A new energy automobile motor system operation risk quantitative evaluation method

Abstract

The present application relates to the technical field of automobile safety performance evaluation, and discloses a new energy automobile motor system operation risk quantitative evaluation method, comprising the following steps: step S1, determining the risk indexes related to the risk in the new energy automobile motor system, and collecting and summarizing the operation data corresponding to the risk indexes to form a first data set; step S2, using the first data set to establish the risk quantitative function of each risk index, and determining the constant value in the risk quantitative function; step S3, calculating the risk value of each risk index, and establishing a risk value matrix; step S4, establishing a risk level for determining the risk size according to the risk value of each risk index. The present application has the beneficial effects of realizing real-time calculation of risk severity value, improving the flexibility and accuracy of risk evaluation.

Description

Technical Field

[0001] This invention relates to the field of automotive safety performance evaluation technology, specifically to a method for quantitatively assessing the operational risks of a new energy vehicle motor system. Background Technology

[0002] With the rapid growth in the number of new energy vehicles, the number of accidents involving them has also increased year by year. Among these accidents, those caused by sudden loss of power control in new energy vehicles are particularly prominent, becoming the most concerning type of accident after fires. A sudden loss of control in the power system of a new energy vehicle can cause significant psychological stress and intense anxiety for the driver, greatly increasing the likelihood of operational errors and seriously threatening the safety of passengers. This has led to a surge in consumer complaints about power system malfunctions in new energy vehicles, ultimately resulting in a qualitative change and severely negatively impacting the reputation of automobile manufacturers. Simultaneously, from a regulatory and after-sales perspective, it is necessary to obtain operational risk data on key components of new energy vehicles to assess the risk status of the power system, providing a basis for fault detection and quality after-sales service, and also offering guidance for vehicle improvement to manufacturers. Therefore, obtaining quantifiable values ​​of operational risk indicators for new energy vehicles and reducing the probability of unexpected power system malfunctions has become a consensus among drivers, manufacturers, regulatory authorities, and after-sales departments.

[0003] Currently, the main method for assessing operational risks in new energy vehicles is based on the vehicle's alarm levels, typically categorized as 0, 1, 2, and 3, with the severity increasing progressively. These alarm levels are based on threshold parameters set within vehicle components. When a parameter reaches a set threshold, the component sends a corresponding alarm level signal to the vehicle and monitoring system, simultaneously implementing pre-defined emergency strategies for component safety protection. However, this method has limitations. For instance, relying on alarm levels is affected by the level classification, resulting in a large granularity and unclear description of risks beyond the threshold. Furthermore, this method depends on component alarm signals to predict the next risk, making it difficult for drivers to predict the potential and severity of sudden loss of control of the powertrain. Summary of the Invention

[0004] The present invention aims to provide a method for quantitatively assessing the operational risks of electric motor systems in new energy vehicles, so as to improve the accuracy of predicting the risk of sudden loss of control of the power system in new energy vehicles.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a method for quantitatively assessing the operational risks of a new energy vehicle motor system, comprising the following steps: Step S1: Determine the risk indicators related to risks in the new energy vehicle motor system, and collect and summarize the corresponding operating data to form the first data set. Step S2: Use the first dataset to establish risk quantification functions for each risk indicator and determine the constant values ​​in the risk quantification functions; Step S3: Calculate the risk value of each risk indicator and establish a risk value matrix; Step S4: Establish a risk level for determining the magnitude of risk based on the risk values ​​of each risk indicator.

[0006] The principle and advantages of this solution are as follows: In practical application, it eliminates the need for traditional alarm-based methods. After collecting risk-related indicators of the motor system, it establishes a risk quantification function for each indicator, thereby quantifying the motor's risk from multiple dimensions using a unified scale. Based on the current value of each risk indicator, it determines the constant value in the risk quantification function, ultimately defining the function's expression. After calculating the risk value of each indicator, it establishes a risk level for judging the magnitude of risk based on the motor and controller design parameters and safety control strategies. This allows for accurate risk conversion of real-time data from new energy vehicles, and the judgment is made according to the corresponding risk level standards, yielding an accurate risk magnitude result. Compared to existing technologies, this solution overcomes the shortcomings of traditional methods that only trigger alarms when parameter values ​​reach alarm thresholds. It monitors the operating data of the new energy vehicle's power system in real time and comprehensively judges the current risk magnitude based on multiple risk indicators. Furthermore, this risk judgment result is real-time and dynamic; it does not require reaching a certain value to trigger an alarm but can be displayed to the user in real time, allowing the user to clearly understand the vehicle's risk dynamics at all times, thus enabling reasonable driving arrangements and ensuring driving safety.

[0007] Preferably, as an improvement, the risk indicators include DC voltage, AC current, motor speed, motor torque, electronic control temperature, and motor temperature.

[0008] Beneficial effects: Comprehensive risk assessment from the above multiple dimensions not only ensures a sufficiently wide range of risk reference values, but also allows for risk assessment based on the most relevant indicators, thus guaranteeing the accuracy of risk detection results.

[0009] Preferably, as an improvement, the risk quantification function is: or

[0010] Where Rs is the risk severity value, A is function constant 1, B is function constant 2, IND is the current value of the risk indicator, INDst is the nominal value of the risk indicator, INDmin is the minimum value of the risk indicator, and INDmax is the maximum value of the risk indicator.

[0011] Beneficial effects: By using the above-mentioned risk quantification function to perform specific calculations of risk values, the intermediate calculation process can be reduced, effectively improving the speed and efficiency of risk calculation, thereby improving the calculation efficiency and accuracy of risk values ​​for new energy vehicle power systems.

[0012] Preferably, as an improvement, the constant values ​​in the risk quantification function are determined by substituting the rated values ​​of each risk indicator, the alarm thresholds at each level in the control strategy, and the unified alarm risk value into the risk quantification function to calculate the function constants A and B respectively.

[0013] Beneficial effect: This step enables the accurate and rapid calculation of constants A and B in the risk quantification function, thus ensuring that the function has only a single variable, which in turn helps in the subsequent calculation of risk value.

[0014] Preferably, as an improvement, the constant value of the DC voltage risk quantification function in the risk index includes the constant value of the overvoltage risk function and the constant value of the undervoltage risk function. Beneficial effects: When calculating the constant of DC voltage, since there are two situations of overvoltage and undervoltage, the constant will also change accordingly. Therefore, calculating different constants for different voltage situations can maximize the accuracy of the risk quantification function and the true reliability of the risk calculation results.

[0015] Preferably, as an improvement, the alarm risk value includes a level 2 alarm risk value and a level 3 alarm risk value; the value range of both the level 2 and level 3 alarm risk values ​​is 0-1, and the level 2 alarm risk value is less than the level 3 alarm risk value.

[0016] Beneficial effects: This setting allows for the adjustment of the risk level corresponding to the risk quantification function based on the risk value of the existing alarm levels, thereby making the level distribution more reasonable and the classification and judgment of risks more accurate, thus ensuring the accuracy of risk monitoring of new energy vehicle power systems.

[0017] Preferably, as an improvement, the risk value of each risk indicator is calculated and a risk value matrix is ​​established. The first data set is processed by time slicing, and the current value of each risk indicator in the same time segment is obtained. After substituting into the risk quantification function, the current risk value of each risk indicator is calculated, and a multi-dimensional risk matrix at the current moment is formed.

[0018] Beneficial effects: By using the current values ​​of risk indicators at the same time to calculate the current value of risk and establish a matrix, the risk assessment results can be obtained comprehensively and accurately, ensuring accurate monitoring of power system risks and improving the operational safety of new energy vehicles.

[0019] Preferably, as an improvement, the risk level includes 7 levels in total, from 1 to 7; the risk standards corresponding to the risk level include, level 1 is safe, level 2 does not affect performance, level 3 is close to affecting performance, level 4 is performance limited, level 5 is performance limitation increased, level 6 is close to loss of function, and level 7 is loss of function.

[0020] Beneficial effects: Compared with the existing alarm level classification, this classification is more accurate and comprehensive, and can fully assess the specific situation of the risk, thus providing users with specific and accurate assessment results, making it easier for users to fully understand the risk situation of the power system.

[0021] Preferably, as an improvement, in step S4, when establishing the risk level, the risk boundaries of performance degradation and functional loss are determined based on the design parameters of the motor and controller and the safety control strategy. Finally, other level boundaries are determined based on the change patterns of risk parameters and risk changes.

[0022] Beneficial effects: This setting can greatly ensure the rationality of the evaluation indicators corresponding to the risk level, thereby making the risk assessment results more accurate and ensuring vehicle safety.

[0023] Preferably, as an improvement, the risk boundary for performance degradation is 0.3, and the risk boundary for functional loss is 1.

[0024] Beneficial effects: By defining this risk boundary, the range of the most critical risk values ​​can be determined, thereby ensuring the accuracy of the judgment on the critical risk of loss of power system function and thus ensuring driving safety. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating an embodiment of the method for quantitatively assessing the operational risks of a new energy vehicle motor system according to the present invention.

[0026] Figure 2 This is a schematic diagram illustrating the overvoltage risk quantification in Embodiment 1 of the method for quantitatively assessing the operational risks of a new energy vehicle motor system according to the present invention.

[0027] Figure 3 This is a schematic diagram illustrating the undervoltage risk quantification in Embodiment 1 of the method for quantitatively assessing the operational risks of a new energy vehicle motor system according to the present invention.

[0028] Figure 4 This is a schematic diagram illustrating the AC current risk quantification in Embodiment 1 of the method for quantitatively assessing the operational risks of a new energy vehicle motor system according to the present invention.

[0029] Figure 5 This is a schematic diagram illustrating the motor speed risk quantification in Embodiment 1 of the method for quantitatively assessing the operational risk of a new energy vehicle motor system according to the present invention.

[0030] Figure 6 This is a schematic diagram illustrating the quantification of motor torque risk in Embodiment 1 of the method for quantifying the operational risk assessment of a new energy vehicle motor system according to the present invention.

[0031] Figure 7 This is a schematic diagram illustrating the electronic control temperature risk quantification in Embodiment 1 of the method for quantitatively assessing the operational risks of a new energy vehicle motor system according to the present invention.

[0032] Figure 8 This is a schematic diagram illustrating the quantification of motor temperature risk in Embodiment 1 of the method for quantifying the operational risk assessment of a new energy vehicle motor system according to the present invention. Detailed Implementation

[0033] The following detailed description illustrates the specific implementation method: Example 1: This embodiment is basically as shown in the appendix. Figure 1 The following is a method for quantitatively assessing the operational risks of a new energy vehicle motor system, comprising the following steps: Step S1: Determine the risk indicators related to risks in the new energy vehicle motor system, and collect and summarize the corresponding operating data to form the first data set. Step S2: Use the first dataset to establish risk quantification functions for each risk indicator and determine the constant values ​​in the risk quantification functions; Step S3: Calculate the risk value of each risk indicator and establish a risk value matrix; Step S4: Establish a risk level for determining the magnitude of risk based on the risk values ​​of each risk indicator.

[0034] Specifically, the risk indicators include DC voltage, AC current, motor speed, motor torque, electronic control temperature, and motor temperature, and the risk quantification function is as follows: or

[0035] Where Rs is the risk severity value, A is function constant 1, B is function constant 2, IND is the current value of the risk indicator, INDst is the nominal value of the risk indicator, INDmin is the minimum value of the risk indicator, and INDmax is the maximum value of the risk indicator.

[0036] Specifically, in step S2, the constant values ​​in the risk quantification function are determined by substituting the rated values ​​of each risk indicator, the alarm thresholds at each level in the control strategy, and the unified alarm risk value into the risk quantification function to calculate the function constants A and B respectively. The constant values ​​of the DC voltage risk quantification function include the constant values ​​of the overvoltage risk function and the undervoltage risk function. The alarm risk values ​​include the secondary alarm risk value Rs_2 and the tertiary alarm risk value Rs_3. The values ​​of Rs_2 and Rs_3 are both in the range of 0-1, and Rs_2... <Rs_3。

[0037] 1. Determination of the risk quantification function for DC voltage Vdc index: First, based on the battery type and motor controller settings, determine the nominal values ​​and thresholds, where the nominal voltage Vst = 317V, the level 3 undervoltage threshold Vth_L3 = 198V, the level 2 undervoltage threshold Vth_L2 = 248V, the level 2 overvoltage threshold Vth_H2 = 348V, and the level 3 overvoltage threshold Vth_H3 = 360V. Take Rs_2 = 0.3 and Rs_3 = 1.

[0038] First, calculate the constant values ​​of the overpressure risk function. Substitute Vmax=Vth_H3=360, Vth_H2=348, Vst=317, Rs_2=0.3, Rs_3=1 into the risk quantification function:

[0039] We can calculate that: A = 0.0098, B = 35.915. The overpressure risk quantification function is:

[0040] Then, calculate the constant values ​​of the undervoltage risk function by substituting Vmin=Vth_L3=198, Vth_L2=248, Vst=317, Rs_2=0.3, and Rs_3=1 into the risk quantification function:

[0041] We can calculate that: A = 0.0792, B = -5.396. The undervoltage risk quantification function is:

[0042] Second, the risk quantification function for the alternating current Irms index is determined: First, based on the parameters of the power devices in the motor controller and the setting data of the motor controller, determine the nominal values ​​and thresholds, where the nominal current Ist = 250, the secondary overcurrent threshold Ith_H2 = 300, and the tertiary overcurrent threshold Ith_H3 = 400. Similarly, take Rs_2 = 0.3 and Rs_3 = 1.

[0043] Substituting Imax=Ith_H3=400, Ith_H2=300, Ist=250, Rs_2=0.3, Rs_3=1 into the risk quantification function:

[0044] We can calculate that: A = 0.1399, B = 4.1851. The alternating current risk quantification function is then:

[0045] Third, the risk quantification function for motor speed RPM index is determined. First, based on the motor design parameters and motor controller settings, determine the nominal values ​​and thresholds. The nominal speed of the motor is Nst=3000, the controller is set to a secondary overspeed threshold Nth_H2=9000, and a tertiary overcurrent threshold Nth_H3=10000. Rs_2=0.3 and Rs_3=1.

[0046] Substituting Nmax=Nth_H3=10000, Nth_H2=9000, Nst=3000, Rs_2=0.3, and Rs_3=1 into the risk quantification function:

[0047] We can calculate that: A = 0.000001059, B = 11.427 The motor speed risk quantification function is:

[0048] Fourth, determination of the risk quantification function for the motor torque NM index: First, based on the motor design parameters and motor controller settings, determine the nominal values ​​and thresholds. The nominal torque of the motor is Tst=200, the controller is set to a secondary overspeed threshold Tth_H2=300, and a tertiary overcurrent threshold Tth_H3=400. Rs_2=0.3 and Rs_3=1.

[0049] Substituting Tmax=Tth_H3=400, Tth_H2=300, Tst=200, Rs_2=0.3, Rs_3=1 into the risk quantification function:

[0050] We can calculate that: A = 0.055, B = 4.1851 The motor torque risk quantification function is then:

[0051] 5. Determination of the risk quantification function for the electronically controlled temperature Kmcu index: First, based on the motor design parameters and motor controller settings, determine the nominal values ​​and thresholds. The nominal temperature of the motor controller is Kmcu_st = 60℃, the controller is set to a secondary over-temperature threshold of Kmcu_th_H2 = 85℃, and a tertiary over-temperature threshold of Kmcu+th_H3 = 95℃. Rs_2 = 0.3 and Rs_3 = 1 are taken.

[0052] Substituting Kmcu_max=Kmcu_th_H3=95, Kmcu_th_H2=85, Kmcu_st=60, Rs_2=0.3, Rs_3=1 into the risk quantification function:

[0053] We can calculate that: A = 0.0069136, B = 10.8245 The electronic temperature risk quantification function is:

[0054] 6. Determination of the risk quantification function for the motor temperature Ktm index: First, based on the motor design parameters and motor controller settings, determine the nominal values ​​and thresholds. The nominal temperature of the motor is Ktm_st = 120℃, the controller sets the secondary over-temperature threshold Ktm_th_H2 = 140℃, and the tertiary over-temperature threshold Ktm_th_H3 = the maximum motor temperature Ktm_max = 160℃. Take Rs_2 = 0.3 and Rs_3 = 1.

[0055] Substituting Ktm_max=Ktm_th_H3=160, Ktm_th_H2=140, Ktm_st=120, Rs_2=0.3, Rs_3=1 into the risk quantification function:

[0056] We can calculate that: A = 0.0747, B = 9.0164. The motor temperature risk quantification function is:

[0057] Specifically, in step S3, after determining the risk quantification function corresponding to each risk indicator, the collected data is processed by time slicing to obtain the current value of each risk indicator in the same time segment. After substituting into the corresponding risk quantification function, the current risk value corresponding to each risk indicator is calculated, and a 6-dimensional risk matrix is ​​formed at the current moment.

[0058] As attached Figure 2 and attached Figure 3As shown, the overvoltage risk quantification function of DC voltage generally shows an upward trend, while the undervoltage risk quantification function generally shows a downward trend.

[0059] As attached Figure 4 As shown, the change pattern of the AC current risk quantification function generally shows an upward trend.

[0060] As attached Figure 5 As shown, the risk quantification function of motor speed first remains at zero for a period of time, and then shows a sharp upward trend.

[0061] As attached Figure 6 As shown, the variation law of the motor torque risk quantification function generally shows an upward trend.

[0062] As attached Figure 7 As shown, the change pattern of the electronically controlled temperature risk quantification function is that it first remains at zero for a period of time, and then shows a sharp upward trend.

[0063] As attached Figure 8 As shown, the motor temperature risk quantification function first remains at zero for a period of time, and then shows a sharp upward trend.

[0064] Specifically, in step S4, a risk level is established based on the risk value of each risk indicator to determine the magnitude of the risk. The risk level includes 7 levels in total, from 1 to 7. The risk standards corresponding to the risk level are as follows: Level 1 is safe, Level 2 does not affect performance, Level 3 is close to affecting performance, Level 4 is performance limited, Level 5 is performance limitation increased, Level 6 is close to loss of function, and Level 7 is loss of function.

[0065] Meanwhile, when establishing risk levels, based on the design parameters of the motor and controller and the safety control strategy, the risk boundary for performance degradation is determined to be 0.3, and the risk boundary for functional loss is 1. Finally, based on the variation law of risk parameters and the variation law of risk, other level boundaries are determined, as shown in Table 1.

[0066] Table 1: Risk Level Determination Table

[0067] The specific implementation process of this embodiment is as follows: The first step, based on the control strategy of the new energy vehicle motor system, selects six measurable and continuously changing indicators as risk indicators: DC voltage, AC current, motor speed, motor torque, electronic control temperature, and motor temperature. Operational data corresponding to each risk indicator is collected, and then the rated values ​​of each risk indicator, the alarm thresholds at each level in the control strategy, and the unified alarm risk value are analyzed. To facilitate unified risk value statistics, the level 2 alarm risk value Rs_2 is set to 0.3, and the level 3 alarm risk value Rs_3 is set to 1. These values ​​are then substituted into the risk quantification function to calculate the constants A and B in the risk quantification function corresponding to each risk indicator.

[0068] The second step is to process the collected data into time slices and obtain the current values ​​of the six risk indicators in the same time segment. After substituting them into the risk quantification function, the current risk value of each risk indicator is calculated, and a six-dimensional risk matrix is ​​formed at the current moment.

[0069] The third step involves establishing risk levels based on the risk values ​​of each risk indicator to determine the magnitude of the risk. These risk levels range from 1 to 7, with corresponding risk standards: Level 1 is safe, Level 2 does not affect performance, Level 3 is close to affecting performance, Level 4 has limited performance, Level 5 has increased performance limitation, Level 6 is close to loss of function, and Level 7 has loss of function. Simultaneously, based on the design parameters of the motor and controller, as well as the safety control strategy, the risk boundary for performance degradation is determined to be 0.3, and the risk boundary for loss of function is determined to be 1. Finally, based on the changing patterns of risk parameters and risk changes, the boundaries of other levels are determined to obtain a complete risk level determination table.

[0070] With the continuous depletion of global oil resources and the rapid development of power battery technology, new energy vehicles have been experiencing a golden age of rapid development in recent years. As new energy vehicle technology advances and consumers become more environmentally conscious and accepting of them, the number of new energy vehicles on the market is increasing year by year. However, this widespread adoption and application of new energy vehicles has led to a significant increase in their numbers, which has also exposed some safety issues. These include difficulties in charging, a significant reduction in range during winter, and battery safety concerns. Among these, the sudden loss of power in new energy vehicles, leading to accidents, is particularly prominent and has become the most concerning type of accident after fires. Once the power system malfunctions, it can cause significant psychological stress for the driver, potentially leading to misoperation and ultimately resulting in a serious accident that threatens the lives of passengers.

[0071] Based on this safety issue, relevant technicians have developed an alarm system for the power system of new energy vehicles. This system mainly sets four alarm levels, from 0 to 3, and sets parameter thresholds within vehicle components. When the parameter value reaches the set threshold, the component will upload the corresponding alarm level signal to the vehicle and monitoring system. At the same time, the component will perform safety protection according to the established emergency strategy. Although this method can achieve safety warning to a certain extent, its effect is still not ideal. The main reason is that relying on reaching the threshold to trigger an alarm is too limited. If the threshold requirement is not met in the intermediate stage, the system will not trigger an alarm, but the risk situation of the whole vehicle has already occurred, which poses a serious safety hazard to the safe operation of the vehicle.

[0072] This solution breaks away from conventional early warning methods. It utilizes six measurable, continuously changing indicators in the motor system—DC voltage, AC current, motor speed, motor torque, electronic control temperature, and motor temperature—as risk indicators. These risk indicators are quantified to achieve real-time, continuous risk calculation, analysis, and assessment. No longer is a threshold required for an alarm signal to be issued. The operating data of new energy vehicles is converted into risk severity values ​​in real time, enabling real-time monitoring of risk severity in the system. This provides a fundamental guarantee for continuous risk analysis and solves the problems of lacking real-time risk quantification methods for new energy vehicle motor system operating indicators and the crude risk rating of motor systems, which is inconvenient for management. More importantly, this solution is only related to the current value of the parameters, regardless of the system alarm level. Furthermore, it uses two parameters to adjust the weights of the risk quantification function, which not only allows for flexible adjustment but also makes the risk severity assessment results more accurate. Continuous calculation of risk severity values ​​achieves higher numerical resolution, making risk trend prediction possible. This greatly improves the operational safety of new energy vehicles and protects the personal safety of drivers and passengers.

[0073] The above descriptions are merely embodiments of the present invention. Commonly known structures and characteristics are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are aware of all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A method for quantitatively assessing the operational risks of a new energy vehicle motor system, characterized in that: Includes the following steps: Step S1: Determine the risk indicators related to risks in the new energy vehicle motor system, and collect and summarize the corresponding operating data to form the first data set. Step S2: Use the first dataset to establish risk quantification functions for each risk indicator and determine the constant values ​​in the risk quantification functions; Step S3: Calculate the risk value of each risk indicator and establish a risk value matrix; Step S4: Establish a risk level for determining the magnitude of risk based on the risk values ​​of each risk indicator; The risk quantification function is: or Where Rs is the risk severity value, A is function constant 1, B is function constant 2, IND is the current value of the risk indicator, INDst is the nominal value of the risk indicator, INDmin is the minimum value of the risk indicator, and INDmax is the maximum value of the risk indicator; The constant values ​​in the risk quantification function are determined by substituting the rated values ​​of each risk indicator, the alarm thresholds at each level in the control strategy, and the unified alarm risk value into the risk quantification function to calculate the function constants A and B respectively. The risk levels include 7 levels in total, from 1 to 7; the risk standards corresponding to the risk levels are as follows: Level 1 is safe, Level 2 does not affect performance, Level 3 is close to affecting performance, Level 4 is performance limited, Level 5 is performance limitation increased, Level 6 is close to loss of function, and Level 7 is loss of function.

2. The method for quantitatively assessing the operational risk of a new energy vehicle motor system according to claim 1, characterized in that: The risk indicators include DC voltage, AC current, motor speed, motor torque, electronic control temperature, and motor temperature.

3. The method for quantitatively assessing the operational risk of a new energy vehicle motor system according to claim 1, characterized in that: Among the risk indicators, the constant values ​​of the risk quantification function for DC voltage include the constant values ​​of the overvoltage risk function and the constant values ​​of the undervoltage risk function.

4. The method for quantitatively assessing the operational risk of a new energy vehicle motor system according to claim 1, characterized in that: The alarm risk values ​​include a level 2 alarm risk value and a level 3 alarm risk value; the values ​​of the level 2 alarm risk value and the level 3 alarm risk value are both in the range of 0-1, and the level 2 alarm risk value is less than the level 3 alarm risk value.

5. The method for quantitatively assessing the operational risk of a new energy vehicle motor system according to claim 1, characterized in that: The process of calculating the risk value of each risk indicator and establishing a risk value matrix involves processing the first data set into time slices, obtaining the current value of each risk indicator in the same time segment, substituting it into the risk quantification function to calculate the current risk value of each risk indicator, and forming a multi-dimensional risk matrix for the current moment.

6. The method for quantitatively assessing the operational risk of a new energy vehicle motor system according to claim 1, characterized in that: In step S4, when establishing risk levels, the risk boundaries of performance degradation and functional loss are determined based on the design parameters of the motor and controller and the safety control strategy. Finally, other level boundaries are determined based on the change patterns of risk parameters and risk changes.

7. The method for quantitatively assessing the operational risk of a new energy vehicle motor system according to claim 6, characterized in that: The risk boundary for performance degradation is 0.3, and the risk boundary for loss of function is 1.

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