is presented in the most suitable way for the recipient.

[0069] In practical applications, the fusion process involves converting and formatting the structured and coded failure data according to the rules of the selected description strategy. For example, if the strategy requires the use of non-technical terms, "partial discharge" in the code will be converted to "insulation aging signs"; if the strategy requires high detail, more details about "possible impact" and "inspection direction" in the code will be expanded. As a result, the final failure description is highly personalized and can effectively improve the driver's understanding and response efficiency to the warning information.

[0070] The scheme of the present application effectively solves the problem of strong generality and insufficient pertinence of traditional fault warning information by introducing driver identity recognition and customized description strategy. Specifically, first, the matching results are structured and coded, converting complex fault information into standardized data, laying the foundation for subsequent flexible processing. Second, by recognizing the identity of the driver currently receiving the warning information, the system can accurately determine the technical background and needs of the information receiver. It is precisely because of the accurate recognition of the driver's identity that the system can select the most suitable strategy from the preset description strategy library, which takes into account the level of professional terminology use, the level of detail, and the focus of attention, ensuring the effectiveness of information transmission. Finally, the structured and coded fault information is fused with the selected description strategy to generate a composite fault property description customized for a specific group of drivers. This customized processing method enables the warning information to be presented in a way that is most easily understood and accepted by the driver, avoiding misunderstandings or delays caused by information asymmetry or professional terminology barriers, significantly improving the practicality and user experience of fault warning.

[0071] Through the above technical scheme, the present application can significantly improve the transmission efficiency and accuracy of new energy vehicle motor fault warning information. Compared with the scheme of providing only general fault description, the present application identifies the driver's identity and dynamically selects the description strategy, enabling the warning information to be customized according to the specific circumstances of the receiver. This not only avoids the understanding barrier caused by professional terminology for ordinary drivers, but also provides more detailed and more instructive technical information for professional maintenance personnel. As a result, the driver can more clearly understand the nature, severity and potential impact of the fault, making more timely and appropriate response decisions, effectively reducing secondary damage or safety risks caused by fault information understanding bias, and improving the overall operational safety and user satisfaction of the vehicle.

[0072] The present application further proposes to generate a description containing a composite fault property according to the matching results, including: According to the matching results, the fault type, severity, possible impact, and suggested preliminary inspection direction are structured and coded; Obtain the type and display capability parameters of the current on-board display device, the display capability parameters including display screen size, resolution, color depth, and supported font size range; According to the display device type and the display capability parameters, select the corresponding display optimization strategy from the preset display optimization strategy library, the display optimization strategy including information priority sorting, text truncation rules, icon or graphical representation conversion rules; Fuse the structured and coded fault information with the display optimization strategy to generate a description of the composite fault property customized for the current on-board display device.

[0073] Specifically, structuring the coding of fault type, severity, possible impact and suggested preliminary inspection direction means that the original fault information obtained by matching the results is converted into a standardized, computer-friendly and easy-to-analyze data format. For example, the fault type can be coded as "insulation deterioration", "mechanical wear", etc., the severity can be coded as "mild", "moderate", "severe", etc. numerical values or levels, the possible impact can be coded as "power drop", "abnormal noise", etc., and the preliminary inspection direction can be coded as "check motor winding", "check bearing", etc. This structured coding lays the foundation for subsequent information processing and customized display.

[0074] Among them, the type and display capability parameters of the current vehicle-mounted display device are obtained, aiming to fully understand the specific characteristics of the current information output interface. The display device type can refer to the instrument panel, central control screen, HUD (head-up display), etc. The display capability parameters specifically include the display screen size, resolution, color depth, and supported font size range. These parameters can be obtained from the vehicle's communication bus (such as CAN bus or Ethernet) from the vehicle infotainment system or instrument panel control unit.

[0075] In actual application, according to the display device type and the display capability parameters, a corresponding display optimization strategy is selected from a preset display optimization strategy library. The display optimization strategy library pre-stores a plurality of display rule sets designed for different display device characteristics and capabilities. For example, for a small size, low resolution instrument panel, a "simple mode" strategy can be selected, which specifies the information priority order as: fault type > severity > suggested inspection direction, and adopts strict text truncation rules, while preferentially using simple icons for representation. For a large size, high resolution central control screen, a "detailed mode" strategy can be selected, allowing longer text descriptions to be displayed, and graphical representations can be used to enhance information intuitiveness. The display optimization strategy also includes icon or graphical representation conversion rules, such as converting "insulation deterioration" to a specific warning icon, or visualizing the severity through a color gradient bar.

[0076] Finally, the structured coding of the fault information and the display optimization strategy are fused, which means that the structured coding of the fault data is processed and formatted according to the information priority, text truncation, icon conversion, etc. rules defined in the selected display optimization strategy, so as to generate the final, customized composite fault property description for the current vehicle-mounted display device.

[0077] The scheme of the present application solves the problem of mismatch between fault description information and display device in the traditional scheme by structurally coding the fault information and dynamically selecting and applying the most suitable display optimization strategy combined with the specific type and capability parameters of the current vehicle-mounted display device. Specifically, the structural coding makes the fault information highly flexible and operable, enabling flexible combination and tailoring according to different display requirements. Obtaining the display device type and capability parameters ensures that the system can accurately assess the limitations and advantages of the current display environment. On this basis, by selecting the most matching strategy from the preset display optimization strategy library, the information can be presented in the most effective and intuitive way on different display devices. For example, in the scenario of limited display resources, the system will preferentially display the most critical information and use simple icons and concise text; while in the scenario of abundant display resources, more detailed textual explanations and rich graphical representations can be provided. This fusion processing mechanism enables intelligent adjustment of fault description according to actual display conditions, avoiding the problems of information overload or insufficient information, and greatly improving the efficiency and accuracy of information transmission.

[0078] Through the above technical scheme, the present application can intelligently customize the description of the composite fault nature according to the specific characteristics of the current vehicle-mounted display device, thereby significantly improving the presentation effect of fault information and the understanding efficiency of the driver. Compared with the scheme of generating only general fault descriptions, the present application can effectively avoid problems such as incomplete information display, too small text difficult to read, or information redundancy on different display devices. Specifically, this scheme enables fault information to clearly and concisely convey core warning content on small-size, low-resolution instrument panels, while providing more rich and intuitive graphical and detailed textual explanations on large-size, high-resolution center screens, fully utilizing the advantages of display devices. As a result, the driver can more quickly and accurately understand the nature, severity, and potential impact of the motor fault, thereby making timely and appropriate responses, effectively improving driving safety and user experience.

[0079] In some preferred embodiments, the following is described by a specific example. Assuming that the drive motor of a new energy vehicle has an early insulation deterioration fault, after processing by the above method, the system has generated structurally coded fault information according to the matching result, for example: the fault type is "motor winding insulation deterioration", the severity is "moderate", the possible impact is "long-term operation may cause motor performance degradation or even failure", and the recommended preliminary inspection direction is "it is recommended to go to the service station for professional detection as soon as possible".

[0080] At this time, the system needs to present this fault information to the driver.

[0081] Scenario one: the current vehicle-mounted display device is a small monochrome LCD screen on the instrument panel; The system first acquires the type of the display device as "dashboard LCD", and the display capability parameters include "size: 3 inches", "resolution: 128x64 pixels", "color depth: monochrome", "supported font size range: small"; According to these parameters, the system selects the "dashboard concise mode" strategy from the preset display optimization strategy library. This strategy specifies that the information priority is severity > fault type > recommended inspection direction; the text truncation rule is to display a maximum of 10 characters for each field; and the icon conversion rule is to convert "insulation deterioration" into a specific warning icon. The system fuses the structured and coded fault information with this strategy, and finally displays on the dashboard as "Warning! Moderate insulation deterioration. Please check." accompanied by a flashing warning icon.

[0082] Scenario two: the current vehicle-mounted display device is a large-size color touch screen on the center console. The system acquires the type of the display device as "center touch screen", and the display capability parameters include "size: 12 inches", "resolution: 1920x1080 pixels", "color depth: 24-bit true color", "supported font size range: small to large". According to these parameters, the system selects the "center detailed mode" strategy from the preset display optimization strategy library. This strategy specifies that the information priority is fault type > severity > possible impact > recommended inspection direction; the text truncation rule is relaxed, allowing complete descriptions to be displayed; and the icon conversion rule is to convert "insulation deterioration" into a high-resolution motor fault schematic diagram, and color can be used to distinguish severity. The system fuses the structured and coded fault information with this strategy, and finally displays on the center screen as "Motor fault warning: winding insulation deterioration (moderate). Possible impact: long-term operation may cause performance degradation. Recommendation: go to the service station for detection as soon as possible." At the same time, a detailed motor fault schematic diagram is displayed on the screen, and the affected winding area is highlighted in yellow.

[0083] As can be seen from the comparison of the above two scenarios, the scheme of the present application can intelligently adjust the presentation mode of fault information according to the characteristics of different display devices, ensuring the effectiveness of information transmission and optimizing the user experience.

[0084] In some embodiments of the present application described above, a scheme for issuing an early motor fault warning according to the results of cumulative evaluation is proposed. However, in the implementation process, if the warning information is directly presented when the driver is in a driving working condition with high concentration of attention, it may distract the driver's attention, and even cause unnecessary safety risks.

[0085] To this end, the application further proposes a scheme for optimizing the early fault warning mode of the motor, which intelligently adjusts the presentation strategy of the warning information by dynamically evaluating the running state of the vehicle, so as to ensure effective communication of fault information while maximizing driving safety and user experience.

[0086] According to the result of the cumulative evaluation, an early fault warning of the motor is issued, including: Obtain the current running state of the vehicle, including vehicle speed, gear position, steering angle, brake pedal stroke, and driver's throttle pedal opening degree; According to the current running state of the vehicle, determine whether the vehicle is in a high dynamic condition, including high-speed driving, emergency braking, or sharp turning; If the vehicle is in the high dynamic condition, temporarily suppress the direct presentation of the warning information, and cache the warning information; When the vehicle is out of the high dynamic condition, or the driver's operation intensity is reduced to a preset safety level, the warning is issued according to the cached warning information through the instrument panel indicator light, the central screen text prompt, or the voice prompt; If the result of the cumulative evaluation indicates that the fault severity reaches the level of immediate parking, regardless of whether the vehicle is in the high dynamic condition, the highest level of warning is issued through mandatory voice broadcast and vibration feedback, and the vehicle power output is automatically reduced.

[0087] Specifically, the current running state of the vehicle can be understood as a series of parameters reflecting the real-time motion of the vehicle and the driver's operation intention. Among them, the vehicle speed refers to the driving speed of the vehicle; the gear position refers to the current transmission gear of the vehicle; the steering angle refers to the steering angle of the steering wheel, reflecting the steering intention of the vehicle; the brake pedal stroke refers to the depth of the brake pedal being stepped on, reflecting the braking intensity; the throttle pedal opening degree of the driver refers to the degree of the throttle pedal being stepped on, reflecting the acceleration intention. These parameters can be obtained in real time by sensors and controllers inside the vehicle.

[0088] Further, the high dynamic condition refers to the driving state of the vehicle that requires the driver to pay high attention to operation. For example, high-speed driving can be defined as a situation where the vehicle speed exceeds a preset threshold (such as 100 km / h); emergency braking can be defined as a situation where the brake pedal stroke increases rapidly in a short time and the deceleration exceeds a preset threshold; sharp turning can be defined as a situation where the steering angle changes rapidly in a short time and the steering angle speed exceeds a preset threshold. These thresholds can be preset and adjusted according to the vehicle type, road conditions, and safety standards.

[0089] When the vehicle is in a high dynamic condition, in order to avoid unnecessary interference of the warning information to the driver, the direct presentation of the warning information will be temporarily inhibited. This means that even if the cumulative evaluation result has reached the warning trigger condition, the warning information will not be immediately presented to the driver in a visual or auditory manner, but will be cached. The cached warning information will wait for the right opportunity to be presented.

[0090] When the vehicle is out of the high dynamic condition, for example, the vehicle speed is reduced, the brake is released, the steering is stable, or the driver's operation intensity is reduced to a preset safety level, that is, the driver is no longer in a highly tense driving state, the system will issue a warning in a relatively mild manner according to the previously cached warning information. These ways can include the indicator light on the instrument panel, the text prompt displayed on the central screen, or the broadcast through the voice prompt. These ways aim to remind the driver to pay attention to the motor failure without interfering with the driving.

[0091] However, as an exception, if the result of the cumulative evaluation indicates that the severity of the motor failure has reached the level of immediate parking, for example, a failure that may cause the vehicle to lose control or serious damage, the system will immediately issue the highest level of warning regardless of whether the vehicle is in the high dynamic condition. The highest level of warning is usually through mandatory voice broadcast and strong vibration feedback (such as steering wheel or seat vibration) to ensure that the driver can immediately perceive the danger. At the same time, in order to further ensure safety, the system will also automatically reduce the power output of the vehicle to limit the driving ability of the vehicle and prompt the driver to park safely as soon as possible.

[0092] The scheme of the present application solves the problem that the traditional warning mechanism may distract the driver's attention at a critical driving moment by introducing real-time monitoring of the vehicle operating state and judgment of the high dynamic condition. It is because the system can intelligently temporarily inhibit the direct presentation of non-emergency warning information and cache these information when the vehicle is in a high dynamic condition such as high-speed driving, emergency braking, or sharp turning, that the driver can focus on the control of the vehicle and avoid potential dangers caused by sudden warnings. When the vehicle is out of the high dynamic condition or the driver's operation intensity is reduced, the system presents the warning information in an appropriate manner, ensuring effective communication of the information without affecting driving safety. In addition, for emergency situations indicating that the severity of the failure has reached the level of immediate parking, the scheme of the present application ensures that the driver is reminded to take emergency measures in time and forcibly under any condition through the highest level of warning measures such as mandatory voice broadcast, vibration feedback, and automatic reduction of vehicle power output, thereby maximizing the safety of driving, personnel, and property.

[0093] By the technical solution, the intelligence and safety of the new energy vehicle motor fault early warning system can be improved. Compared with the scheme of directly issuing a warning according to the fault accumulation evaluation result, the application considers the real-time running state of the vehicle, especially the intelligent management of the warning information presentation in the high dynamic working condition, effectively avoids the interference of the warning information to the driver, and reduces the risk of secondary accidents caused by distraction due to the warning. At the same time, for the extremely serious fault, the application ensures the immediacy and compulsiveness of the warning, and through the measures such as automatically reducing the power output, the valuable reaction time is obtained for the driver, so that more humanized and efficient fault warning is realized under the premise of ensuring the driving safety.

[0094] In some embodiments of the application, it is proposed to determine whether the vehicle is in a high dynamic working condition according to the current running state of the vehicle, and adjust the presentation strategy of the warning information accordingly. However, in actual application, the accuracy and integrity of the vehicle running state data are crucial for subsequent working condition judgment and warning decision. If the original data is abnormal or missing, it may cause misjudgment or omission of the vehicle working condition by the system, thereby affecting the reliability and timeliness of the warning system. In this regard, the application further optimizes the acquisition and processing of vehicle running state data to ensure the quality and availability of the data, thereby improving the robustness of the entire fault monitoring method.

[0095] The above-mentioned current running state of the vehicle includes vehicle speed, gear, steering angle, brake pedal stroke and driver's throttle pedal opening, comprising: Through the vehicle high-speed communication bus, the original data from the wheel speed sensor, the transmission control unit, the steering angle sensor, the brake pedal position sensor and the throttle pedal position sensor is synchronously collected at a preset fixed period, and a precise time stamp is attached to each set of collected data and it is judged whether the data is abnormal; If yes, the type of abnormality is obtained, and the data is repaired by the method of predictive filling based on historical data trend or weighted average of adjacent valid data.

[0096] The vehicle high-speed communication bus can be understood as a high-speed network inside the vehicle for data exchange between electronic control units, such as controller area network (CAN) bus, FlexRay bus or vehicle Ethernet, etc., which aims to realize efficient data transmission between vehicle components. The preset fixed period means that the system collects data at a predetermined time interval (e.g. every 10 milliseconds or every 50 milliseconds) to ensure the real-time and continuity of the data. The synchronous collection means that the relevant data is obtained from different sensors and control units at the same time point or within a very short time window to ensure the time consistency between different data, which is crucial for accurately reflecting the instantaneous running state of the vehicle.

[0097] The wheel speed sensor is used to measure the rotation speed of the wheel, and then calculate the vehicle speed. The transmission control unit provides the current gear information. The steering angle sensor is used to detect the rotation angle of the steering wheel, reflecting the steering intention of the vehicle. The brake pedal position sensor is used to measure the stroke of the brake pedal, indicating the strength of the driver's braking operation. The accelerator pedal position sensor is used to measure the opening degree of the accelerator pedal, reflecting the acceleration intention of the driver. These sensors and control units together provide key raw data constituting the vehicle operating state.

[0098] The scheme of the present application introduces a vehicle high-speed communication bus for preset fixed period synchronous data acquisition, and judges and repairs the collected raw data, thereby effectively solving the problem of inaccurate or incomplete vehicle operating state data in the basic scheme. Accurate time stamp ensures the time synchronization between different sensor data, providing a reliable basis for subsequent working condition judgment. When data is abnormal, by obtaining the abnormal type and using predictive filling or weighted average method for data repair, the integrity and effectiveness of the data can be maximized. It is precisely because of the strict control of the quality of raw data and the introduction of repair mechanism that the subsequent judgment of whether the vehicle is in high dynamic working condition is more accurate and robust, avoiding misjudgment caused by data quality problems, thereby ensuring the rationality of the early warning information presentation strategy.

[0099] Through the above technical scheme, the present application can significantly improve the accuracy and reliability of vehicle operating state data acquisition in the new energy vehicle motor fault monitoring method. Through synchronous acquisition and accurate time stamp, the time sequence consistency of multi-source data is ensured, laying a solid foundation for accurate judgment of high dynamic working condition. In addition, the introduced data abnormality judgment and repair mechanism can effectively deal with data loss or error caused by sensor failure or communication abnormality, greatly enhancing the adaptability and robustness of the system to data quality under complex working conditions. This optimization of data source directly improves the accuracy of subsequent early warning logic, reduces false positives or false negatives caused by data problems, thereby improving the reliability and safety of the entire motor early fault warning system.

[0100] Referring to Figure 4 , the embodiment of the present application provides a new energy vehicle motor fault monitoring system structure diagram, comprising: A detection end is configured to acquire a switch control signal of an inverter of the new energy vehicle drive motor. The processing end is configured to start ultra-high frequency sampling in a time window according to the switch control signal, collect motor three-phase current and voltage signals and perform analysis, extract transient abnormal characteristics of early insulation deterioration by comparing analysis results with ideal waveforms, acquire motor operation conditions corresponding to the transient abnormal characteristics, and perform weighted cumulative evaluation on the transient abnormal characteristics according to the motor operation conditions. The early warning end is configured to issue an early fault warning of the motor according to a result of the cumulative evaluation.

[0101] It should be noted that the new energy automobile motor fault monitoring system provided by the embodiments of the present application is used to execute all process steps of the new energy automobile motor fault monitoring method provided by the above embodiments, and the working principles and beneficial effects of the two are one-to-one corresponding, and thus will not be repeated.

[0102] The embodiments of the present application further provide a terminal device. The terminal device comprises a processor, a memory, and a computer program stored in the memory and executable on the processor. The processor implements the steps in the above various new energy automobile motor fault monitoring method embodiments when executing the computer program, for example Figure 1 The processor implements the functions of the modules / units in the above various system embodiments when executing the computer program.

[0103] For example, the computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present application. The one or more modules / units can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the terminal device.

[0104] The terminal device can be a desktop computer, a notebook computer, a palm computer, and a smart tablet computer, etc. The terminal device can include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above components are only examples of the terminal device and do not constitute a limitation on the terminal device, and the terminal device can include more or fewer components than the above, or combine certain components, or different components, for example, the terminal device can further include an input / output device, a network access device, a bus, etc.

[0105] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The processor is a control center of the terminal device, and connects all parts of the terminal device through various interfaces and lines.

[0106] The memory can be used to store the computer programs and / or modules, and the processor realizes various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area. The program storage area can store an operating system, at least one application program required by a function (such as a sound playing function, an image playing function, etc.), etc.; and the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a nonvolatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state memory device.

[0107] The modules / units integrated in the terminal device, if realized in the form of software function units and sold or used as independent products, can be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. The computer program can implement the steps of each method embodiment when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms, etc. The computer readable medium can include any entity or system, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium, etc. that can carry the computer program code. It should be noted that the computer readable medium can include or exclude contents according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable medium does not include electric carrier signals and telecommunication signals.

[0108] It should be noted that the above-described system embodiments are only illustrative, and the units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, i.e., they can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment according to actual needs. In addition, the connection relationship between the modules in the system embodiments provided by the present application indicates that there is a communication connection between them, which can be realized as one or more communication buses or signal lines. Those skilled in the art can understand and implement it without creative labor.

[0109] The above-described specific embodiments further illustrate the purpose, technical solutions, and beneficial effects of the present application. It should be understood that the above-described specific embodiments are only examples of the present application and are not intended to limit the protection scope of the present application. It is particularly pointed out that any modification, equivalent replacement, improvement, etc. made by those skilled in the art within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for monitoring faults in a new energy vehicle motor, characterized in that, The method includes: Obtain the switching control signal of the inverter of the drive motor of the new energy vehicle; Based on the switch control signal, ultra-high frequency sampling is initiated within the time window to collect and analyze the three-phase current and voltage signals of the motor. After comparing the analysis results with the ideal waveform, the transient anomaly characteristics of early insulation degradation were extracted. Obtain the motor operating conditions corresponding to the transient abnormal characteristics; Based on the motor operating conditions, the transient abnormal characteristics are evaluated using a weighted cumulative approach. Based on the results of cumulative assessments, early warnings of motor failures are issued.

2. The method for monitoring faults in a new energy vehicle motor according to claim 1, characterized in that, The step of comparing the analysis results with the ideal waveform and extracting transient anomaly features of early insulation degradation includes: In the initial stage of motor operation, transient current and voltage waveforms under different operating conditions are collected, and the waveforms under each operating condition are averaged to establish an initial healthy reference waveform. For each newly captured transient current and voltage waveform, a preliminary fault assessment is performed to ensure that it does not contain obvious sudden fault characteristics; If the waveform is determined to be a normal waveform under the current healthy state, then the corresponding adaptive reference waveform is updated. Calculate the instantaneous deviation between the newly captured transient current and voltage waveform and the current adaptive reference waveform; A filtered deviation signal is generated based on the instantaneous deviation; The deviation signal is used to identify pulses whose amplitude exceeds a preset instantaneous threshold and whose duration is extremely short. For each detected pulse, the peak amplitude, rise time, pulse width, and high-frequency energy content are extracted as pulse features; The pulse characteristics are compared with the pre-established partial discharge characteristic signature to determine whether it is a real partial discharge event, so as to extract the transient abnormal characteristics of the early insulation degradation.

3. The method for monitoring faults in a new energy vehicle motor according to claim 1, characterized in that, The step of weighted cumulative evaluation of the transient abnormal characteristics based on the motor operating conditions includes: Monitor the insulation resistance value of the motor windings; When the insulation resistance value decreases and the rate of decrease exceeds a preset threshold, the motor insulation material is determined to have entered the accelerated aging stage. During the accelerated aging phase, the weighting coefficients of the transient abnormal characteristics are dynamically adjusted according to the current motor operating conditions. When the temperature of the motor windings is higher than a preset temperature threshold or the load current of the motor is higher than a preset current threshold, the corresponding weighting coefficient is increased. The baseline value of the weighting coefficient is periodically adjusted based on the total running time of the motor. The transient anomaly features are evaluated using a weighted cumulative assessment based on the dynamically adjusted weighting coefficients and the baseline value.

4. The method for monitoring faults in a new energy vehicle motor according to claim 1, characterized in that, The step of issuing an early warning for motor faults based on the results of the cumulative assessment includes: performing a preliminary classification of motor faults based on the results of the cumulative assessment to obtain a description of the nature of the faults; The warning trigger threshold or the duration of the warning trigger is dynamically adjusted based on the current vehicle driving mode and the driver's driving habits. When the cumulative evaluation result reaches the warning trigger threshold or the duration, a first-level warning message is issued through the vehicle display screen, voice broadcast or vibration feedback. The first-level warning message includes a description of the nature of the fault. If the cumulative assessment results continue to rise and are not addressed within a preset time, a second-level warning message will be issued. The second-level warning message includes a description of the nature of the fault and preliminary countermeasures recommendations. The frequency or intensity of the warning information is adjusted based on the current driver's identity and historical warning response records.

5. The method for monitoring faults in a new energy vehicle motor according to claim 4, characterized in that, Based on the results of the cumulative evaluation, the motor faults are initially classified to obtain a fault nature description, including: A multi-dimensional analysis of the transient anomaly features contained in the cumulative evaluation results is performed to obtain the transient anomaly feature analysis results; Acquire vibration signals collected by the motor housing vibration sensor; The vibration signal is subjected to spectral analysis to extract features related to mechanical faults, thereby obtaining mechanical fault features; The transient anomaly feature analysis results are combined with the mechanical fault features to obtain a fused feature; the fused feature is matched according to a preset composite fault feature library to obtain a matching result; and a description containing composite fault properties is generated based on the matching result.

6. The method for monitoring faults in a new energy vehicle motor according to claim 5, characterized in that, The step of generating a description containing composite fault properties based on the matching result includes: Based on the matching results, the fault type, severity, potential impact, and suggested initial inspection directions are structured and coded. Identify the driver currently receiving the warning information; Based on the driver's identity, a corresponding description strategy is selected from a preset description strategy library. The description strategy includes the level of use of technical terms, the level of detail, and the focus of attention. By fusing structured coded fault information with the description strategy, a description of the complex fault characteristics is customized for the current driver group.

7. A method for monitoring faults in a new energy vehicle motor according to claim 5, characterized in that, The step of generating a description containing composite fault properties based on the matching result includes: Based on the matching results, the fault type, severity, potential impact, and suggested initial inspection directions are structured and coded. Obtain the current in-vehicle display device type and display capability parameters, including display screen size, resolution, color depth, and supported font size range; Based on the display device type and the display capability parameters, a corresponding display optimization strategy is selected from a preset display optimization strategy library. The display optimization strategy includes information priority sorting, text truncation rules, and icon or graphical representation conversion rules. The structured coded fault information is fused with the display optimization strategy to generate a description of the composite fault characteristics customized for the current in-vehicle display device.

8. A method for monitoring faults in a new energy vehicle motor according to claim 1, characterized in that, The step of issuing an early warning for motor faults based on the results of the cumulative evaluation includes: The current operating status of the vehicle is obtained, including vehicle speed, gear, steering angle, brake pedal travel, and driver's accelerator pedal opening. Based on the current operating status of the vehicle, determine whether the vehicle is in a high dynamic condition, which includes high-speed driving, emergency braking, or sharp turning. If the vehicle is in the high-dynamic operating condition, the direct presentation of the warning information is temporarily suppressed, and the warning information is cached. When the vehicle leaves the high-dynamic operating condition, or when the driver's operating intensity decreases to a preset safety level, a warning is issued through the instrument panel indicator lights, text prompts on the central control screen, or voice prompts based on the cached warning information. If the cumulative assessment indicates that the severity of the fault reaches the level requiring immediate shutdown, then regardless of whether the vehicle is in the high-dynamic condition, the highest level warning will be issued through mandatory voice broadcast and vibration feedback, and the vehicle's power output will be automatically reduced.

9. A method for monitoring faults in a new energy vehicle motor according to claim 8, characterized in that, The process of acquiring the vehicle's current operating status, which includes vehicle speed, gear, steering angle, brake pedal travel, and the driver's accelerator pedal opening, includes: The system synchronously collects raw data from wheel speed sensors, transmission control unit, steering angle sensor, brake pedal position sensor and accelerator pedal position sensor at a preset fixed period via the vehicle high-speed communication bus, and adds a precise timestamp to each set of collected data and determines whether the data is abnormal. If so, the anomaly type is obtained, and data repair is performed using predictive filling based on historical data trends or a weighted average of adjacent valid data.

10. A fault monitoring system for a new energy vehicle motor, characterized in that, The system includes: a detection terminal, used to acquire the switching control signal of the inverter of the new energy vehicle drive motor; The processing end is used to initiate ultra-high frequency sampling within the time window according to the switch control signal, collect and analyze the three-phase current and voltage signals of the motor; after comparing the analysis results with the ideal waveform, extract the transient abnormal features of early insulation degradation; obtain the motor operating conditions corresponding to the transient abnormal features; and perform weighted cumulative evaluation on the transient abnormal features according to the motor operating conditions. The early warning terminal is used to issue early warnings of motor faults based on the results of cumulative evaluation.

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