New energy automobile big data-based power battery safety evaluation method and device

Abstract

This application discloses a method and apparatus for evaluating the safety of power batteries based on big data from new energy vehicles. The method includes: acquiring single-vehicle data; dividing the single-vehicle data to obtain at least one charging condition segment data and / or at least one driving condition segment data; obtaining at least one charging fault parameter and / or at least one driving fault parameter based on the charging condition segment data; and obtaining a fault score value for each charging fault parameter and / or a fault score value for each driving fault parameter. This method for evaluating the safety of power batteries based on big data from new energy vehicles considers both the severity of individual charging fault parameters and / or driving fault parameters, as well as the overall effect of all faults, making it highly applicable.

Description

Technical Field

[0001] This application relates to the field of power battery safety technology, specifically to a power battery safety evaluation method and a power battery safety evaluation device based on new energy vehicle big data. Background Technology

[0002] The safety of electric vehicles has always been a hot topic of concern, and how to effectively evaluate the safety of power batteries has become a research focus for many scholars. Current technologies have limited dimensions for considering fault risks, only considering a single operating condition such as charging or driving, or considering a small number of faults, and lack comprehensive evaluation results and comprehensive assessment level classifications, thus failing to intuitively reflect the safety status and existing fault risks of power batteries.

[0003] Therefore, there is a need for a technical solution to address or at least mitigate the aforementioned shortcomings of existing technologies. Summary of the Invention

[0004] The purpose of this invention is to provide a method for evaluating the safety of power batteries based on big data from new energy vehicles, which at least solves one of the above-mentioned technical problems.

[0005] One aspect of the present invention provides a method for evaluating the safety of power batteries based on big data from new energy vehicles, the method comprising:

[0006] Obtain bicycle data;

[0007] The single-vehicle data is divided to obtain at least one charging condition segment data and / or at least one driving condition segment data.

[0008] Based on the charging condition segment data, obtain at least one charging fault parameter and / or at least one driving fault parameter;

[0009] Obtain the fault score value for each charging fault parameter and / or obtain the fault score value for each driving fault parameter.

[0010] Optionally, the power battery safety evaluation method based on big data from new energy vehicles further includes:

[0011] Obtain the weight information for each charging fault parameter and / or driving fault parameter;

[0012] The charging fault parameters and / or driving fault parameters are classified to form at least one fault group, and each fault group includes at least one charging fault parameter and / or driving fault parameter.

[0013] Correlation analysis is performed on various charging fault parameters and / or driving fault parameters located in the same group to obtain correlation information;

[0014] The safety score of each fault group is obtained by acquiring the fault score of each charging fault parameter and / or the fault score of each driving fault parameter, the correlation information, and the weight information of each charging fault parameter and / or driving fault parameter.

[0015] Optionally, the power battery safety evaluation method based on big data from new energy vehicles further includes:

[0016] The vehicle's battery safety response level is classified based on the fault score value of each charging fault parameter and / or the fault score value of each driving fault parameter, as well as the safety score value of each fault group.

[0017] Optionally, the step of dividing the single-vehicle data to obtain at least one charging condition segment data and / or at least one driving condition segment data includes:

[0018] Determine whether the bicycle data can be segmented using the charging gun connection status signal and the high-voltage power-on status signal. If so, segment the bicycle data using the charging gun connection status signal and the high-voltage power-on status signal.

[0019] Optionally, the step of dividing the single-vehicle data to obtain at least one charging condition segment data and / or at least one driving condition segment data further includes:

[0020] Determine whether the bicycle data can be segmented using the charging gun connection status signal and the high voltage power-on status signal. If not, segment the bicycle data using the charging gun connection status signal and the main positive relay status signal.

[0021] Optionally, each charging condition segment data includes at least one charging condition start frame and one charging condition end frame;

[0022] Each driving condition segment data includes at least one driving condition start frame and one driving condition end frame; wherein...

[0023] Each charging condition segment data is obtained by acquiring the charging condition start frame and the charging condition end frame in the following manner:

[0024] The single-vehicle data is traversed. If three consecutive frames of signals show that the charging gun is in the charging state, the first frame of the three frames is selected as the charging start frame of the charging condition segment data.

[0025] After obtaining the charging start frame, the data of each frame after the charging start frame is traversed. If the charging gun connection status of three consecutive frames is either charging complete or not charging, the first frame of the three frames is selected as the charging end frame of the charging condition segment data.

[0026] Each driving condition segment data is obtained by acquiring the driving condition start frame and driving condition end frame in the following manner:

[0027] The single vehicle data is traversed. If three consecutive frames of signals show that the charging gun connection status is not charging and the high voltage power-on status signal is power-on, then the first frame of the three frames is selected as the driving start frame of the driving condition segment data.

[0028] After obtaining the driving start frame, the data of each frame after the driving start frame is traversed. If the high voltage power-on status signal of three consecutive frames is not powered on, the first frame of the three frames is selected as the driving end frame of the driving condition segment data.

[0029] Optionally, the charging fault parameters include charging SOC consistency abnormality parameters and charging self-discharge rate parameters;

[0030] The driving fault parameters include driving SOC consistency abnormality parameters, driving self-discharge rate parameters, and driving insulation abnormality parameters.

[0031] Optionally, the fault score values ​​for the charging SOC consistency anomaly parameter, charging insulation anomaly parameter, driving SOC consistency anomaly parameter, and driving insulation anomaly parameter are obtained according to the following formulas:

[0032] in,

[0033] σ i1 , σ i2 , σ i3 There are three fault level thresholds, where σ i1 <σ i2 <σ i3 ;

[0034] i represents the i-th charging fault parameter or driving fault parameter.

[0035] Optionally, the fault score value of the charging self-discharge rate parameter is obtained in the following manner:

[0036] The fault score value of the charging self-discharge rate parameter of each battery cell is obtained based on the self-discharge rate value of each battery cell.

[0037] Determine whether there is only one battery cell whose charging self-discharge rate parameter fault score value is less than the preset self-discharge rate score value. If so, obtain the charging self-discharge rate parameter fault score value of the battery cell whose self-discharge rate parameter is less than the preset self-discharge rate score value as the charging self-discharge rate parameter fault score value.

[0038] Determine whether only one battery cell has a fault score value for its self-discharge rate parameter that is lower than the preset self-discharge rate score value. If not, use the following formula to obtain the fault score value for the self-discharge rate parameter:

[0039] in,

[0040] G min The minimum score among all individual battery cells; G i自放电 The fault score value is the self-discharge rate parameter of the i-th battery cell.

[0041] Optionally, the weighting information for each charging fault parameter and / or driving fault parameter includes:

[0042] Obtain the scale table;

[0043] Construct a judgment matrix for any two of the charging fault parameters and / or at least one driving fault parameters based on the scale table.

[0044] The weight information is obtained based on the judgment matrix.

[0045] Optionally, after obtaining the weight information of each charging fault parameter and / or driving fault parameter, the power battery safety evaluation method based on new energy vehicle big data further includes:

[0046] Determine whether each of the acquired judgment matrices meets the preset conditions. If not, correct the judgment matrices that do not meet the conditions so that they meet the preset conditions.

[0047] Alternatively, the following formula can be used to obtain correlation information:

[0048] in,

[0049] m ij This represents the correlation between the standardized fault parameter i and the fault parameter j; M ij This represents the correlation between fault parameter i and fault parameter j before standardization; M min M represents the minimum value of the correlation parameter before standardization. max This represents the maximum value of the correlation parameter before standardization.

[0050] Alternatively, the security score can be obtained using the following formula:

[0051] in,

[0052] W t The following formula is used to obtain it:

[0053] in,

[0054] d i The following formula is used to obtain it:

[0055] d i =(100-G i )×w i ;in,

[0056] W t For the deduction score in the fault classification of t, G i For fault parameter i, the fault score value, w i For the weight information of fault parameter i, d max-t For each fault parameter in the same fault category, the one with the largest d i The numerical value; d i This represents the score to be deducted for the i-th fault parameter; d max-t This indicates that among the fault parameters under the same fault category, d i The maximum value.

[0057] This application also provides a power battery safety evaluation device based on big data from new energy vehicles, the power battery safety evaluation device based on big data from new energy vehicles includes:

[0058] A single-vehicle data acquisition module, wherein the single-vehicle data acquisition module is used to acquire single-vehicle data;

[0059] A segmentation module is used to segment the single-vehicle data to obtain at least one charging condition segment data and / or at least one driving condition segment data.

[0060] The fault parameter acquisition module is used to acquire at least one charging fault parameter and / or at least one driving fault parameter based on the charging condition segment data.

[0061] The scoring module is used to obtain a fault score value for each charging fault parameter and / or a fault score value for each driving fault parameter.

[0062] This application also provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the computer program, it implements the power battery safety evaluation method based on big data of new energy vehicles as described above.

[0063] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the method for constructing the reliability cyclic operating conditions of an electric drive system based on user big data as described above.

[0064] Beneficial effects

[0065] The power battery safety evaluation method based on big data of new energy vehicles proposed in this application considers both the severity of individual charging fault parameters and / or driving fault parameters, as well as the comprehensive effect of all faults, making it highly applicable. The proposed data segmentation method can effectively segment electric vehicle charging condition data segments and driving condition data segments, reducing the risk of segmentation errors caused by data loss, false alarms, and other issues. The proposed self-discharge rate fault evaluation method uses the monthly self-discharge rate of individual battery cells to reflect the degree of fault, analyzing self-discharge rate faults from the perspective of individual battery cells. The proposed dimensionless fault parameter design method can effectively normalize different types of fault parameters. Attached Figure Description

[0066] Figure 1 This is a flowchart illustrating a method for evaluating the safety of power batteries based on big data from new energy vehicles, according to an embodiment of this application.

[0067] Figure 2 This is a schematic diagram of an electronic device capable of implementing the power battery safety evaluation method based on big data from new energy vehicles, as described in an embodiment of this application.

[0068] Figure 3 This is a schematic diagram of segment division according to an embodiment of this application. Detailed Implementation

[0069] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be described in more detail below with reference to the accompanying drawings. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some, but not all, embodiments of this application. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. The embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0070] Figure 1 This is a flowchart illustrating a method for evaluating the safety of power batteries based on big data from new energy vehicles, according to an embodiment of this application.

[0071] like Figure 1 The power battery safety evaluation method based on big data from new energy vehicles shown includes:

[0072] Step 1: Obtain bicycle data;

[0073] Step 2: Divide the single-vehicle data to obtain at least one charging condition segment data and / or at least one driving condition segment data;

[0074] Step 3: Obtain at least one charging fault parameter and / or at least one driving fault parameter based on the charging condition segment data;

[0075] Step 4: Obtain the fault score value for each charging fault parameter and / or obtain the fault score value for each driving fault parameter.

[0076] The power battery safety evaluation method based on big data of new energy vehicles proposed in this application considers both the severity of individual charging fault parameters and / or driving fault parameters, as well as the comprehensive effect of all faults, making it highly applicable. The proposed data segmentation method can effectively segment electric vehicle charging condition data segments and driving condition data segments, reducing the risk of segmentation errors caused by data loss, false alarms, and other issues. The proposed self-discharge rate fault evaluation method uses the monthly self-discharge rate of individual battery cells to reflect the degree of fault, analyzing self-discharge rate faults from the perspective of individual battery cells. The proposed dimensionless fault parameter design method can effectively normalize different types of fault parameters.

[0077] In this embodiment, the power battery safety evaluation method based on big data from new energy vehicles further includes:

[0078] Obtain the weight information of each charging fault parameter and / or driving fault parameter. Specifically, calculate each of the charging fault parameters and / or driving fault parameters in a pairwise manner to obtain the weight information of each charging fault parameter and / or driving fault parameter.

[0079] The charging fault parameters and / or driving fault parameters are classified to form at least one fault group, and each fault group includes at least one charging fault parameter and / or driving fault parameter.

[0080] Correlation analysis is performed on various charging fault parameters and / or driving fault parameters located in the same group to obtain correlation information;

[0081] The safety score of each fault group is obtained by acquiring the fault score of each charging fault parameter and / or the fault score of each driving fault parameter, the correlation information, and the weight information of each charging fault parameter and / or driving fault parameter.

[0082] In this embodiment, the power battery safety evaluation method based on big data from new energy vehicles further includes:

[0083] The vehicle's battery safety response level is classified based on the fault score value of each charging fault parameter and / or the fault score value of each driving fault parameter, as well as the safety score value of each fault group.

[0084] In this embodiment, dividing the single-vehicle data to obtain at least one charging condition segment data and / or at least one driving condition segment data includes:

[0085] Determine whether the bicycle data can be segmented using the charging gun connection status signal and the high-voltage power-on status signal. If so, segment the bicycle data using the charging gun connection status signal and the high-voltage power-on status signal.

[0086] In this embodiment, dividing the single-vehicle data to obtain at least one charging condition segment data and / or at least one driving condition segment data further includes:

[0087] Determine whether the vehicle data can be segmented using the charging gun connection status signal and the high-voltage power-on status signal. If not, segment the vehicle data using the charging gun connection status signal and the main positive relay status signal.

[0088] In this embodiment, each charging condition segment data includes at least one charging condition start frame and one charging condition end frame.

[0089] Each driving condition segment data includes at least one driving condition start frame and one driving condition end frame; wherein...

[0090] The start frame and end frame of each charging condition segment are obtained in the following manner:

[0091] The data of a single vehicle is traversed. If three consecutive frames of signals show that the charging gun is in the process of charging, the first frame of the three frames is selected as the starting frame of the charging condition segment data.

[0092] After obtaining the charging start frame, the data of each frame after the charging start frame is traversed. If the charging gun connection status of three consecutive frames is either charging complete or not charging, the first frame of the three frames is selected as the charging end frame of the charging condition segment data.

[0093] Each driving condition segment data is obtained by acquiring the driving condition start frame and driving condition end frame in the following manner:

[0094] The data of each vehicle is traversed. If three consecutive frames of signals show that the charging gun connection status is not charging and the high voltage power-on status signal is power-on, then the first frame of the three frames is selected as the starting frame of the driving condition segment data.

[0095] After obtaining the driving start frame, the data of each frame after the driving start frame is traversed. If the high voltage power-on status signal of three consecutive frames is not powered on, the first frame of the three frames is selected as the driving end frame of the driving condition segment data.

[0096] In this embodiment, each driving condition segment data includes at least one driving condition start frame and one driving condition end frame;

[0097] Each driving condition segment data includes at least one driving condition start frame and one driving condition end frame; wherein...

[0098] The start frame and end frame of each driving condition segment are obtained in the following manner:

[0099] The data of a single vehicle is traversed. If three consecutive frames of signals show that the driving gun connection status is "driving", then the first frame of the three frames is selected as the starting frame of the driving condition segment data.

[0100] After obtaining the start frame of driving, the data of each frame after the start frame of driving is traversed. If the driving gun connection status of three consecutive frames is driving completed or not driving, the first frame of the three frames is selected as the end frame of driving condition segment data.

[0101] Each driving condition segment data is obtained by acquiring the driving condition start frame and driving condition end frame in the following manner:

[0102] The data of each vehicle is traversed. If three consecutive frames of signals show that the driving gun connection status is not driving and the high voltage power-on status signal is powered on, then the first frame of the three frames is selected as the driving start frame of the driving condition segment data.

[0103] After obtaining the driving start frame, the data of each frame after the driving start frame is traversed. If the high voltage power-on status signal of three consecutive frames is not powered on, the first frame of the three frames is selected as the driving end frame of the driving condition segment data.

[0104] See Figure 3 In this embodiment, since different fault parameter calculations require data from different operating conditions, it is necessary to divide the data into segments for charging and driving conditions. The data sources for different vehicle models differ; to expand the model's applicability, a multi-signal judgment method is used to divide the data segments for the two operating conditions.

[0105] (1) Perform signal judgment: If the data source contains the charging gun connection status signal and the high voltage power-on status signal, then select these two signals as the segment division judgment condition; if the data source does not contain the high voltage power-on status signal, then select the main positive relay status signal as the substitute.

[0106] (2) Charging Start Frame: Data is traversed from top to bottom. If three consecutive frames of signals indicate that the charging gun is charging, the first frame of the three frames is selected as the charging start frame. This method of judging three consecutive frames greatly reduces the possibility of abnormal segmentation caused by individual erroneous data.

[0107] (3) Charging end frame: After finding the charging start frame, continue traversing downwards from this frame until three consecutive frames are found in which the charging gun connection status is either charging complete or not charging. Then, select the first frame of the three frames as the charging end frame. This method of judging by three consecutive frames greatly reduces the possibility of abnormal segmentation caused by individual erroneous data.

[0108] (4) Start of driving frame: Data is traversed from top to bottom. If three consecutive frames show that the charging gun connection status is not charging and the high voltage power-on status signal is powered on (or the main positive relay status signal is on), then the first frame of the three frames is selected as the start of driving frame. This method of judging three consecutive frames greatly reduces the possibility of abnormal segmentation caused by individual erroneous data.

[0109] (5) End of driving frame: After finding the start of driving frame, continue traversing downwards from this frame until three consecutive frames are found where the high-voltage power-on status signal is not powered on (or the main positive relay status signal is closed). Then, select the first frame of the three frames as the end of driving frame. This method of judging three consecutive frames greatly reduces the possibility of abnormal segmentation caused by individual erroneous data.

[0110] In this embodiment, to enable subsequent dimensionless processing of various faults, fault parameters need to be designed for each fault indicator to characterize the severity of the fault. The larger the value of the fault parameter, the higher the severity of the fault, ensuring the consistency of each fault parameter in the formula during subsequent dimensionless design. Since the types of faults differ, the number of fault parameters may vary in a single driving or charging segment. Therefore, for each fault, only the maximum value of the fault parameter is selected as the representative value in a single operating condition segment data (regardless of whether it is a driving or charging condition).

[0111] In this embodiment, the charging fault parameters include charging SOC consistency anomaly parameters and charging self-discharge rate parameters. The driving fault parameters include driving SOC consistency anomaly parameters, driving self-discharge rate parameters, and driving insulation anomaly parameters.

[0112] It is understood that the charging faults of this application may also include abnormal SOC consistency, abnormal SOC jump during charging, abnormal charging temperature, abnormal charging current, abnormal voltage consistency during charging, abnormal self-discharge rate during charging, abnormal power consistency during charging, abnormal charging gun connection, etc.

[0113] Driving fault parameters include, but are not limited to, the following: abnormal SOC consistency parameters during driving, abnormal self-discharge rate parameters during driving, abnormal insulation parameters, abnormal internal short circuit parameters, abnormal external short circuit parameters, abnormal water ingress parameters in the battery pack, abnormal SOC jump parameters during driving, abnormal power parameters, abnormal battery temperature parameters during driving, abnormal thermal runaway parameters, etc.

[0114] Understandably, other charging fault parameters can also be used, which will not be elaborated here.

[0115] In this embodiment, various charging fault parameters are obtained using the following methods:

[0116] (1) Obtaining SOC consistency anomaly parameters (both charging SOC consistency anomaly parameters and driving SOC consistency anomaly parameters can be obtained using this method).

[0117] Generally, vehicle data only includes the battery pack SOC, not the individual cell SOC. Therefore, when diagnosing SOC consistency anomalies, it is necessary to perform equivalent calculations on the individual cell SOC before judging its consistency.

[0118] a) Segment selection: The SOC variation range of charging condition segment data or driving condition segment data must include [SOC1, SOC2] (that is, the maximum SOC value in a single segment data is greater than SOC2, and the minimum SOC value is less than SOC1). Other cases are not calculated because the SOC variation range is small.

[0119] b) Calculation of the internal resistance of a single battery cell. Within a single segment, locate points of current change and capture the current data I of the frames preceding and following these points. A I B With individual unit voltage data U i-A U i-B Where i represents the i-th battery cell, and since the battery cells are connected in series, each cell has the same current; calculate the internal resistance of battery cell i at the m-th current change point.

[0120]

[0121] When the voltage of a single cell remains unchanged between two frames at the point of current change, i.e., the internal resistance is 0, this internal resistance value is discarded. The average of all internal resistance values ​​calculated for each cell is taken as the internal resistance of each cell in a single segment.

[0122] c) Calculation of individual cell OCV (open-circuit voltage). Based on the calculated internal resistance R of the individual cell... i Current I and unit voltage U i It can calculate the open-circuit voltage (OCV) of a single cell at any given time. i :

[0123] OCV i =U i +I×R i ;

[0124] d) SOC Consistency Parameter Calculation. Based on the correspondence table of SOC-OCV curves for individual battery cells (Table 1), the SOC value corresponding to the OCV of each battery cell in each frame is calculated using the difference method. Let the OCV value of cell i at time m be... Find the values ​​greater than and less than in the SOC-OCV curve correspondence table respectively. And closest The two values ​​OCVi-1 and OCVi-2 The corresponding SOCs are SOCi-1 and SOCi-2, respectively, obtained through interpolation. corresponding

[0125]

[0126] Therefore, the SOC value of each battery cell at any time in the segment is calculated. The maximum and minimum SOC values ​​of each cell at each time are found, and the difference is taken to obtain the SOC range of the cell at any time. The maximum value of the SOC range of the cells in the entire segment data is selected as the SOC consistency parameter of this segment data.

[0127] Table 1. Correspondence between SOC-OCV curves

[0128] OCV <![CDATA[OCV1]]> <![CDATA[OCV2]]> <![CDATA[OCV3]]> <![CDATA[OCV4]]> …… <![CDATA[OCV n ]]> SOC <![CDATA[SOC1]]> <![CDATA[SOC2]]> <![CDATA[SOC3]]> <![CDATA[SOC4]]> …… <![CDATA[SOC n ]]>

[0129] (2) Acquisition of self-discharge rate parameters (both charging self-discharge rate parameters and driving self-discharge rate parameters can be obtained using this method). By calculating the battery cell capacity of each data segment, the battery cell capacity at this time is compared with the battery cell capacity one month ago to obtain the monthly self-discharge rate of the battery cell, which is used as the self-discharge rate parameter.

[0130] a) The SOC variation range of charging condition segment data or driving condition segment data needs to include [SOC1, SOC2] (that is, the maximum SOC value in a single segment is greater than SOC2, and the minimum SOC value is less than SOC1). Other cases are not calculated because the SOC variation range is small.

[0131] b) Calculation of the internal resistance of a single battery cell. Within a single data segment, locate points of current change and capture the current data I of the frames preceding and following these points. A I B With individual unit voltage data U i-A U i-B Where i represents the i-th battery cell, and since the battery cells are connected in series, each cell has the same current; calculate the internal resistance of battery cell i at the m-th current change point.

[0132]

[0133] When the voltage of a single cell remains unchanged between two frames at the point of current change, i.e., the internal resistance is 0, this internal resistance value is discarded. The average of all calculated internal resistance values ​​for each battery cell is taken as the internal resistance R of each battery cell in a single segment. i .

[0134] c) Calculation of individual battery cell capacity. Based on the calculated internal resistance R of the individual battery cell... i Current I and unit voltage U i It can calculate the open-circuit voltage (OCV) of individual cells and the average cell at any given time. i :

[0135] OCV i =U i +I×R i ;

[0136] The charge E at any given time m can be calculated using the ampere-hour integration method. m :

[0137]

[0138] Where t0 is the start time of the segment, t m Let m be the calculation time point. Since the battery cells are connected in series, the charge received by each cell is the same at any given time, which is E. m .

[0139] Select two OCV reference values. H and OCV L (OCV H >OCV L Based on the correspondence between the SOC and OCV curves, the OCV can be obtained. H and OCV L Corresponding SOC H and SOC L Find the optimal OCV (Optical Capacity) for each individual battery cell. H The corresponding charge level at that time. If the data does not contain a value exactly equal to OCV... H For the data frame, the one with the closest OCV among the battery cells is selected. H The two values ​​of OCV i-1 OCV i-2 (OCV i-1 <OCV H <OCV i-2 The corresponding single-unit charging capacity E i-1 E i-2 OCV is calculated using interpolation. H Corresponding charging capacity E i-H :

[0140]

[0141] Similarly, we can obtain the OCV of each individual battery cell. L The corresponding charging capacity E i-L Combining SOC H and SOC L The capacity Q of battery cell i in this segment can be calculated. i :

[0142]

[0143] Based on the start time of this data segment, find the battery cell capacity calculation data Q from 30 days ago. i-bef Calculate the monthly self-discharge rate φ of battery cell i. i-self_discharge :

[0144]

[0145] The monthly self-discharge rate φ of the power battery i-self_discharge As a parameter of self-discharge rate.

[0146] (3) Acquisition of insulation abnormal parameters (insulation abnormal parameters during driving are acquired using this method). When acquiring the insulation value of the whole vehicle system, it is necessary to exclude the influence of the charging state. Therefore, the calculation of insulation abnormal fault parameters is only processed through the driving state.

[0147] a) Extract segment data of driving conditions by segmentation and extract insulation value signals.

[0148] b) Compare the insulation values ​​in the data segments with the insulation standard value R, record the insulation anomaly intervals where the insulation value is less than the insulation standard value, and record the duration of each insulation anomaly interval as t. i .

[0149] c) Calculate the average insulation value r for each insulation anomaly interval. i Calculate the ratio of the insulation standard value R to the average insulation value for each insulation abnormality interval.

[0150]

[0151] d) Calculate the proportion τ of each insulation fault interval time to the total insulation fault interval time. i :

[0152]

[0153] e) Set the duration parameter φ for insulation faults. R1 With fault kurtosis parameter φ R2 Let the duration of the entire segment be T, then the insulation anomaly duration parameter φ of this segment is... R1 for:

[0154]

[0155] f) Kurtosis parameter of insulation abnormality fault φ R2 for:

[0156]

[0157] In this embodiment, the fault score values ​​for the charging SOC consistency anomaly parameter, charging insulation anomaly parameter, driving SOC consistency anomaly parameter, and driving insulation anomaly parameter are obtained according to the following formulas:

[0158]

[0159] σ i1 , σ i2 , σ i3 There are three fault level thresholds, where σ i1 <σ i2 <σ i3 ;

[0160] i represents the i-th charging fault parameter or driving fault parameter.

[0161] Specifically, in acquiring fault parameters, it was ensured that the parameter values ​​increased with the severity of the fault. To convert each fault parameter into a dimensionless score ranging from [0, 100], three fault level thresholds σ were set for each parameter. i1 , σ i2 , σ i3 (σ i1 <σ i2 <σ i3 ), where i represents the i-th fault parameter. σ i1 The first-level fault threshold is defined as the fault parameter being less than σ. i1 At that time, it was considered that there was no fault; σ i2 The second-level fault threshold, σ i3 The threshold is for a level three fault.

[0162] When the rating is G i When the score is 100, it is considered fault-free and the safety is excellent; when the score is G... i When the value range is (80, 100), the safety is considered good; when the score is G... i When the value range is (60, 80], the security level is considered medium; when the score G... i When the value range is (0, 60], the security is considered poor.

[0163] In this embodiment, the fault score value of the charging self-discharge rate parameter is obtained in the following way:

[0164] The fault score value of the charging self-discharge rate parameter of each battery cell is obtained based on the self-discharge rate value of each battery cell.

[0165] Determine whether there is only one battery cell whose charging self-discharge rate parameter fault score value is less than the preset self-discharge rate score value. If so, obtain the charging self-discharge rate parameter fault score value of the battery cell whose self-discharge rate parameter is less than the preset self-discharge rate score value as the charging self-discharge rate parameter fault score value.

[0166] Determine whether only one battery cell has a fault score value for its self-discharge rate parameter that is lower than the preset self-discharge rate score value. If not, use the following formula to obtain the fault score value for the self-discharge rate parameter:

[0167] in,

[0168] G min The minimum score among all individual battery cells; G i自放电 The fault score value is the self-discharge rate parameter of the i-th battery cell.

[0169] Specifically, for battery cell self-discharge rate faults, since each battery cell has a self-discharge rate value, an additional scoring calculation method needs to be designed: Let the number of battery cells be n, and the self-discharge rate scores for all cells be G1, G2, ..., G... n If only one battery cell has a self-discharge rate score below 100, then that battery cell's score represents the self-discharge rate score of the entire battery pack; if multiple battery cells have self-discharge rate scores below 100, then the self-discharge rate score of the entire battery pack is G. cha for:

[0170]

[0171] Among them, G min This represents the minimum score among all individual battery cells.

[0172] In this embodiment, each of the charging fault parameters and / or driving fault parameters is calculated in pairs to obtain the weight information of each charging fault parameter and / or driving fault parameter, including:

[0173] Obtain the scale table;

[0174] Construct a judgment matrix for any two of the charging fault parameters and / or at least one driving fault parameters based on the proportional scaling table;

[0175] Obtain weight information based on the judgment matrix.

[0176] Specifically, because some faults are highly correlated and simultaneously affected by a certain basic signal, when the basic signal value shows a deteriorating trend, it can directly or indirectly lead to the simultaneous occurrence of multiple faults. Therefore, when performing weighted scoring, the analytic hierarchy process (AHP) needs to be improved to consider the impact of highly correlated fault scores on the overall safety assessment.

[0177] (1) Construct the judgment matrix. Table 2 is a proportional scale table. All fault indicators are compared pairwise. The relative importance values ​​are selected according to the proportional scale table to generate the judgment matrix.

[0178] Table 2 Proportional Scale Table

[0179]

[0180] in,

[0181] P represents the judgment matrix for each fault indicator; a ij Indicates fault a i For fault a j The relative importance of the quantization value, and a ij =1 / a ij .

[0182] For example, a 12 This represents the quantified value of the relative importance of the first fault to the second fault. Assuming that the table shows a1 is strongly important to a2, then a... 12 The answer is 7.

[0183] For example, suppose there are n parameters, and there is an importance score 'a' between every two parameters. ij This forms an n×n matrix with n eigenvalues, corresponding to the weights of the n parameters.

[0184] (2) Calculate the weights. Connect the rows of the above matrix and take the geometric mean, then normalize it to obtain the weights and eigenvectors of each average index.

[0185]

[0186]

[0187] In the formula: w is the eigenvector of matrix P, w i The weights of each fault indicator.

[0188] For example, W1 represents the eigenvector of the first row in matrix P, and specifically the eigenvector of the first fault. After obtaining the eigenvector of each fault, the above w... i The formula is used to obtain the weights of each fault parameter.

[0189] In this embodiment, after obtaining the weight information of each charging fault parameter and / or driving fault parameter, the power battery safety evaluation method based on new energy vehicle big data further includes:

[0190] Determine whether each of the acquired judgment matrices meets the preset conditions. If not, correct the judgment matrices that do not meet the conditions so that they meet the preset conditions.

[0191] Specifically, the following conditions must be met:

[0192] The following formula is used to determine whether the weight allocation obtained from the two matrices is reasonable. If matrix P satisfies the condition CR < 0.1, then P is considered to have passed the consistency test; otherwise, it fails and the values ​​in P need to be readjusted.

[0193]

[0194]

[0195] In the above formula, RI is the average random consistency index of the judgment matrix P, which is divided into 1 to 10 levels. Table 3 shows the standard values ​​of RI for the judgment matrix P.

[0196] Table 3 Standard values ​​of the average random consistency index RI

[0197]

[0198] In this embodiment, the correlation information is obtained using the following formula:

[0199] in,

[0200] m ij This represents the correlation between the standardized fault parameter i and the fault parameter j; M ij This represents the correlation between fault parameter i and fault parameter j before standardization; M min M represents the minimum value of the correlation parameter before standardization. max This represents the maximum value of the correlation parameter before standardization.

[0201] Specifically, among various types of faults, there are some highly correlated fault groups that are all affected by the same underlying signal. When the value of the underlying signal changes unfavorably, it can lead to a series of faults. If all faults are assigned weights in the calculation, the safety evaluation results will lack credibility. Therefore, it is necessary to classify and process faults.

[0202] First, based on the calculation principles of each fault, and combined with the basic signals, the faults are classified into: current abnormality faults, voltage abnormality faults, temperature abnormality faults, internal resistance abnormality faults, etc.

[0203] Correlation analysis was performed on faults belonging to the same category, with values ​​from 1 to 5 representing the strength of the correlation between two faults, where 1 indicates no correlation and 5 indicates complete correlation. The results were then normalized using a min-max standardization process to ensure that all results fall within the range [0,1], as shown in the following formula.

[0204]

[0205] Where: m ijM represents the correlation between fault i and fault j after standardization; ij M represents the correlation between fault i and fault j before standardization; min M represents the minimum value of the correlation parameter before standardization. max This represents the maximum value of the correlation parameter before standardization. The final correlation analysis results are shown in the table below. (m) i Indicates fault i.

[0206]

[0207] In this embodiment, the security score is obtained using the following formula:

[0208] in,

[0209] W t The following formula is used to obtain it:

[0210] in,

[0211] d i The following formula is used to obtain it:

[0212] d i =(100-G i )×w i ;in,

[0213] W t For the deduction score in the fault classification of t, G i For fault parameter i, the fault score value, w i For the weight information of fault parameter i, d max-t For each fault parameter in the same fault category, the one with the largest d i The numerical value.

[0214] In this embodiment, the following method is used to classify the vehicle's battery safety response level based on the obtained fault score values ​​of each charging fault parameter and / or the fault score values ​​of each driving fault parameter, as well as the safety score values ​​of each fault group:

[0215] Based on the fault scores and safety evaluation results, safety levels are classified into four levels according to the severity of the fault, from most severe to least severe: immediate response, response within 24 hours, response within 72 hours, and response within one week.

[0216] A response meets one of the following two criteria and is considered to be at the immediate response level:

[0217] (1) There are faults with scores lower than m1, and the sum of the weight values ​​of faults with scores lower than m1 is greater than p%.

[0218] (2) The safety evaluation result is lower than n1.

[0219] A response meets one of the following two criteria and is considered to be within the 24-hour response level:

[0220] (1) There are faults with scores lower than m1, and the sum of the weight values ​​of faults with scores lower than m1 is less than p%.

[0221] (2) The safety evaluation result is lower than n2 points.

[0222] A response meets one of the following two criteria and is considered to be within the 72-hour response level:

[0223] (1) The lowest fault score is less than m2 and greater than or equal to m1;

[0224] (2) The safety evaluation result is lower than n3 points.

[0225] A response level within one week is considered acceptable if either of the following two conditions is met:

[0226] (1) The lowest fault score is less than 100 points and greater than or equal to m2 points;

[0227] (2) The safety evaluation result is lower than n4 points.

[0228] In the above judgment conditions: p% is the judgment threshold of the sum of fault weights; m1 and m2 are the judgment thresholds for fault scores and 0 < m1 < m2 < 100; n1, n2, n3, and n4 are the judgment thresholds for safety evaluation results and 0 < n1 < n2 < n3 < n4 < 100.

[0229] By combining the single-fault score results with the safety evaluation results to determine the level, both the impact of the overall fault situation on safety and the sharing of fault dimensions with the overall fault severity are considered.

[0230] By combining the single-fault score with the safety evaluation results to determine the level, both the impact of the overall fault situation on safety and the impact of the fault dimension on the overall fault severity are considered.

[0231] This application also provides a power battery safety evaluation device based on big data from new energy vehicles. The device includes a single-vehicle data acquisition module, a segmentation module, a fault parameter acquisition module, and a scoring module. The single-vehicle data acquisition module acquires single-vehicle data; the segmentation module segments the single-vehicle data to acquire at least one charging condition segment data and / or at least one driving condition segment data; the fault parameter acquisition module acquires at least one charging fault parameter and / or at least one driving fault parameter based on the charging condition segment data; and the scoring module acquires a fault score value for each charging fault parameter and / or a fault score value for each driving fault parameter.

[0232] It is understandable that the above description of the method also applies to the description of the apparatus.

[0233] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the computer program, it implements the above-mentioned method for evaluating the safety of power batteries based on big data from new energy vehicles.

[0234] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can implement the above-mentioned method for evaluating the safety of power batteries based on big data from new energy vehicles.

[0235] Figure 2 This is an exemplary structural diagram of an electronic device capable of implementing the power battery safety evaluation method based on big data from new energy vehicles provided in one embodiment of this application.

[0236] like Figure 2As shown, the electronic device includes an input device 501, an input interface 502, a central processing unit 503, a memory 504, an output interface 505, and an output device 506. The input interface 502, central processing unit 503, memory 504, and output interface 505 are interconnected via a bus 507. The input device 501 and output device 506 are connected to the bus 507 via the input interface 502 and output interface 505, respectively, and thus connected to other components of the electronic device. Specifically, the input device 504 receives input information from the outside and transmits it to the central processing unit 503 via the input interface 502. The central processing unit 503 processes the input information based on computer-executable instructions stored in the memory 504 to generate output information, temporarily or permanently storing the output information in the memory 504, and then transmitting the output information to the output device 506 via the output interface 505. The output device 506 outputs the output information to the outside of the electronic device for user use.

[0237] In other words, Figure 2 The illustrated electronic device may also be implemented as including: a memory storing computer-executable instructions; and one or more processors, which can be coupled when executing the computer-executable instructions. Figure 1 This paper describes a method for evaluating the safety of power batteries based on big data from new energy vehicles.

[0238] In one embodiment, Figure 2 The electronic device shown can be implemented as including: a memory 504 configured to store executable program code; and one or more processors 503 configured to run the executable program code stored in the memory 504 to execute the power battery safety evaluation method based on big data of new energy vehicles in the above embodiments.

[0239] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0240] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0241] Computer-readable media include both permanent and non-permanent, removable and non-removable media, and information storage can be achieved by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, DVD or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0242] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0243] Furthermore, it is clear that the word "comprising" does not exclude other units or steps. Multiple units, modules, or devices recited in the apparatus claims may also be implemented by a single unit or overall apparatus via software or hardware.

[0244] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code, which includes one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutively marked blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or the overall flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0245] In this embodiment, the processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0246] Memory can be used to store computer programs and / or modules. The processor implements various functions of the device / terminal equipment by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory. Memory can mainly include a program storage area and a data storage area. The program storage area can store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area can store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). In addition, memory can include high-speed random access memory, and can also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0247] In this embodiment, if the modules / units integrated into the device / terminal equipment are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. Although this application discloses preferred embodiments as described above, it is not intended to limit this application. Any person skilled in the art can make possible changes and modifications without departing from the spirit and scope of this application. Therefore, the scope of protection of this application should be determined by the scope defined in the claims of this application.

[0248] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0249] Furthermore, it is clear that the word "comprising" does not exclude other units or steps. Multiple units, modules, or devices recited in the apparatus claims may also be implemented by a single unit or overall apparatus via software or hardware.

[0250] Although the present invention has been described in detail above with general descriptions and specific embodiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

Claims

1. A method for evaluating safety of power batteries based on big data of new energy vehicles, characterized in that, The power battery safety evaluation method based on big data from new energy vehicles includes: Obtain bicycle data; The single-vehicle data is divided to obtain at least one charging condition segment data and / or at least one driving condition segment data. Based on the charging condition segment data, obtain at least one charging fault parameter and / or at least one driving fault parameter; Obtain the fault score value for each charging fault parameter and / or obtain the fault score value for each driving fault parameter based on each driving fault parameter; The power battery safety evaluation method based on big data from new energy vehicles further includes: Obtain the weight information for each charging fault parameter and / or driving fault parameter; The charging fault parameters and / or driving fault parameters are classified to form at least one fault group, and each fault group includes at least one charging fault parameter and / or driving fault parameter. Correlation analysis is performed on various charging fault parameters and / or driving fault parameters located in the same group to obtain correlation information; The safety score of each fault group is obtained by acquiring the fault score of each charging fault parameter and / or the fault score of each driving fault parameter, the correlation information, and the weight information of each charging fault parameter and / or driving fault parameter. The power battery safety evaluation method based on big data from new energy vehicles further includes: The vehicle's battery safety response level is classified based on the fault score value of each charging fault parameter and / or the fault score value of each driving fault parameter, as well as the safety score value of each fault group. The step of dividing the single-vehicle data to obtain at least one charging condition segment data and / or at least one driving condition segment data includes: Determine whether the bicycle data can be segmented using the charging gun connection status signal and the high voltage power-on status signal. If so, segment the bicycle data using the charging gun connection status signal and the high voltage power-on status signal. The step of dividing the single-vehicle data to obtain at least one charging condition segment data and / or at least one driving condition segment data further includes: Determine whether the bicycle data can be divided using the charging gun connection status signal and the high voltage power-on status signal. If not, divide the bicycle data using the charging gun connection status signal and the main positive relay status signal. Each charging condition segment data includes at least one charging condition start frame and one charging condition end frame; Each driving condition segment data includes at least one driving condition start frame and one driving condition end frame; wherein... Each charging condition segment data is obtained by acquiring the charging condition start frame and the charging condition end frame in the following manner: The single-vehicle data is traversed. If three consecutive frames of signals show that the charging gun is in the charging state, the first frame of the three frames is selected as the charging start frame of the charging condition segment data. After obtaining the charging start frame, the data of each frame after the charging start frame is traversed. If the charging gun connection status of three consecutive frames is either charging complete or not charging, the first frame of the three frames is selected as the charging end frame of the charging condition segment data. Each driving condition segment data is obtained by acquiring the driving condition start frame and driving condition end frame in the following manner: The single vehicle data is traversed. If three consecutive frames of signals show that the charging gun connection status is not charging and the high voltage power-on status signal is power-on, then the first frame of the three frames is selected as the driving start frame of the driving condition segment data. After obtaining the driving start frame, the data of each frame after the driving start frame is traversed. If the high voltage power-on status signal of three consecutive frames is not powered on, the first frame of the three frames is selected as the driving end frame of the driving condition segment data. The charging fault parameters include charging SOC consistency abnormality parameters and charging self-discharge rate parameters. The driving fault parameters include driving SOC consistency abnormality parameters, driving self-discharge rate parameters, and driving insulation abnormality parameters; The fault score values ​​for charging SOC consistency anomaly parameters, charging insulation anomaly parameters, driving SOC consistency anomaly parameters, and driving insulation anomaly parameters are obtained according to the following formulas: ; wherein, , , There are three fault level thresholds, among which, ; i represents the i-th charging fault parameter or driving fault parameter; The fault score value of the charging self-discharge rate parameter is obtained in the following way: The fault score value of the charging self-discharge rate parameter of each battery cell is obtained based on the self-discharge rate value of each battery cell. Determine whether there is only one battery cell whose charging self-discharge rate parameter fault score value is less than the preset self-discharge rate score value. If so, obtain the charging self-discharge rate parameter fault score value of the battery cell whose self-discharge rate parameter is less than the preset self-discharge rate score value as the charging self-discharge rate parameter fault score value. Determine whether only one battery cell has a fault score value for its self-discharge rate parameter that is lower than the preset self-discharge rate score value. If not, use the following formula to obtain the fault score value for the self-discharge rate parameter: ;in, The minimum score among all individual battery cells; 自放电 The fault score value is the self-discharge rate parameter of the i-th battery cell.

2. The method for evaluating the safety of power batteries based on big data from new energy vehicles as described in claim 1, characterized in that, The weighting information for each charging fault parameter and / or driving fault parameter includes: Obtain the scale table; Construct a judgment matrix for any two of the charging fault parameters and / or at least one driving fault parameters based on the scale table. The weight information is obtained based on the judgment matrix.

3. The method for evaluating the safety of power batteries based on big data from new energy vehicles as described in claim 2, characterized in that, After obtaining the weight information of each charging fault parameter and / or driving fault parameter, the power battery safety evaluation method based on new energy vehicle big data further includes: Determine whether each of the acquired judgment matrices meets the preset conditions. If not, correct the judgment matrices that do not meet the conditions so that they meet the preset conditions.

4. The method for evaluating the safety of power batteries based on big data from new energy vehicles as described in claim 3, characterized in that, The following formula is used to obtain correlation information: ;in, This represents the correlation between the standardized fault parameter i and the fault parameter j. This indicates the correlation between fault parameter i and fault parameter j before standardization. This represents the minimum value of the correlation parameter before standardization. This represents the maximum value of the correlation parameter before standardization.

5. The method for evaluating the safety of power batteries based on big data from new energy vehicles as described in claim 4, characterized in that, The security score is obtained using the following formula: ;in, The following formula is used to obtain it: ;in, The following formula is used to obtain it: ;in, W t For the deduction score in the fault classification of t, G i For fault parameter i, the fault score value, Weight information for fault parameter i, For each fault parameter in the same fault category, the one with the largest value is... The numerical value; d i This represents the score to be deducted for the i-th fault parameter; d max-t This indicates that among the fault parameters under the same fault category, d i The maximum value.

6. A power battery safety evaluation device based on big data from new energy vehicles, used in the power battery safety evaluation method based on big data from new energy vehicles as described in any one of claims 1 to 5, characterized in that, The power battery safety evaluation device based on big data from new energy vehicles includes: A single-vehicle data acquisition module, wherein the single-vehicle data acquisition module is used to acquire single-vehicle data; A segmentation module is used to segment the single-vehicle data to obtain at least one charging condition segment data and / or at least one driving condition segment data. The fault parameter acquisition module is used to acquire at least one charging fault parameter and / or at least one driving fault parameter based on the charging condition segment data. The scoring module is used to obtain a fault score value for each charging fault parameter and / or a fault score value for each driving fault parameter.

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