A method for quantifying the risk of operating indicators of new energy vehicle power systems

Abstract

The present invention relates to the technical field of new energy vehicle performance evaluation, and discloses a method for quantifying the risk of operating indicators of a new energy vehicle power system. The method converts the operating data of the new energy vehicle power system into risk variables using a preset conversion formula, dynamically adjusts the variables of the calculation formula by setting two parameters, A and B, and adjusts the values ​​of A and B through continuous calculation to make the two parameter values ​​closer to the true values, thereby improving the accuracy of the risk variable assessment results, ensuring the operational safety of the vehicle and the personal safety of the driver and passengers. The present invention has the beneficial effect of reducing the risk indicators of existing alarm systems, accurately converting risk variables, and effectively improving the accuracy of vehicle operation risk assessment results.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicle performance evaluation, and in particular to a method for quantifying the risks of operating indicators of a new energy vehicle power system. Background Art

[0002] With the rapid growth in the number of new energy vehicles (NEVs), the number of accidents involving these vehicles has also increased rapidly year by year. Complaints regarding sudden power outages have surged, becoming the most concerning type of accident after fires. Sudden power outages pose a significant threat to personal safety, placing significant psychological stress and anxiety on drivers. This leads to a high rate of complaints regarding powertrain failures in NEVs, negatively impacting the reputation of vehicle manufacturers. From a regulatory and after-sales perspective, understanding the operational risks of key NEV components is crucial to assessing the powertrain's risk status, providing a basis for testing and after-sales service, and providing manufacturers with a tool for vehicle improvement. Therefore, quantifying operational risk indicators for NEVs to reduce the probability of unexpected power outages has become a consensus among drivers, OEMs, regulators, and after-sales service providers.

[0003] New energy powertrain systems monitor a large number of analog quantities and set corresponding alarm thresholds based on these indicators. When the indicator reaches the threshold, protective mechanisms are triggered, impacting the performance and functionality of the vehicle's powertrain, and thus safety. Currently, the operational risk assessment method for new energy vehicles is primarily based on the vehicle's alarm levels, which are generally categorized as 0, 1, 2, and 3, with fault levels increasing in severity. Alarm levels are based on parameter thresholds set within vehicle components. When a parameter reaches the set threshold, the component transmits the corresponding alarm level to the vehicle and monitoring system, and the component's safety protection is implemented according to a pre-defined strategy. Risk quantification methods based on system alarm levels are limited by the alarm level classification and have a high granularity, making it difficult to clearly describe risks outside the threshold. Furthermore, this method relies on component alarm information to generate predictions about the next risk, making it difficult for drivers to predict the potential and severity of powertrain failure risks. Summary of the Invention

[0004] The present invention aims to provide a method for quantifying the risk of operating indicators of a new energy vehicle power system, so as to improve the timeliness of risk assessment of the new energy vehicle power system.

[0005] To achieve the above-mentioned purpose, the present invention adopts the following technical solution: a method for quantifying the risk of operating indicators of a new energy vehicle power system, comprising the following steps:

[0006] Step S1, obtaining operating parameters of a new energy vehicle power system, converting them into risk variables using a preset conversion formula, and determining a first risk quantification general function;

[0007] Step S2, calculating the values ​​of constant coefficients A and B in the first risk quantification universal function to determine the final expression of the first risk quantification universal function;

[0008] Step S3, obtain the current value of the risk indicator of the current time segment, substitute it into the first risk quantification general function to calculate the risk severity value of the current value of the power system risk indicator, and establish a risk level according to the risk severity value to evaluate the safety status of the new energy vehicle power system.

[0009] The principles and advantages of this solution are as follows: In practical application, a general function for quantifying risk indicators is established, along with a method for calculating general function constants and a real-time risk indicator value quantification method. This method converts the operating data of new energy vehicles into risk severity values ​​in real time, enabling real-time monitoring of risk severity within the system and providing a foundation for continuous risk analysis. This improved risk resolution makes risk warnings possible, and by calculating risk severity values ​​based on real-time operating data, real-time calculation of risk severity values ​​avoids system alarms via thresholds, enabling real-time risk assessment. This significantly improves the timeliness of risk assessments for new energy vehicle powertrains, ensures the accuracy of risk assessment results, and ultimately, ensures the safety of new energy vehicles.

[0010] Preferably, as an improvement, the conversion formula is

[0011]

[0012] Among them, Rs is the risk severity value, A is adjustment parameter 1, B is adjustment parameter 2, IND is the current value of the indicator, and INDst is the nominal value of the indicator.

[0013] Beneficial effect: Through this formula, the operating parameters of new energy vehicles can be accurately converted into risk variables, and the function can be adjusted by using the set two parameter variables A and B, thereby effectively improving the accuracy of the risk value quantification results.

[0014] Preferably, as an improvement, in step S3, after obtaining the current value of the risk indicator of the current time segment, the risk severity per unit value of the current value of the power system risk indicator can be calculated using the second risk quantification general function.

[0015] Beneficial effect: The per-unit value is the ratio of the actual value to the reference value. Therefore, by calculating the per-unit value of risk severity, it is easy to compare the characteristics of each parameter. At the same time, it can greatly simplify the calculation process, thereby effectively improving the efficiency of risk assessment.

[0016] Preferably, as an improvement, the second risk quantification general function is

[0017]

[0018] Among them, Rs1 is the per-unit value of risk severity, A is adjustment parameter 1, B is adjustment parameter 2, IND is the current value of the indicator, INDst is the nominal value of the indicator, INDmin is the minimum value of the indicator, and INDmax is the maximum value of the indicator.

[0019] Beneficial effects: Based on the above formula, a new per-unit value calculation model is reintroduced to simplify the calculation and analysis process, improve the rigor of the risk severity value assessment procedure, and further improve the accuracy of the assessment results of the risk value of the new energy vehicle power system.

[0020] Preferably, as an improvement, when calculating the risk severity value of the current value of the risk indicator, regardless of whether the power system itself alarms or not, the acquired operating data is used to perform real-time analysis and calculation of the risk severity value, and then the calculation result is compared with the alarm level to obtain a comparison result, and the alarm threshold of the power system is dynamically adjusted according to the comparison result.

[0021] Beneficial effects: In conventional mode, the alarm system will not be activated until a certain risk value is reached. However, during this process, the risk of the car already exists. If it is not handled in time, it may lead to more serious accidents. Therefore, this solution directly analyzes and evaluates the risk in real time without meeting the system alarm regulations, thereby improving the control of risk assessment and controlling the risk to the minimum range.

[0022] Preferably, as an improvement, when calculating the risk severity value of the current value of the power system risk indicator, the changing law of the first risk quantification general function is corrected by adjusting the values ​​of parameters A and B, and the risk severity values ​​of different types of risk indicators are unified by adjusting the values ​​of parameters A and B.

[0023] Beneficial effects: Different parameter values ​​may lead to different calculation results. In order to further conform to the actual situation, the values ​​of A and B are dynamically adjusted to correct the change law of the function, making the function change law closer to the risk change law. In addition, adjusting the values ​​of parameters A and B to unify the risk severity values ​​of different types of risk indicators can also increase the types of assessable risks, improve the comprehensiveness of risk assessment, and ensure the safety of new energy vehicles.

[0024] Preferably, as an improvement, the parameter A is a natural number greater than 1.

[0025] Beneficial effect: By limiting parameter A to a natural number greater than 1, the positive value of the base in the function can be guaranteed, thereby ensuring that the entire function is an increasing function, improving the correlation between the risk function and the actual risk change trend, and ensuring the accuracy of the risk assessment results.

[0026] Preferably, as an improvement, the parameter A takes the value of the natural constant e.

[0027] Beneficial effect: This setting not only makes calculation easier, but also makes the value of A closer to the true value, thereby further improving the accuracy of the assessment results of the risk severity value.

[0028] Preferably, as an improvement, when obtaining the real-time value of the indicator, the real-time value of the risk indicator is obtained by parsing the CAN network or reading the data uploaded to the monitoring platform by TBOX.

[0029] Beneficial effects: This setting allows for faster and more convenient data acquisition, reducing the intermediate data acquisition process and allowing for sufficient time to be reserved for calculation and analysis, thereby improving the accuracy of risk severity assessments.

[0030] Preferably, as an improvement, when evaluating the risk severity value, the weights are adjusted by dual parameters, and the functional expressions of the changing rules of different risk indicators are dynamically adjusted to accurately determine the risk severity value.

[0031] Beneficial effect: By adjusting the parameters, the accuracy of the function is continuously improved, thereby greatly improving the accuracy of the evaluation results of the risk severity of new energy vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a flow chart of a first embodiment of a method for quantifying the risk of operating indicators of a new energy vehicle power system according to the present invention.

[0033] Figure 2 This is a schematic diagram of the influence of parameter A on the risk value in Example 1 of a new energy vehicle power system operation indicator risk quantification method of the present invention.

[0034] Figure 3 This is a schematic diagram of the influence of parameter B on the risk value in Example 1 of a new energy vehicle power system operation indicator risk quantification method of the present invention. DETAILED DESCRIPTION

[0035] The following is further described in detail through specific implementation methods:

[0036] Example 1:

[0037] The embodiment is basically as shown in the attached Figure 1 As shown: A method for quantifying the risk of operating indicators of a new energy vehicle power system includes the following steps:

[0038] Step S1, obtaining operating parameters of a new energy vehicle power system, converting them into risk variables using a preset conversion formula, and determining a first risk quantification general function;

[0039] Step S2, calculating the values ​​of constant coefficients A and B in the first risk quantification universal function to determine the final expression of the first risk quantification universal function;

[0040] Step S3, obtain the current value of the risk indicator of the current time segment, substitute it into the first risk quantification general function to calculate the risk severity value of the current value of the power system risk indicator, and establish a risk level according to the risk severity value to evaluate the safety status of the new energy vehicle power system.

[0041] Specifically, the conversion formula is:

[0042]

[0043] Among them, Rs is the risk severity value, A is adjustment parameter 1, B is adjustment parameter 2, IND is the current value of the indicator, and INDst is the nominal value of the indicator.

[0044] Specifically, after obtaining the current value of the risk indicator of the current time segment, the risk severity per unit value of the current value of the power system risk indicator can be calculated using the second risk quantification general function.

[0045] Specifically, the second risk quantification general function of the per-unit value of risk severity is:

[0046]

[0047] At the same time, the general function of risk quantification also includes:

[0048] Or Rs=Ae B*IND

[0049] Or Rs=A*IND B Or Rs=A*IND 2 +B*IND+C

[0050] Among them, Rs1 is the per-unit value of risk severity, Rs is the risk severity value, A is adjustment parameter 1, B is adjustment parameter 2, C is adjustment parameter 3, IND is the current value of the indicator, INDst is the nominal value of the indicator, INDmin is the minimum value of the indicator, INDmax is the maximum value of the indicator, and INDmax / min is the boundary extreme value.

[0051] Specifically, when obtaining the current value of the risk indicator of the current time segment, the real-time value of the risk indicator is obtained by parsing the CAN network or reading the data uploaded to the monitoring platform by TBOX.

[0052] The risk-related indicators of the power system of new energy vehicles include 5 risk indicators, namely cell voltage, DC current, cell temperature, insulation resistance, and SOC. And 11 states, such as high cell voltage, low cell voltage, large cell voltage difference, large DC current, high cell temperature, low cell temperature, large cell temperature difference, low Vcell insulation resistance, high SOC, low SOC, and SOC jump, are used as risk quantification index items.

[0053] Specifically, high cell voltage means that the current value of the cell voltage is greater than the nominal value of the cell voltage; low cell voltage means that the current value of the cell voltage is less than the nominal value of the cell voltage; large cell voltage difference means that at the same moment, the difference between the maximum and minimum values of the cell voltage is greater than the threshold; large DC current means that the ratio of the cell discharge current to the allowable rated current in the current state is greater than 1; high cell temperature means that the current value of the cell temperature is higher than the optimal working temperature value of the cell; low cell temperature means that the current value of the cell temperature is lower than the optimal working temperature value of the cell; large cell temperature difference means that at the same moment, the difference between the maximum and minimum values of the cell temperature is greater than the threshold; low Vcell insulation resistance means that the insulation resistance is lower than the normal value; high SOC means that the SOC value is close to 100%; low SOC means that the SOC value is close to 0%; SOC jump means that within a certain period of time, the change value of SOC is higher than the threshold.

[0054] During the change process from the nominal value to the maximum or minimum value, the severity of the risk varies. By adjusting the values of parameters A and B to correct the change law of the first risk quantification general function, the change law of the function can be made closer to the risk change law. At the same time, adjusting the values of parameters A and B can unify the severity values of different types of risk indicators.

[0055] According to the battery type and the set data of the BMS, the nominal value and the threshold are determined. In this embodiment, the nominal voltage Vcell_st = 3.2V, the third-level undervoltage threshold Vcell_th_L3 = 2.0V, the second-level undervoltage threshold Vcell_th_L2 = 2.5V, the second-level overvoltage threshold Vcell_th_H2 = 3.60V, and the third-level overvoltage threshold Vcell_th_H3 = 3.65V.

[0056] To facilitate risk value statistics and unify the risk values for alarming, the value ranges of the second-level alarm risk value Rs_2 and the third-level alarm risk value Rs_3 are 0 - 1, and Rs_2 < Rs_3. Specifically, Rs_2 = 0.3 is selected; Rs_3 = 1.

[0057] 1. Calculate the constant value of the cell overvoltage risk function

[0058] When the cell voltage is high, that is, the current cell voltage is greater than the nominal cell voltage, there is a risk of overvoltage. Vmax = Vcell_th_H3 = 3.65; Vcell_th_H2 = 3.55; Vst = Vcell_st = 3.2.

[0059] Substitute the above data into the general function:

[0060] The solution is: A = 0.3; B = 64. Therefore, the functional relationship between overvoltage risk and V / Vst is as follows:

[0061]

[0062] Rs=A*IND B

[0063] Or substitute into a general function:

[0064] Another expression for overvoltage risk can be obtained:

[0065] 2. Calculate the constant value of the undervoltage risk function

[0066] When the cell voltage is low, that is, the current cell voltage value is less than the nominal cell voltage value, there is a risk of undervoltage in the cell voltage. Vmin=Vth_L3=2.0;Vth_L2=2.5;Vst=3.2,Rs_2=0.3;Rs_3=1

[0067] Substituting into the general function:

[0068] We can solve for: A = 0.3; B = -6.4, and the functional relationship between undervoltage risk and V / Vst is as follows:

[0069]

[0070] Rs=Ae B*IND

[0071] Or substitute into the general function:

[0072] Rs_L_V=123.46e -2.408Vcell

[0073] We can get another expression:

[0074] 3. Calculate the constant value of the voltage differential risk function

[0075] When the voltage difference of the battery cells is large at the same time, that is, the voltage difference between the maximum value and the minimum value is greater than the predetermined value, the battery cell voltages are discrete and there is a risk of inconsistency. Vdif_max = Vth_L3 = 0.3; Vdif_th_L2 = 0.2; Vdif_st = 0.1; Rs_2 = 0.3; Rs_3 = 1

[0076] Substitute into the general function:

[0077] It can be solved to obtain: A = 0.3; B = 1, then the functional relationship between the risk of large voltage difference and the voltage difference is as follows:

[0078]

[0079] Or substitute into the general function: Rs = Ae B*IND

[0080] Rs_dif = 0.027e 12.04*Vdif <able to obtain another expression:

[0081]

[0082] Then, determine the risk quantification function of the DC current Idc index. According to the current output capacity of the battery in the current state, determine the nominal value and the threshold value. In this embodiment, the nominal current Ist = 200, the secondary overcurrent threshold Ith_H2 = 260, the tertiary overcurrent threshold Ith_H3 = 400, and the value ranges of the secondary alarm risk value Rs_2 and the tertiary alarm risk value Rs_3 are 0 - 1, and Rs_2 < Rs_3. Specifically, select Rs_2 = 0.3; Rs_3 = 1.

[0083] IV. Calculate the constant value of the overcurrent risk function

[0084] Imax = Ith_H3 = 400; Ith_H2 = 300; Ist = 250; Rs_2 = 0.3; Rs_3 = 1

[0085] Substitute into the general function:

[0086] It can be solved to obtain: A = 0.3; B = 5, then the functional relationship between the overcurrent risk Rs_O_I of the current and the current I and the nominal current Ist is as follows:

[0087] <able to obtain another expression:

[0088] B*IND

[0089]

[0090] Next, the risk quantification function of the state of charge (SOC) indicator is determined, and the risks of high SOC, low SOC and SOC jump states are calculated respectively.

[0091] According to the battery design parameters and battery controller setting data, the nominal values ​​and thresholds are determined: the battery's nominal SOCst = 60%, the second-level SOC high threshold SOCth_H2 = 95%, the third-level threshold SOCth_H3 = SOCmax = 100%, SOCth_L2 = 20%, SOCth_L3 = 5%; the battery SOC standard change rate SOCrate_st = 100% / h, SOCrate_H2 = 170%, SOCrate_H3 = 200%; Rs_2 = 0.3; Rs_3 = 1.

[0092] 5. Calculate the constant value of the SOC high-risk function

[0093] Substitute the above data into the general function:

[0094] The solution is: A = 0.3; B = 12. The functional relationship between the SOC high risk and the current SOC and rated SOC is as follows:

[0095]

[0096] Or substitute the universal function: Rs = Ae B*IND , we can get another expression for SOC high risk:

[0097] 6. Calculate the SOC low-risk function constant value

[0098] Substitute the above data into the general function:

[0099] The solution is: A = 0.3; B = -4. The functional relationship between SOC low risk and current SOC and rated SOC is as follows:

[0100]

[0101] Or substitute the universal function: Rs = Ae B*IND , we can get another expression of SOC low risk:

[0102] 7. Calculate the constant value of the SOC change rate high risk function

[0103] Substitute the above data into the general function:

[0104] The solution is: A = 0.3; B = 3.333. The functional relationship between SOC low risk and current SOC and rated SOC is as follows:

[0105]

[0106] Or substitute the universal function: Rs = Ae B*IND , we can get another expression of SOC low risk:

[0107] Rs_H_SOCrate=0.0003e 4.0132*SOCrate

[0108] Next, the risk quantification function of the insulation resistance (ISLr) indicator is determined. Taking 100Ω / V as the minimum requirement, the insulation resistance threshold is set. The nominal insulation value is ISLr_st = 20MΩ, the secondary threshold is ISLr = 1MΩ, and the tertiary threshold is ISLr_max = 0.03MΩ; Rs_2 = 0.3; and Rs_3 = 1.

[0109] 8. Calculate the insulation risk function constant value

[0110] Substitute the above data into the general function:

[0111] The solution is: A = 0.300; B = -10.309. The functional relationship between insulation risk and insulation resistance is as follows:

[0112] or

[0113] Rs_ISLr_L=+10 -5 *ISLr 6 -0.0005*ISLr 5 +0.0094*ISLr 4 -0.0884*ISLr 3 +0.4353*ISLr 2 -1.0603*ISLr+1.0197

[0114] Finally, the risk quantification function for the cell temperature indicator Tcell is determined, determining the risk function constant values ​​for the two states of high and low cell temperatures. Based on the battery design parameters and battery controller settings, the nominal values ​​and thresholds are determined: the battery's nominal temperature Tcell_st = 35°C, the second-level overtemperature threshold Tcell_th_H2 = 55°C, the third-level overtemperature threshold Tmcu_th_H3 = 65°C, the second-level low-temperature threshold Tcell_th_L2 = 1°C, and the third-level low-temperature threshold Tmcu_th_H3 = -5°C; Rs_2 = 0.3; and Rs_3 = 1.

[0115] 9. Calculate the constant value of the high-risk function of the cell temperature

[0116] Substitute the above data into the general function:

[0117] We can solve for: A = 0.3; B = 3.5. The functional relationship between the high cell temperature risk and the cell temperature is as follows:

[0118]

[0119] Or substitute the universal function: Rs = Ae B*IND , we can get another value of Rs_H_Tcell=0.0004e for high cell temperature risk 0.1204*Tcell expression:

[0120] 10. Calculate the constant value of the low-risk function of the cell temperature

[0121] Substitute the above data into the general function:

[0122] We can solve for: A = 0.3; B = -5.833. The functional relationship between the cell temperature low risk and the cell temperature is as follows:

[0123]

[0124] Or substitute the universal function: Rs = Ae B*Tcell , we can get another expression for the low risk of cell temperature: Rs_L_Tcell=0.3667e -0.201*Tcell

[0125] After all risk indicator functions determine the constants, the risk of each risk indicator value can be calculated, the current values ​​of the five risk indicators in the same time segment can be obtained, and the state risk function can be substituted according to the state to calculate the current risk value of each indicator, forming a multi-dimensional risk value at the current moment.

[0126] A risk level is established based on the risk value of each risk indicator to determine the size of the risk. The risk level includes 7 levels from 1 to 7, and the risk standards corresponding to the risk levels include: Level 1 is safe, Level 2 is no impact on performance, Level 3 is close to impacting 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.

[0127] In this embodiment, the performance degradation risk boundary is 0.3, and the function loss risk is 1. Other level boundaries are determined based on the risk parameter change law and the risk change law, as shown in Table 1.

[0128] Table 1: Risk level determination table

[0129] Risk Level Risk Standards Risk Range 1 Safety 0.00-0.15 2 No impact on performance 0.16-0.25 3 Proximity affects performance 0.26-0.29 4 Limited performance 0.30-0.50 5 Increased performance limitations 0.51-0.70 6 Close to loss of function 0.71-0.99 7 Loss of function ≥1

[0130] Taking the cell voltage indicator in new energy vehicle energy storage systems as an example, the risk varies differently with cell voltage for different systems. Lithium iron phosphate battery cells have a slow charge and discharge voltage change, while ternary battery cells have a slow charge and discharge voltage change but a high charge and discharge voltage. The same cell voltage represents different risk severity levels. By adjusting the values ​​of parameters A and B, this method can be adapted to assess different types of risk indicators.

[0131] As attached Figure 2 and attached Figure 3 As shown in the figure, by adjusting the AB value, the risk curve can be flexibly adjusted to make the risk indicator closer to the actual situation.

[0132] On the other hand, by adjusting the A and B values, the risk values ​​of different risk indicators can be unified so that different indicator values ​​correspond to the same risk value, which facilitates unified analysis and evaluation of various risk indicators.

[0133] The specific implementation process of this embodiment is as follows:

[0134] The first step is to obtain the operating parameters of the new energy vehicle power system, including speed, acceleration, braking distance, etc., and then use the preset conversion formula to convert them into risk variables.

[0135] In the second step, the real-time value of the risk indicator is obtained by parsing the CAN network or reading the data uploaded to the monitoring platform by TBOX. Two different levels of alarm thresholds and risk expectation values ​​are taken and substituted into the general function of risk quantification. The value of parameter A is replaced by the natural constant e, and the values ​​of constants A and B are solved so that the function variables are only the risk indicator values.

[0136] In the third step, the obtained indicator values ​​are substituted into the risk function to calculate the risk severity value of the current value of each risk indicator of the power system, and a risk level is established based on the risk value of each risk indicator to determine the size of the risk. Finally, the risk level is used to judge the safety status of the new energy vehicle power system.

[0137] With the rapid growth in the number of new energy vehicles (NEVs), the number of accidents involving them has also increased rapidly year by year. Among these, complaints regarding unexpected power outages have surged, becoming the most concerning type of accident after fires. Sudden power outages pose a significant threat to personal safety, causing significant psychological stress and anxiety for drivers. This leads to a high rate of complaints regarding powertrain failures in NEVs, negatively impacting the reputation of vehicle manufacturers. From a regulatory and after-sales perspective, it is also necessary to understand the operational risks of key NEV components to assess the powertrain's risk status, providing a basis for testing and after-sales service, and providing manufacturers with a tool for vehicle improvements. Therefore, obtaining quantified operational risk indicators for NEVs to reduce the probability of unexpected power outages has become a consensus among drivers, OEMs, regulators, and after-sales service. NEV powertrains monitor a wide range of analog quantities and set alarm thresholds based on these indicators. When these thresholds are reached, protective mechanisms are triggered, impacting the performance and functionality of the vehicle's powertrain, and thus safety.

[0138] Currently, the method for assessing operating risks in new energy vehicles is primarily based on the vehicle's alarm levels. Alarm levels are generally categorized as 0, 1, 2, and 3, with fault levels increasing in severity. Alarm levels are based on parameter thresholds set within vehicle components. When a parameter value reaches the threshold, the component transmits the corresponding alarm level to the vehicle and monitoring system. Simultaneously, component safety protection is implemented according to established strategies. Risk quantification methods based on system alarm levels are affected by the alarm level classification, resulting in high granularity and unclear descriptions of risks beyond the threshold. Furthermore, this method relies on component alarm information to generate predictions about the next risk. This makes it difficult for drivers to predict the potential and severity of power outage risks.

[0139] In this solution, the traditional alarm mode is directly avoided to prevent the system from failing to issue early warning information in time due to the alarm level not meeting the standard. The preset conversion formula is used to convert the vehicle's operating data into risk variables, and the parameters A and B are used to adjust the weights of the two parameters. The risk change rate can be adjusted flexibly, and the change rules of different risk indicators can be accurately described by adjusting the two parameters. The calculation results are accurately close to the risk severity value. More importantly, this solution can continuously calculate the risk severity value, and the risk identification rate is higher, ultimately achieving the purpose of accurately assessing the vehicle's operating risk. Compared with the level alarm method, this solution can perform risk assessment in real time, improve the timeliness of the power system risk assessment, and give risk assessment results more accurately. There is no need to wait until the risk exceeds the threshold before alarming, and the risk can be nipped in the bud, thereby maximizing the safety of the vehicle's operation and the user's driving safety.

[0140] Example 2:

[0141] This embodiment is basically the same as embodiment 1, with the difference that: when calculating the risk severity value of the current value of the risk indicator, regardless of whether the power system itself alarms, the risk severity value is analyzed and calculated in real time using the acquired operating data, and then the calculation result is compared and analyzed with the alarm level to obtain a comparison result, and the alarm threshold of the power system is dynamically adjusted according to the comparison result.

[0142] Specifically, if the risk severity value is greater than the alarm value of the current alarm level and the current power system has not sounded an alarm, it means that the preset alarm threshold of the power system is too high, and the alarm threshold should be lowered accordingly; if the risk severity value is less than the alarm value of the current alarm level, and the power system has already sounded an alarm, it means that the preset alarm threshold of the power system is too low, and the alarm threshold of the alarm level should be increased accordingly.

[0143] The specific implementation process of this embodiment is the same as that of the first embodiment, except that:

[0144] In the third step, regardless of whether the power system itself alarms or not, the acquired operating data is used to perform real-time analysis and calculation of the risk severity value, and the acquired index value is substituted into the first risk quantification general function or the second risk quantification general function to calculate the risk severity value of the current value of the power system risk index. After obtaining the risk severity value calculation result, the alarm value and alarm level of the power system at this moment are collected, and the risk severity value is compared and analyzed with the alarm level, and the alarm threshold of the power system is dynamically adjusted according to the comparison result.

[0145] By calculating the risk severity value of the new energy vehicle power system in real time and comparing it with the system's own alarm value and alarm level, the rationality of the alarm level and alarm threshold settings can be quickly analyzed, and the alarm threshold can be dynamically adjusted according to the actual detection situation, so that the accuracy of the system's own alarm can also be guaranteed, thereby providing double reliable protection for the operation safety of new energy vehicles.

[0146] The above is only an embodiment of the present invention. Common knowledge such as the known specific structures and characteristics in the scheme is not described in detail here. Ordinary technicians in the field are aware of all common technical knowledge in the technical field of the invention before the application date or priority date, can obtain all existing technologies in the field, and have the ability to apply conventional experimental means before that date. Ordinary technicians in the field can improve and implement this scheme in combination with their own abilities under the inspiration given by this application. Some typical known structures or known methods should not become obstacles for ordinary technicians in the field to implement this application. It should be pointed out that for those skilled in the art, without departing from the structure of the present invention, several variations and improvements can be made, which should also be regarded as the scope of protection of the present invention. These will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A method for quantifying the risk of operating indicators of a new energy vehicle power system, characterized by: The following steps are involved: Step S1, obtaining operating parameters of a new energy vehicle power system, converting them into risk variables using a preset conversion formula, and determining a first risk quantification general function; Step S2, calculating the values ​​of constant coefficients A and B in the first risk quantification universal function to determine the final expression of the first risk quantification universal function; Step S3, obtaining the current value of the risk indicator for the current time segment, substituting it into the first risk quantification general function to calculate the risk severity value of the current value of the power system risk indicator, and establishing a risk level based on the risk severity value to evaluate the safety status of the new energy vehicle power system; The conversion formula is: Among them, Rs is the risk severity value, A is adjustment parameter 1, B is adjustment parameter 2, IND is the current value of the indicator, and INDst is the nominal value of the indicator.

2. The method for quantifying the risk of operating indicators of a new energy vehicle power system according to claim 1 is characterized by: In step S3, after obtaining the current value of the risk indicator for the current time segment, the risk severity per unit value of the current value of the power system risk indicator is calculated using the second risk quantification general function; The second risk quantification general function is: Among them, Rs1 is the per-unit value of risk severity, A is adjustment parameter 1, B is adjustment parameter 2, IND is the current value of the indicator, INDst is the nominal value of the indicator, INDmin is the minimum value of the indicator, and INDmax is the maximum value of the indicator.

3. The method for quantifying the risk of operating indicators of a new energy vehicle power system according to claim 1 is characterized by: When calculating the risk severity value of the current value of the risk indicator, regardless of whether the power system itself has issued an alarm, the risk severity value is analyzed and calculated in real time using the acquired operating data, and then the calculation result is compared with the alarm level to obtain a comparison result, and the alarm threshold of the power system is dynamically adjusted according to the comparison result.

4. The method for quantifying the risk of operating indicators of a new energy vehicle power system according to claim 1 is characterized by: When calculating the risk severity value of the current value of the power system risk indicator, the change law of the first risk quantification general function is corrected by adjusting the values ​​of parameters A and B. At the same time, the risk severity values ​​of different types of risk indicators are unified by adjusting the values ​​of parameters A and B.

5. The method for quantifying the risk of operating indicators of a new energy vehicle power system according to claim 4 is characterized in that: The parameter A is a natural number greater than 1.

6. The method for quantifying the risk of operating indicators of a new energy vehicle power system according to claim 4 is characterized by: The parameter A takes the value of the natural constant e.

7. The method for quantifying the risk of operating indicators of a new energy vehicle power system according to claim 1 is characterized by: When obtaining the current value of the risk indicator of the current time segment, the real-time value of the risk indicator is obtained by parsing the CAN network or reading the data uploaded to the monitoring platform by TBOX.

8. The method for quantifying the risk of operating indicators of a new energy vehicle power system according to claim 1 is characterized by: When evaluating the risk severity value, the weights are adjusted through dual parameters, and the functional expression of the changing rules of different risk indicators is dynamically adjusted to accurately determine the risk severity value.

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