New energy vehicle module power consumption abnormity identification method, system, device and medium

Abstract

The invention relates to a new energy vehicle module power consumption abnormity identification method, system and device and a medium, and belongs to the technical field of new energy vehicle safety, and the method comprises the steps: obtaining whole vehicle operation monitoring data of a battery pack; the voltage difference 90% quantile of each module at each moment in the non-driving state process of the vehicle is calculated every day; designing the single voltage data according to a BMS module, obtaining the voltage level of each module, analyzing the relation between a statistical magnitude and a threshold value through variance, judging whether the power consumption of the vehicle module is abnormal or not, comparing the differences among the modules through a minimum significant difference method, and positioning the module with significant abnormal power consumption. Existing data are fully utilized, and the voltage difference data are not limited to the charging data by dividing the vehicle state; and in combination with the voltage data and the BMS module distribution data, whether module power consumption abnormity exists or not is judged through variance analysis and a minimum significant difference method, and the module with abnormal power consumption is positioned, so that the accuracy and precision of judgment are improved.

Description

Technical Field

[0001] The present disclosure belongs to the field of new energy vehicle safety technology, and in particular relates to a method, system, device and medium for identifying abnormal power consumption of a new energy vehicle module. Background Art

[0002] At present, the new energy industry is developing by leaps and bounds and is in a period of rapid growth. The market size is huge and is still expanding. With the joint effects of technological innovation, policy promotion and market demand, it is expected to continue to maintain rapid growth in the next few years.

[0003] In new energy vehicles, the battery pack is composed of multiple single cells (commonly called battery cells) connected in series and / or parallel. Module voltage consistency is extremely important in new energy vehicles: Extending the service life of the entire battery pack: Ensuring that each module ages evenly during the charge and discharge process to prevent premature failure of certain modules due to overcharging or over-discharging; Avoiding overcharging / over-discharging: Inconsistent voltage may cause certain modules to overcharge or over-discharge, affecting the overall performance and safety of the battery; Reducing energy loss: Inconsistent voltage will cause additional loss during energy transfer between modules, reducing overall energy efficiency. Voltage consistency can reduce this loss and improve energy utilization efficiency; Preventing thermal runaway and avoiding short circuits: Inconsistent voltage may cause certain modules to overheat, increasing the risk of thermal runaway. Voltage consistency can reduce this risk and improve battery safety.

[0004] Related technologies typically analyze battery consistency by calculating the voltage difference between each charge cycle, or by directly using short-term charging voltage data. This can lead to significant errors and hinders monitoring and implementation. Furthermore, related technologies focus on analyzing voltage differences between individual cells, but lack the ability to identify and warn of inconsistent module power consumption.

[0005] Therefore, it is necessary to provide a new method, system, device and medium for identifying abnormal power consumption of new energy vehicle modules to solve the above technical problems. Summary of the Invention

[0006] The purpose of the present disclosure is to provide a method, system, device and medium for identifying abnormal power consumption of new energy vehicle modules in order to solve the above problems.

[0007] The present disclosure achieves the above objectives through the following technical solutions:

[0008] A method for identifying abnormal power consumption of a new energy vehicle module comprises the following steps:

[0009] Obtain real-time vehicle monitoring data of the vehicle battery pack within a preset period;

[0010] Preprocessing the real-time vehicle monitoring data;

[0011] The pre-processed real-time vehicle monitoring data is divided into working conditions according to preset conditions to obtain vehicle non-driving data;

[0012] extracting a pressure difference of a single frame of data from the non-driving data of the vehicle;

[0013] Counting the voltage differences of all the single-frame data to obtain single-cell voltage data corresponding to the 90% quantile of the voltage difference;

[0014] The single cell voltage data is designed according to the BMS module to obtain the voltage level of each module. A variance analysis is performed on the voltage level of each module to obtain the analysis statistic F value. Based on the relationship between the analysis statistic F value and the preset threshold, it is determined whether the vehicle module has power consumption abnormality, and by comparing the differences between multiple modules, the module with significantly abnormal power consumption is located.

[0015] As a further optimization solution of the present disclosure, real-time vehicle monitoring data of the vehicle battery pack within a preset period is obtained, including:

[0016] Acquire data that can monitor the vehicle battery pack pressure difference within a preset period, including vehicle identification code, acquisition time, displayed SOC, current, single cell voltage and mileage.

[0017] As a further optimization solution of the present disclosure, preprocessing the real-time vehicle monitoring data includes:

[0018] Cleaning the real-time vehicle monitoring data to remove abnormal data, including invalid data, data outside the normal voltage range, or duplicate data;

[0019] Arrange the cleaned data in ascending order according to vehicle identification code and time.

[0020] As a further optimization solution of the present disclosure, the pre-processed real-time vehicle monitoring data is divided into working conditions according to preset conditions to obtain vehicle non-driving data, including:

[0021] The pre-processed real-time vehicle monitoring data is grouped according to the vehicle identification code, and the time interval between adjacent data frames after the same vehicle is sorted is calculated;

[0022] According to the obtained time interval, combined with the charging state and current data, only the non-driving state data of the vehicle that meets the preset time interval threshold condition, charging state threshold condition and current data threshold condition is retained.

[0023] As a further optimization solution of the present disclosure, the voltage difference of all the single-frame data is counted to obtain the single-unit voltage data corresponding to the 90% quantile of the voltage difference, including:

[0024] The pressure difference is statistically processed to obtain statistical data, including the 90th percentile of the battery pressure difference, the single cell voltage corresponding to the 90th percentile of the battery pressure difference, the displayed SOC, the daily mileage, the abnormal module and the proportion of abnormal pressure difference data, and the data are stored.

[0025] A system for identifying abnormal power consumption of new energy vehicle modules, comprising:

[0026] A data acquisition module is used to obtain real-time vehicle monitoring data of the vehicle battery pack within a preset period;

[0027] A preprocessing module, used for preprocessing the real-time vehicle monitoring data;

[0028] The data division module is used to divide the pre-processed real-time vehicle monitoring data into working conditions according to preset conditions to obtain vehicle non-driving data;

[0029] a pressure difference calculation module, configured to extract the pressure difference of a single frame of data from the non-driving data of the vehicle;

[0030] A statistical module, configured to perform statistics on the voltage differences of all the single-frame data to obtain single-cell voltage data corresponding to a 90% quantile of the voltage difference;

[0031] The abnormality identification and positioning module is used to design the single-cell voltage data according to the BMS module to obtain the voltage level of each module, perform variance analysis on the voltage level of each module to obtain the analysis statistic F value, and judge whether the vehicle module has power consumption abnormality based on the relationship between the analysis statistic F value and a preset threshold value. By comparing the differences between multiple modules, the module with significantly abnormal power consumption is located.

[0032] As a further optimization solution of the present disclosure, the data segmentation module divides the pre-processed real-time vehicle monitoring data into working conditions according to preset conditions to obtain vehicle non-driving data, including:

[0033] The pre-processed real-time vehicle monitoring data is grouped according to the vehicle identification code, and the time interval between adjacent data frames after the same vehicle is sorted is calculated;

[0034] According to the obtained time interval, combined with the charging state and current data, only the non-driving state data of the vehicle that meets the preset time interval threshold condition, charging state threshold condition and current data threshold condition is retained.

[0035] As a further optimization solution of the present disclosure, the statistical module performs statistics on the voltage differences of all the single-frame data to obtain the single-cell voltage data corresponding to the 90% quantile of the voltage difference, including:

[0036] The pressure difference is statistically processed to obtain statistical data, including the 90th percentile of the battery pressure difference, the single cell voltage corresponding to the 90th percentile of the battery pressure difference, the displayed SOC, the daily mileage, the abnormal module and the proportion of abnormal pressure difference data, and the data are stored.

[0037] An electronic device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0038] Memory for storing computer programs;

[0039] The processor is used to execute the program stored in the memory to implement a method for identifying abnormal power consumption of new energy vehicle modules.

[0040] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements a method for identifying abnormal power consumption of a new energy vehicle module.

[0041] The beneficial effects of the present disclosure are:

[0042] The present disclosure can make full use of existing data and divide the vehicle status so that the pressure difference data is not limited to charging data; combining voltage data and BMS module distribution data, through variance analysis and the least significant difference method (LSD) to determine whether there is module power consumption abnormality and locate the power consumption abnormality module, thereby improving the accuracy and precision of the judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is a flow chart of a method in an embodiment of the present disclosure;

[0044] Figure 2 is a specific method flow chart in an embodiment of the present disclosure;

[0045] Figure 3 is a system structure block diagram in an embodiment of the present disclosure;

[0046] Figure 4 It is a block diagram of the device structure in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0047] The present application will be described in further detail below in conjunction with the accompanying drawings. It is necessary to point out that the following specific implementation methods are only used to further illustrate the present application and cannot be understood as limiting the scope of protection of the present application. Technicians in this field can make some non-essential improvements and adjustments to the present application based on the above application content.

[0048] like Figure 1 As shown, a method for identifying abnormal power consumption of a new energy vehicle module includes the following steps:

[0049] S1. Obtaining real-time vehicle monitoring data of the vehicle battery pack within a preset period;

[0050] S2. Preprocessing the real-time vehicle monitoring data;

[0051] S3. Divide the pre-processed real-time vehicle monitoring data into working conditions according to preset conditions to obtain vehicle non-driving data;

[0052] S4, extracting the pressure difference of a single frame of data from the non-driving data of the vehicle;

[0053] S5. Counting the voltage differences of all the single-frame data to obtain single-unit voltage data corresponding to the 90% quantile of the voltage difference;

[0054] S6. Calculate the single-cell voltage data according to the BMS module design to obtain the voltage level of each module, perform variance analysis on the voltage level of each module to obtain an analysis statistic F value, and determine whether the vehicle module has abnormal power consumption based on the relationship between the analysis statistic F value and a preset threshold value. Locate the module with significantly abnormal power consumption by comparing the differences between multiple modules.

[0055] like Figure 2 As shown, in this embodiment, the method for identifying abnormal power consumption of a new energy vehicle module is specifically as follows:

[0056] S1. Obtain real-time vehicle monitoring data of the vehicle battery pack within a preset period, including:

[0057] The real-time vehicle data of new energy vehicles collected from the data platform includes vehicle identification code, collection time, displayed SOC, current, single cell voltage and mileage, etc., which can monitor the vehicle battery pressure difference during the statistical period.

[0058] S2. Preprocessing the real-time vehicle monitoring data, specifically including:

[0059] In step S2, the data obtained in step S1 is cleaned to remove invalid, duplicate and other abnormal data;

[0060] Furthermore, the cleaned data is sorted in ascending order according to vehicle identification code and time.

[0061] S3. Divide the pre-processed real-time vehicle monitoring data into working conditions according to preset conditions to obtain vehicle non-driving data, specifically including:

[0062] In step S3, the data processed in step S2 is grouped according to the vehicle identification code, and the time interval between adjacent data frames after the same vehicle is sorted is calculated;

[0063] Furthermore, based on the obtained time interval data, combined with the charging state and current data, only the non-driving state data of the vehicle that meets the time interval threshold condition, the charging state threshold condition and the current data threshold condition is retained.

[0064] S4. Extracting the pressure difference of a single frame of data from the non-driving vehicle data, specifically comprising:

[0065] In step S4, the voltage difference of the new energy vehicle battery of each data frame is calculated using the following formula:

[0066]

[0067] Where, ΔV i is the value of the battery voltage difference of the new energy vehicle at time i, and are the maximum and minimum values of all battery cells in the vehicle at time i.

[0068] S5. Counting the voltage differences of all the single-frame data to obtain single-unit voltage data corresponding to the 90% quantile of the voltage difference, specifically including:

[0069] In step S5, the obtained data is statistically processed to obtain statistical data that can represent the voltage consistency of new energy vehicles to a certain extent, including: the 90th percentile (Δ) of the battery voltage difference, the cell voltage corresponding to the 90th percentile of the battery voltage difference, the displayed SOC, the daily mileage, the abnormal modules, and the proportion of abnormal voltage difference data;

[0070] Furthermore, the 90% quantile of a relevant variable is defined as the value such that 90% of the data items in the dataset are less than or equal to this value, while the remaining 10% of the data items are greater than this value. In other words, if the relevant variables are sorted from smallest to largest, the 90% quantile is the value at the 90th percentile in this sorted sequence.

[0071] Furthermore, the 90% percentile of the pressure difference within a preset period of each vehicle, as well as the single cell voltage, displayed SOC, daily mileage, abnormal modules and the proportion of abnormal pressure difference data are stored.

[0072] S6. Calculate the voltage level of each module based on the single-cell voltage data according to the BMS module design, perform variance analysis on the voltage level of each module to obtain an analysis statistic F value, determine whether the vehicle module has abnormal power consumption based on the relationship between the analysis statistic F value and a preset threshold, and locate the module with significantly abnormal power consumption by comparing the differences between the modules multiple times, specifically including:

[0073] In step S6, based on the data obtained in step S6, the voltage data of different vehicles are combined with the grouping method of the BMS to obtain the voltage distribution information of each module;

[0074] Furthermore, the average voltage of the i-th module is expressed as follows:

[0075]

[0076] Where, VM i is the average voltage value of the i-th module; V im is the single cell voltage value of the mth cell in the i-th module; in the i-th module, there are a total of M single cells i indivual;

[0077] Furthermore, a variance analysis is performed on the average voltage of K modules for each vehicle to obtain the test statistic F value of the variance analysis. If the F value of a vehicle meets the threshold requirement, it is considered that there is an abnormal module power consumption phenomenon;

[0078] Furthermore, the calculation process of the F value is as follows:

[0079] The expression of the average voltage of the i-th module is as follows:

[0080]

[0081] VM i is the average voltage value of the i-th module; V im is the single cell voltage value of the mth cell in the i-th module; in the i-th module, there are a total of M single cells i indivual;

[0082] The expression for the average vehicle voltage is as follows:

[0083]

[0084] Where K is the total number of modules in the battery pack, and N is the total number of cells in the battery pack;

[0085] The expression for the total sum of squares is as follows:

[0086]

[0087] The expression for the between-group sum of squares is as follows:

[0088]

[0089] The expression for the within-group sum of squares is as follows:

[0090]

[0091] The expression for the total variance is as follows:

[0092] MST = SST / (N-1);

[0093] The expression for the between-group variance is as follows:

[0094] MSA = SSA / (K-1);

[0095] The expression for the within-group variance is as follows:

[0096] MSE = SSE / (NK);

[0097] Assume the test statistic is F = MSA / MSE, F ~ F(N-1,NK). That is, the F value follows an F distribution with K-1 degrees of freedom, NK;

[0098] The threshold is F α (N-1.,Nk), can be obtained by querying the F distribution table. If F>F α (N-1.,Nk), it is considered that there is a module power consumption anomaly and the next step is carried out; otherwise, the process is terminated;

[0099] Furthermore, after determining that the power consumption is abnormal, the LSD judgment matrix of each module is obtained through the least significant difference method (LSD): if the values in the column of the judgment matrix of the mth module are all 0, then the power consumption of the mth module is determined to be abnormal. The calculation formula for the value of the i-th row and j-th column of the LSD judgment matrix is:

[0100]

[0101] like Figure 3 As shown, an embodiment of the present disclosure provides a system for identifying abnormal power consumption of a new energy vehicle module, including:

[0102] A data acquisition module is used to obtain real-time vehicle monitoring data of the vehicle battery pack within a preset period;

[0103] A preprocessing module, used for preprocessing the real-time vehicle monitoring data;

[0104] The data division module is used to divide the pre-processed real-time vehicle monitoring data into working conditions according to preset conditions to obtain vehicle non-driving data;

[0105] a pressure difference calculation module, configured to extract the pressure difference of a single frame of data from the non-driving data of the vehicle;

[0106] A statistical module, configured to perform statistics on the voltage differences of all the single-frame data to obtain single-cell voltage data corresponding to a 90% quantile of the voltage difference;

[0107] The abnormality identification and positioning module is used to design the single-cell voltage data according to the BMS module to obtain the voltage level of each module, perform variance analysis on the voltage level of each module to obtain the analysis statistic F value, and judge whether the vehicle module has power consumption abnormality based on the relationship between the analysis statistic F value and a preset threshold value. By comparing the differences between multiple modules, the module with significantly abnormal power consumption is located.

[0108] The implementation process of the functions and effects of each module in the above system is specifically described in the implementation process of the corresponding steps in the above method, which will not be repeated here.

[0109] For the system embodiment, since it basically corresponds to the method embodiment, the relevant parts can be referred to the partial description of the method embodiment. The system embodiment described above is only schematic, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the disclosed solution. Those of ordinary skill in the art can understand and implement it without paying any creative work.

[0110] In the above embodiment, any number of all modules can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least some of the functions of one or more of these modules can be combined with at least some of the functions of other modules and implemented in one module. At least one of all modules can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation modes of software, hardware and firmware or in a suitable combination of any of them. Alternatively, at least one of all modules can be at least partially implemented as a computer program module, which can perform the corresponding function when the computer program module is run.

[0111] See also Figure 4 The electronic device provided by an embodiment of the present disclosure includes a processor 1110, a communication interface 1120, a memory 1130 and a communication bus 1140, wherein the processor 1110, the communication interface 1120, and the memory 1130 communicate with each other through the communication bus 1140;

[0112] Memory 1130, for storing computer programs;

[0113] The processor 1110 is configured to implement the following method for identifying abnormal power consumption of a new energy vehicle module when executing the program stored in the memory 1130 .

[0114] The communication bus 1140 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The communication bus 1140 can be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, the figure shows only one thick line, but this does not mean that there is only one bus or only one type of bus.

[0115] The communication interface 1120 is used for communication between the electronic device and other devices.

[0116] The memory 1130 may include a random access memory (RAM) or a non-volatile memory, such as at least one disk storage. Alternatively, the memory 1130 may be at least one storage device located away from the processor 1110.

[0117] The above-mentioned processor 1110 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0118] The embodiments of the present disclosure further provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for identifying abnormal power consumption of a new energy vehicle module.

[0119] The computer-readable storage medium may be included in the device / apparatus described in the above embodiments, or may exist independently without being incorporated into the device / apparatus. The computer-readable storage medium carries one or more programs that, when executed, implement the method for identifying abnormal power consumption in a new energy vehicle module according to the embodiments of the present disclosure.

[0120] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, such as, but not limited to, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0121] The above embodiments merely illustrate several implementation methods of the present disclosure, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present disclosure. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the scope of the present disclosure, all of which fall within the scope of protection of the present disclosure.

Claims

1. A method for identifying abnormal power consumption of a new energy vehicle module, characterized in that: The following steps are involved: Obtain real-time vehicle monitoring data of the vehicle battery pack within a preset period; Preprocessing the real-time vehicle monitoring data; The pre-processed real-time vehicle monitoring data is divided into working conditions according to preset conditions to obtain vehicle non-driving data; extracting a pressure difference of a single frame of data from the non-driving data of the vehicle; Counting the voltage differences of all the single-frame data to obtain single-cell voltage data corresponding to the 90% quantile of the voltage difference; The single cell voltage data is designed according to the BMS module to obtain the voltage level of each module. A variance analysis is performed on the voltage level of each module to obtain the analysis statistic F value. Based on the relationship between the analysis statistic F value and the preset threshold, it is determined whether the vehicle module has power consumption abnormality, and by comparing the differences between multiple modules, the module with significantly abnormal power consumption is located.

2. The method for identifying abnormal power consumption of a new energy vehicle module according to claim 1, characterized in that: Obtain real-time vehicle monitoring data of the vehicle battery pack within a preset period, including: Acquire data that can monitor the vehicle battery pack pressure difference within a preset period, including vehicle identification code, acquisition time, displayed SOC, current, single cell voltage and mileage.

3. The method for identifying abnormal power consumption of a new energy vehicle module according to claim 1, characterized in that: Preprocessing the real-time vehicle monitoring data includes: Cleaning the real-time vehicle monitoring data to remove abnormal data, including invalid data, data outside the normal voltage range, or duplicate data; Arrange the cleaned data in ascending order according to vehicle identification code and time.

4. The method for identifying abnormal power consumption of a new energy vehicle module according to claim 1, characterized in that: The pre-processed real-time vehicle monitoring data is divided into working conditions according to preset conditions to obtain vehicle non-driving data, including: The pre-processed real-time vehicle monitoring data is grouped according to the vehicle identification code, and the time interval between adjacent data frames after the same vehicle is sorted is calculated; According to the obtained time interval, combined with the charging state and current data, only the non-driving state data of the vehicle that meets the preset time interval threshold condition, charging state threshold condition and current data threshold condition is retained.

5. The method for identifying abnormal power consumption of a new energy vehicle module according to claim 1, characterized in that: The voltage difference of all the single-frame data is counted to obtain the single-unit voltage data corresponding to the 90% quantile of the voltage difference, including: The pressure difference is statistically processed to obtain statistical data, including the 90th percentile of the battery pressure difference, the single cell voltage corresponding to the 90th percentile of the battery pressure difference, the displayed SOC, the daily mileage, the abnormal module and the proportion of abnormal pressure difference data, and the data are stored.

6. A system for identifying abnormal power consumption of new energy vehicle modules, characterized in that: include: A data acquisition module is used to obtain real-time vehicle monitoring data of the vehicle battery pack within a preset period; A preprocessing module, used for preprocessing the real-time vehicle monitoring data; The data division module is used to divide the pre-processed real-time vehicle monitoring data into working conditions according to preset conditions to obtain vehicle non-driving data; a pressure difference calculation module, configured to extract the pressure difference of a single frame of data from the non-driving data of the vehicle; A statistical module, configured to perform statistics on the voltage differences of all the single-frame data to obtain single-cell voltage data corresponding to a 90% quantile of the voltage difference; The abnormality identification and positioning module is used to design the single-cell voltage data according to the BMS module to obtain the voltage level of each module, perform variance analysis on the voltage level of each module to obtain the analysis statistic F value, and judge whether the vehicle module has power consumption abnormality based on the relationship between the analysis statistic F value and a preset threshold value. By comparing the differences between multiple modules, the module with significantly abnormal power consumption is located.

7. The system for identifying abnormal power consumption of new energy vehicle modules according to claim 6, characterized in that: The data division module divides the pre-processed real-time vehicle monitoring data into working conditions according to preset conditions to obtain vehicle non-driving data, including: The pre-processed real-time vehicle monitoring data is grouped according to the vehicle identification code, and the time interval between adjacent data frames after the same vehicle is sorted is calculated; According to the obtained time interval, combined with the charging state and current data, only the non-driving state data of the vehicle that meets the preset time interval threshold condition, charging state threshold condition and current data threshold condition is retained.

8. The system for identifying abnormal power consumption of new energy vehicle modules according to claim 6, characterized in that: The statistical module performs statistics on the voltage differences of all the single-frame data to obtain single-cell voltage data corresponding to the 90% quantile of the voltage difference, including: The pressure difference is statistically processed to obtain statistical data, including the 90th percentile of the battery pressure difference, the single cell voltage corresponding to the 90th percentile of the battery pressure difference, the displayed SOC, the daily mileage, the abnormal module and the proportion of abnormal pressure difference data, and the data are stored.

9. An electronic device, characterized in that: The processor, the communication interface, the memory and the communication bus are connected to each other via the communication bus. Memory for storing computer programs; A processor is used to execute a program stored in a memory to implement the method for identifying abnormal power consumption of a new energy vehicle module according to any one of claims 1 to 6.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for identifying abnormal power consumption of a new energy vehicle module according to any one of claims 1 to 6 is implemented.

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