Electric vehicle speed out-of-control fault cause identification method

Abstract

The invention relates to the technical field of electric vehicle fault analysis, and discloses an electric vehicle speed out-of-control fault cause identification method, which comprises the following steps: S1, collecting and analyzing message data in a vehicle operation process, extracting signal parameters from the message data, and preprocessing to obtain vehicle operation data T; s2, segmenting the T, and establishing a training set and a test set by taking an accelerator pedal opening value in the T as a feature and a torque value in the T as a target; s3, establishing a sum-based regression model, predicting a target accelerator pedal opening value based on the regression model, and obtaining a predicted target torque value; s4, calculating the offset with the real torque value; s5, determining a high-risk point according to the offset, and outputting a multi-dimensional feature map; and S6, performing fault judgment and cause analysis according to the multi-dimensional feature map. The method can accurately identify and position the cause of the speed out-of-control fault, and is accurate in identification and reliable in analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle fault analysis, and in particular to a method for identifying the cause of an electric vehicle speed out-of-control fault. Background Art

[0002] With the continuous increase in the number of new energy vehicles and the continuous innovation of new energy vehicle technology, the number of new energy vehicle accidents, deaths and injuries has also reached a record high. Sudden loss of control accidents caused by systemic failures in the power system of new energy vehicles, as well as the inadequate functions of emerging technologies such as assisted driving, smart cockpits, and single-pedal braking modes under specific triggering conditions such as bad weather, poor road conditions, and unexpected behavior of drivers / traffic participants, account for an increasing proportion of new energy vehicle safety accidents, especially traffic safety accidents. Speed ​​loss of control accidents are particularly harmful. Specifically, due to the instantaneous torque characteristics of the power system of new energy vehicles (peak torque can reach more than 3 times that of traditional fuel vehicles), the consequences of speed loss of control are more serious. According to statistics, in 2023, the proportion of vicious collision accidents caused by loss of control of new energy vehicles worldwide with a speed exceeding 80km / h reached 67%, an increase of 42% compared with similar accidents of traditional fuel vehicles (IIHS data). Such accidents are often accompanied by battery thermal runaway (for example, the battery pack temperature of a new energy vehicle soars to 600°C within 2 seconds) and chain failure of the electronic control system, forming a "mechanical-electrical-chemical" multiple disaster coupling, posing a major threat to public safety.

[0003] In this context, the International Organization for Standardization (ISO) has recently released ISO / TR 9968:2023 "Guidelines for Response to Failures in Automated Driving Systems", which explicitly requires automakers to establish a "fault cause traceability mechanism" to promote the early prevention of accidents and reduce the driving risks of new energy vehicles. The EU General Safety Regulation (GSR) stipulates that new energy vehicles launched in July 2024 must have a "black box data for loss of control accidents" function (EDR sampling frequency must reach 1kHz). However, the current domestic GB / T 40429-2021 standard only requires the recording of 0.1 second-level key parameters, resulting in the loss of 74.6% of key cause signals in accident investigations. The demand for technical compliance urgently requires innovation in analysis methods.

[0004] However, since speed loss of control may involve multiple issues such as battery BMS misjudgment, motor controller IGBT junction temperature protection failure, or autonomous driving perception fusion algorithm defects, the causes of accidents are complex, sudden loss of control accidents are highly sporadic, and effective data on accident cases are relatively scarce. Therefore, there is currently no clear research on the identification and location methods of speed loss of control faults. At present, there is still an urgent need for an effective method for identifying the causes of electric vehicle speed loss of control faults to cope with the latest development trends of the new energy vehicle industry and promote the reduction of new energy vehicle driving risks. Summary of the Invention

[0005] The present invention aims to provide a method for identifying the causes of speed runaway faults in electric vehicles, which can accurately identify and locate the causes of speed runaway faults, and has accurate identification and reliable analysis.

[0006] The basic solution provided by the present invention is: A method for identifying the causes of speed runaway faults in electric vehicles, comprising the following steps: S1, Collect and parse the message data during vehicle operation, and extract signal parameters therefrom, and obtain vehicle operation data T after preprocessing; S2, Segment the vehicle operation data T, and use the accelerator pedal opening value in the vehicle operation data T as a feature, and use the torque value in the vehicle operation data T as a target to establish a training set and a test set; S3, Establish a regression model based on and and predict the target accelerator pedal opening value based on the regression model, and obtain the predicted target torque value ; S4, Calculate the offset between the target torque value and the actual torque value ; S5, According to the offset , Confirm high-risk points and output a multi-dimensional feature map; S6, According to the multi-dimensional feature map, perform fault determination and cause analysis.

[0007] The working principle and advantages of the present invention are as follows: The method for identifying the causes of speed runaway faults in electric vehicles of the present invention constructs an abnormal identification and location method through a data-driven approach, which can quickly locate high-risk positions in complex and redundant vehicle operation historical data and automatically output a multi-dimensional feature map. Furthermore, through image feature analysis and verification, it realizes the identification and determination of the causes of speed runaway faults in electric vehicles driven by knowledge, and has accurate identification and reliable analysis, which helps to promote the paradigm shift of new energy vehicle safety engineering from "post-event handling" to "pre-event prevention".

[0008] The key point is: this solution specifically establishes a dynamic regression model based on the accelerator pedal opening and torque output, where the accelerator pedal opening directly reflects the driver's power demand command, and torque is the core parameter of the motor power output. The two have a strong physical correlation (driver intention → control signal → power output). By establishing a regression model for the two, the expected response relationship under normal working conditions can be quantified, and the core contradictions of human-vehicle interaction can be effectively captured. Furthermore, by collaboratively analyzing the driving intention (corresponding to the accelerator pedal opening) and the execution result (corresponding to the torque output), and capturing the risk points, the fault can be accurately located, and the fault clustering characteristics can be intuitively presented in a multi-dimensional feature map. This analysis method has a strong feature-target correlation and is based on a data-driven form, which can avoid the limitation of the traditional FMEA method that requires the pre-definition of fault modes. It is especially suitable for the detection of occasional anomalies caused by the complex logic of smart car software, and can detect faults more comprehensively.

[0009] What is special is that: in order to solve the problem that the causes of faults are complex, sporadic, and difficult to identify and locate, this solution has made a breakthrough in decoupling complex control strategies (such as energy recovery) from driver operations, and established a dynamic benchmark model in a data-driven way to improve the sensitivity of abnormal detection, and then introduced a mechanism model to trace the causes of faults. Among them, in the data preprocessing stage, by excluding the torque mutation caused by energy recovery / single pedal mode when the accelerator pedal is released, the problem that the traditional threshold method is easily interfered by the control strategy is solved. Only retaining the data of the effective action interval of the accelerator pedal can ensure that the model reflects the true "acceleration demand-power response" relationship. At the same time, using linear regression instead of fixed threshold judgment can adapt to the power characteristics of different models (such as the torque response difference between high-performance cars and family cars); by minimizing the prediction error and dynamically fitting the optimal parameters, the limitations of the traditional static threshold method are broken. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 This is a schematic diagram of the method flow structure of Embodiment 1 of a method for identifying the cause of a speed out-of-control fault of an electric vehicle according to the present invention; Figure 2 A schematic diagram of the visualization effect of a regression model of a first embodiment of a method for identifying the cause of an electric vehicle speed out-of-control fault according to the present invention; Figure 3 This is an example diagram of the first characteristic diagram of Embodiment 1 of a method for identifying the cause of a speed out-of-control fault of an electric vehicle according to the present invention; Figure 4 This is an example diagram of the second characteristic diagram of Embodiment 1 of a method for identifying the cause of a speed out-of-control fault of an electric vehicle according to the present invention; Figure 5 It is a signal analysis schematic diagram of a method for identifying the cause of a speed out-of-control fault of an electric vehicle according to a first embodiment of the present invention; Figure 6This is an example diagram of the first characteristic diagram of Embodiment 2 of the method for identifying the cause of the electric vehicle speed out-of-control fault of the present invention; Figure 7 This is the first example diagram of the second characteristic diagram of Embodiment 2 of the method for identifying the cause of the electric vehicle speed out-of-control fault of the present invention; Figure 8 This is the second example diagram of the second characteristic diagram of Embodiment 2 of the method for identifying the cause of the electric vehicle speed out-of-control fault of the present invention; Figure 9 This is the signal analysis schematic diagram of Embodiment 2 of the method for identifying the cause of the electric vehicle speed out-of-control fault of the present invention; Figure 10 This is an example diagram of the first characteristic diagram of Embodiment 3 of the method for identifying the cause of the electric vehicle speed out-of-control fault of the present invention; Figure 11 This is an example diagram of the second characteristic diagram of Embodiment 3 of the method for identifying the cause of the electric vehicle speed out-of-control fault of the present invention; Figure 12 This is the signal analysis schematic diagram of Embodiment 3 of the method for identifying the cause of the electric vehicle speed out-of-control fault of the present invention. Detailed implementation manner

[0011] The following is a more detailed description through specific implementation manners: Embodiment 1 The embodiment is basically as shown in the appendix Figure 1 shown: A method for identifying the cause of the electric vehicle speed out-of-control fault includes the following steps: S1, collect and analyze the message data during the vehicle operation, extract the signal parameters from it, and obtain the vehicle operation data T after preprocessing.

[0012] The signal parameters include: data acquisition time, accelerator pedal opening, brake pedal opening, torque, voltage, speed, etc.

[0013] Further, extract the accelerator pedal opening value and torque value , ; ; and perform data processing according to the following processing strategy to screen out abnormal data: Screen the data where the accelerator pedal opening value and the torque value are both positive, that is, screen the data, and exclude the data with a downward trend and the change rate exceeding the torque mutation threshold.

[0014] In this embodiment, the torque mutation threshold is set to By this operation, abnormal data caused by the sudden change of torque data during the vehicle operation when the accelerator pedal is gradually released, which is caused by control strategies such as energy recovery or one-pedal mode braking of the vehicle, can be excluded, reducing the interference with subsequent data analysis.

[0015] Calculate the difference in the accelerator pedal opening value ; ; In the formula, represents the difference value of the accelerator pedal opening value of the i-th frame of data, and N represents the total number of frames of data in T; Screen the part in that is less than the accelerator pedal mutation threshold t0, and filter the vehicle operation data T through the indexes of this part of data: ; In the formula, represents the index of the position that meets the conditions in the vector.

[0016] In this embodiment, the accelerator pedal mutation threshold is set to 2. By this operation, the interference of abnormal data caused by suddenly stepping on or releasing the accelerator on the fault determination can be excluded.

[0017] S2. Split the vehicle operation data T, and use the accelerator pedal opening value in the vehicle operation data T as a feature, and use the torque value in the vehicle operation data T as a target to establish a training set and a test set.

[0018] S3. Establish a regression model based on and , and predict the target accelerator pedal opening value based on the regression model, and obtain the predicted target torque value .

[0019] The regression model is ; In the formula, w and b are model parameters, which are determined in the following way: ; where N is the total number of frames of data in T, is the torque value of the i-th frame of data, is the accelerator pedal opening value of the i-th frame of data. The visualization effect of the regression model is as Figure 2 shown.

[0020] S4. Calculate the offset between the target torque value and the true torque value .

[0021] Among them, 。

[0022] S5. Based on the offset , identify high - risk points and output a multi - dimensional feature map.

[0023] The high - risk points refer to the corresponding data points where the offset exceeds the threshold t1; a multi - dimensional feature map is composed of the high - risk points and m data points near them.

[0024] In this embodiment, the value of t1 is 30. In practical applications, this value can be corrected according to the power performance of different vehicle models. The value of m is 50. In practical applications, this value can be set according to data quality and fault types.

[0025] The multi - dimensional feature map includes: the first feature map - the change relationship feature map of the accelerator pedal opening and torque value; the second feature map - the change relationship feature map of speed, accelerator pedal opening and voltage.

[0026] S6. Based on the multi - dimensional feature map, conduct fault determination and cause analysis.

[0027] When conducting fault determination and cause analysis, the following situations are included: Observe the numerical fluctuation characteristics in the multi - dimensional feature map. For example, Figure 3 as shown, when the numerical fluctuation characteristics in the first feature map show that the accelerator pedal opening value rises while the driving motor torque suddenly drops to 0, trigger the synchronization verification based on the second feature map - as Figure 4 shown, if the total voltage drops below the safety threshold within the preset time threshold, retrieve the message data in the same period. If there are simultaneously BMS total voltage loss alarms, DC - DC alarms, and driving motor status abnormal signals in the message data, as Figure 5 shown, then it is determined that the high - voltage signal acquisition fails.

[0028] Among them, the BMS total voltage loss alarm refers to the fault alarm signal triggered when the vehicle's BMS system (i.e., the battery management system) detects that the total voltage of the power battery pack drops abnormally or completely disappears. The DC - DC alarm refers to the fault prompt signal triggered when the DC - DC converter (i.e., the direct - current to direct - current converter, which is responsible for converting the direct current of the high - voltage power battery into low - voltage direct current to supply power to the vehicle body electronic devices) detects an abnormal state. The driving motor status abnormal signal refers to the fault indication signal triggered when the vehicle's motor controller (MCU) or vehicle control unit (VCU) detects that the operating parameters of the driving motor exceed the safe range or the function fails.

[0029] The high - voltage signal acquisition failure includes at least one of the following situations: hardware failure of the voltage acquisition board, poor contact of the high - voltage signal harness, or abnormal signal reception channel of the control unit.

[0030] The preset time threshold for the synchronization verification is the time window from when the accelerator pedal opening value rises to when the driving motor torque suddenly drops to 0, and this time window does not exceed 200 ms. In this embodiment, the safety threshold can be set to 40% of the rated value.

[0031] A method for identifying the cause of an electric vehicle speed runaway fault provided in this embodiment constructs an abnormal identification and positioning method through a data-driven approach, which can quickly locate high-risk positions in complex and redundant vehicle operation historical data and automatically output a multi-dimensional feature map. Furthermore, by combining image feature analysis and verification, it realizes the identification and determination of the cause of an electric vehicle speed runaway fault driven by knowledge, and the identification is accurate and the analysis is reliable.

[0032] The key lies in: compared with traditional rule-based or fixed-threshold detection methods, this solution establishes an association benchmark between driving intention and power output through dynamic modeling, which can effectively strip the interference of control strategies such as energy recovery and single-pedal mode on the torque signal, and focus on the abnormal detection of the real "acceleration demand - power response" link.

[0033] At the data processing level, an accelerator pedal effective action interval data screening mechanism is adopted to avoid torque fluctuation interference under non-acceleration instructions; at the model construction level, linear regression is used to establish a dynamic response benchmark under different driving scenarios, which not only retains the personalized parameter space of the power characteristics of different vehicle models, but also constructs a unified abnormal quantification evaluation standard. This dual innovation in data processing and modeling methods enables the system to capture unexpected deviations in torque output on a millisecond time scale. At the same time, through the visual presentation of the multi-dimensional feature map, the fault modes hidden in the high-dimensional data space are projected into the low-dimensional space, providing an analysis interface with both data support and physical interpretability for engineering personnel, and significantly improving the traceability efficiency of complex faults.

[0034] Secondly, the regression model construction logic of this solution has strong superiority - the accelerator pedal opening, as a direct quantitative representation of the driver's intention, and the motor torque output form the core control link of the vehicle power system, and there is a clear physical mapping relationship between the two under normal working conditions. By establishing a dynamic regression model between the two, not only can the expected response relationship be quantified, but also the torque deviation caused by normal control strategy intervention and abnormal faults can be effectively distinguished. Specifically, in the preprocessing stage, by filtering out the data segments when the pedal is released, the interference of the torque reverse output caused by the active intervention of the energy recovery system can be avoided; focusing on the effective travel interval of the pedal, ensuring that the model only analyzes the response abnormality of clear acceleration instructions, so that the model can accurately identify real faults such as electronic control command loss and actuator response delay, and at the same time avoid misjudging normal control logics such as braking energy recovery and thermal management torque limitation as abnormalities.

[0035] Compared with the limitation of the traditional Failure Mode and Effects Analysis (FMEA) method that requires predefined failure types, this solution can adapt to scenarios with frequent software iterations and complex control logics in intelligent vehicles by automatically learning the normal response rules through data-driven means. It can detect unknown faults such as missing electronic control instructions and execution delays, avoiding the missed detection problems caused by incomplete fault libraries in traditional methods. In addition, through the optimization of minimizing the prediction error of the linear regression model, it can automatically adjust parameters according to the dynamic characteristics of different vehicle models (such as the large torque rapid response of high-performance vehicles and the smooth output characteristics of household vehicles), breaking through the strong dependence of the fixed threshold method on vehicle models and driving scenarios. Moreover, this dynamic characteristic enables the system to achieve cross-platform deployment without the need for complex calibration work for each vehicle model. Further, by using the visual analysis of multi-dimensional feature maps to transform abstract data anomalies into spatial clustering features and combining physical knowledge such as the motor electromagnetic torque equation and battery attenuation model for cross-verification, a complete analysis chain of "data anomaly discovery - physical mechanism tracing - failure mode confirmation" can be formed, providing a feasible technical path for the transformation of new energy vehicle safety engineering from passive accident handling to active risk prevention.

[0036] Embodiment 2 A method for identifying the cause of the failure of an electric vehicle's speed out of control makes the following adjustments based on Embodiment 1.

[0037] When performing fault determination and cause analysis, the following situations are included: Observe the numerical fluctuation characteristics in the multi-dimensional feature map, such as Figure 6 As shown, when the numerical fluctuation characteristics in the first feature map show that the accelerator pedal opening value rises while the driving motor torque suddenly drops to 0, trigger the synchronization verification based on the second feature map - as Figure 7 、 Figure 8 As shown, if the total voltage drops below the corresponding threshold and this situation (i.e., the total voltage drop situation) occurs 2 times, retrieve the message data in the same time period and perform multi-stage judgment: If the total voltage drop situation occurs when the vehicle is in a stationary state and there is no driving behavior, if the total voltage drop exceeds the stationary threshold (V1) and automatically recovers within 10 - 30 minutes, and there is no associated system alarm, it is marked as an occasional voltage fluctuation event; If the total voltage drop situation occurs when the vehicle is in a driving state, if the total voltage drops suddenly below the driving threshold (V2) within the preset time (5 seconds), and at the same time satisfies: The contradictory output of the accelerator pedal travel value rising and at least one driving motor torque suddenly dropping to 0; Trigger the composite signal of the BMS total voltage loss alarm, DC-DC alarm, and abnormal driving motor state; as Figure 9 As shown; Then it is determined that the high-voltage signal acquisition link fails.

[0038] According to the triggering scenarios of the failure of the high-voltage signal acquisition link, it is classified into: If there is a record of abnormal static voltage marking, it is determined as an intermittent poor contact fault; If it appears for the first time and there is no static marking, it is determined as a hardware fault of the voltage acquisition board.

[0039] A method for identifying the cause of an electric vehicle speed runaway fault provided in this embodiment provides richer fault determination and cause analysis strategies compared to Embodiment 1. Specifically, through differential diagnosis in the static and driving states, combined with the contradictory phenomenon of acceleration demand and sudden torque drop and the verification of multi-system composite alarms, accurate tracing of high-voltage system faults (distinguishing between occasional poor contact and hardware failure) can be achieved, and dynamic threshold management with state adaptability (different voltage thresholds and time windows for static / driving) is adopted, which can reduce the misjudgment rate while ensuring driving safety.

[0040] Embodiment 3 A method for identifying the cause of an electric vehicle speed runaway fault makes the following adjustments on the basis of Embodiment 1 or Embodiment 2.

[0041] When performing fault determination and cause analysis, the following situations are included: Observe the numerical fluctuation characteristics in the multi-dimensional feature map, such as Figure 10 As shown, when the numerical fluctuation characteristics in the first feature map show that the accelerator pedal opening value rises while the driving motor torque suddenly drops to 0, trigger the synchronization verification based on the second feature map - as Figure 11 As shown, if the accelerator pedal opening value continues to rise and the total voltage drops below the safety threshold within the preset time threshold, retrieve the message data in the same time period and make further judgments: If the following conditions are met simultaneously: the driving motor controller reports a torque output failure alarm, the BMS triggers a total voltage loss fault code and the DC-DC converter output is abnormal (that is, there are BMS total voltage loss alarms, DC-DC alarms and driving motor status abnormal signals in the message data at the same time); and the high-voltage bus current of the vehicle drops below the safety threshold, as Figure 12 As shown; then it is determined that the high-voltage signal acquisition link fails.

[0042] A method for identifying the cause of an electric vehicle speed runaway fault provided in this embodiment provides richer fault determination and cause analysis strategies compared to Embodiment 1. Specifically, by real-time monitoring of the contradictory phenomenon of acceleration demand and sudden high-voltage drop, combined with the composite verification of multi-system alarms such as the driving motor, BMS, and DC-DC converter, the failure of the high-voltage signal acquisition link (such as voltage acquisition board or wiring harness fault) can be accurately identified, effectively excluding the interference of single sensor false alarms.

[0043] The above are only embodiments of the present invention. Common knowledge such as specific structures and characteristics known in the art has not been described in detail herein. Those of ordinary skill in the art know all the common general technical knowledge in the technical field to which the invention pertains before the filing date or the priority date, can learn about all the prior art in this field, and have the ability to apply the conventional experimental means before this date. Those of ordinary skill in the art can, under the inspiration given in this application, combine their own abilities to complete and implement this solution. Some typical well-known structures or well-known methods should not become obstacles for those of ordinary skill in the art to implement this application. It should be noted that for those skilled in the art, without departing from the structure of the present invention, several modifications and improvements can still be made, and these should also be regarded as the protection scope of the present invention, and these will not affect the implementation effect of the present invention and the practicality of the patent.

Claims

1. A method for identifying the cause of an electric vehicle speed out-of-control fault, characterized in that It includes the following steps: S1. Collect and analyze the message data during the vehicle operation, extract the signal parameters from it, and obtain the vehicle operation data T after preprocessing; S2, split the vehicle operation data T, and use the accelerator pedal opening value in the vehicle operation data T as a feature, and use the torque value in the vehicle operation data T as a target to establish a training set and a test set; S3. Establish a regression model based on and , and predict the target accelerator pedal opening value based on the regression model , and obtain the predicted target torque value ; S4, calculate the target torque value and the true torque value for the offset ; S5, according to the offset , identify high-risk points and output a multi-dimensional feature map; S6. Conduct fault determination and cause analysis based on the multi-dimensional feature map.

2. The method for identifying the cause of the electric vehicle speed out-of-control fault according to claim 1, wherein, The signal parameters include: data acquisition time, accelerator pedal opening, brake pedal opening, torque, voltage, and speed.

3. The method for identifying the cause of the electric vehicle speed out-of-control fault according to claim 1, characterized in that, Before step 2, it further includes: extracting the accelerator pedal opening value in the vehicle operation data and the torque value , ; ; and performing data processing according to the following processing strategy: Select data where the accelerator pedal opening value and the torque value are both positive, that is, select the data, and exclude the data that is in a downward trend and whose change rate exceeds the torque mutation threshold.

4. The method for identifying the cause of the electric vehicle speed out-of-control fault according to claim 3, characterized in that, The processing strategy further includes: Calculate the difference in the accelerator pedal opening value ; Wherein, represents the difference value of the accelerator pedal opening value of the i-th frame of data, and N represents the total number of frames of data in T; Screening The part less than the accelerator pedal mutation threshold t0, and filter the vehicle operation data T through the index of this part of the data: ; In the formula, represents the index of the position in the vector that satisfies the condition.

5. A method for identifying the cause of an electric vehicle speed out-of-control fault according to claim 1, characterized in that, The regression model is ; where w and b are model parameters, which are determined as follows: ; where N is the total number of frames of data in T, is the torque value of the i-th frame of data, is the accelerator pedal opening value of the i-th frame of data.

6. The method for identifying the cause of the electric vehicle speed out-of-control fault according to claim 1, characterized in that, The high-risk point refers to the offset corresponding data points where it exceeds the threshold t1; Construct a multi-dimensional feature map with high-risk points and m data points near them.

7. A method for identifying the cause of an electric vehicle speed out-of-control fault according to claim 6, characterized in that, The value of t1 is 30, and the value of m is 50.

8. A method for identifying the cause of an electric vehicle speed out-of-control fault according to claim 1, characterized in that, The multi-dimensional feature map includes: the first feature map - the change relationship feature map of the accelerator pedal opening and the torque value; the second feature map - the change relationship feature map of the speed, accelerator pedal opening, and voltage.

9. The method for identifying the cause of the electric vehicle speed out-of-control fault according to claim 8, wherein, When conducting fault determination and cause analysis, it includes the following situations: Observe the numerical fluctuation characteristics in the multi-dimensional feature map. When the numerical fluctuation characteristics in the first feature map show that the accelerator pedal opening value rises while the driving motor torque suddenly drops to 0, trigger the synchronization verification based on the second feature map - if the total voltage drops below the safety threshold within the preset time threshold, retrieve the message data in the same period. If there are BMS total voltage loss alarms, DC-DC alarms, and driving motor status abnormal signals in the message data at the same time, it is determined that the high-voltage signal acquisition fails; The failure of high-voltage signal acquisition includes at least one of the following situations: hardware failure of the voltage acquisition board, poor contact of the high-voltage signal harness, or abnormal signal receiving channel of the control unit.

10. The method for identifying the cause of the electric vehicle speed out-of-control fault according to claim 9, wherein, The preset time threshold for the synchronization verification is the time window from when the accelerator pedal opening value rises to when the driving motor torque suddenly drops to 0, and this time window does not exceed 200 ms.

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