Initial Bias Correction Method, System, Device and Storage Medium for Cell Voltage

Abstract

The present invention relates to the technical field of new energy vehicle safety, specifically to a method, system, device and storage medium for correcting the initial bias of cell voltage. The method includes selecting a specific continuous number of cell voltage data as an initial data set; calculating the square of each cell data, finding the sum of squares, and calculating the mean value; forming a m*1 dimensional vector from the sum of squares of all cells, calculating the mean value of the vector to obtain a normalization standard b, calculating a bias correction coefficient, and applying the bias correction coefficient to the voltage of each cell to complete the correction. This technical solution can correct the voltage of each cell to improve the accuracy of the cell voltage monitoring result.

Description

Technical Field

[0001] The present invention relates to the field of new energy vehicle safety technology, and specifically to a method, system, device and storage medium for correcting the initial bias of a battery cell voltage. Background Art

[0002] Cell voltage monitoring is a crucial component of current battery management systems, directly impacting battery safety, stability, and service life. However, traditional cell voltage monitoring technologies often focus solely on collecting single data points, lacking comprehensive analysis and processing of continuous operational data. This results in the inability to promptly detect and effectively address missing data, abnormal fluctuations, or data exceeding physical boundaries during battery use, potentially impacting the accuracy and reliability of the battery management system.

[0003] Furthermore, cell voltage data may suffer from initial bias. Because various factors may affect the production, transportation, and installation of cells, cell voltage may initially deviate. If this initial bias is not corrected, it will directly affect the accuracy of voltage monitoring results, negatively impacting the performance of the battery management system (BMS) and the battery's lifespan. Summary of the Invention

[0004] The purpose of the present invention is to propose a method, system, device and storage medium for correcting the initial bias of battery cell voltage. This technical solution can correct the voltage of each battery cell to improve the accuracy of the battery cell voltage monitoring results.

[0005] To achieve the above-mentioned purpose, the first aspect of the present disclosure provides a method for correcting the initial bias of a cell voltage, comprising selecting a specific number of consecutive cell voltage data as an initial data set; calculating the square of each cell voltage data; , find the sum of squares, the formula is as follows:

[0006]

[0007] in, is the sum of the squares of i cells, The number of running data of the selected battery cell.

[0008] Calculate the mean , the formula is as follows:

[0009]

[0010] The sum of the squares of all cells forms a m*1 dimensional vector , calculate the vector The mean of , get the normalized standard b, the formula is as follows:

[0011]

[0012] Among them, is the number of battery cells.

[0013] Calculate the bias correction coefficient, and the formula is as follows:

[0014]

[0015] Among them, represents the bias correction coefficient;

[0016] Apply the bias correction coefficient to the voltage of each battery cell for correction, and the formula is as follows:

[0017]

[0018] Among them, represents the corrected voltage of the battery cell.

[0019] Beneficial effects of the basic solution: Different from traditional solutions that use methods such as simple averaging or filtering, this application adopts an innovative technical solution. Selecting the voltage data of a specific continuous number of battery cells as the initial data set enables subsequent calculations and analyses to be based on a stable data set that reflects the actual voltage state of the battery cells, ensuring that the data basis in the bias correction process is representative and diverse, which helps to improve the reliability and effectiveness of subsequent calculations. By calculating the sum of squares, the degree of change in the overall voltage data of the battery cells can be quantified, and the mean value calculated by the sum of squares reflects the overall volatility and change range of the battery cell voltage data. The formed vector A can intuitively display the average state of the battery cell group, which helps to identify and correct the deviation of individual battery cells from the overall average level, thereby improving the consistency and comparability of the data. Through normalization processing, the measurement differences caused by different voltage levels between different battery cells can be eliminated, effectively improving the reliability and comparability of the data, making the calculation of the battery cell bias correction coefficient more accurate and effective. The calculation of the bias correction coefficient is based on the previous data processing and analysis, so it can accurately reflect the initial bias of each battery cell voltage. By applying the bias correction coefficient, each battery cell voltage can be accurately corrected, effectively eliminating or reducing the systematic deviation in the battery cell voltage measurement, not only improving the accuracy of the battery cell voltage monitoring, but also enhancing the stability of the battery system.

[0020] As an implementable preferred solution, it includes obtaining continuous battery cell voltage operation data and performing data preprocessing, including the following contents:

[0021] Perform data cleaning and conversion, process null values and parameters beyond the physical boundaries, convert the time stamp to the standard time format, and unify the parameter units;

[0022] Perform data aggregation, align parameter fields and sampling frequencies, unify static information, and associate static information with dynamic sampling data;

[0023] Perform data standardization, standardize field classifications and field names, and associate them with local files through static information tables for storage.

[0024] As an implementable preferred solution, aligning the sampling frequency includes the following:

[0025] Determine the sampling frequency of the current vehicle data, calculate the sampling time difference, take the median of the difference sequence as the target sampling period, and obtain the sampling frequency; determine the start and end times of the sampling frequency abnormal period; and perform linear interpolation on the sampling frequency abnormal period.

[0026] As an implementable optimal solution, key data features are extracted from the corrected cell voltage operating data, and the risk characteristics of the key data features are quantitatively described based on stability, and the system stability is reflected through mathematical expectation and variance; the risk characteristics of the key data features are quantitatively described based on correlation, and the correlation coefficient is calculated. η Quantify the safety status, using 1- η Obtain a quantitative value of the risk status; quantitatively describe the risk characteristics of key data features based on consistency, and describe the consistency between battery cells through entropy values.

[0027] As an implementable preferred solution, it also includes quantifying risk characteristics, taking a specific time as a characteristic observation time window, and calculating the safety quantification value within the characteristic observation time window at a certain moment. , the observation risk probability is defined as:

[0028] .

[0029] As an implementable preferred solution, it also includes risk feature identification and risk probability integral calculation. And draw the risk accumulation curve, the formula is as follows:

[0030]

[0031] After obtaining the risk accumulation curve, the safety status of the vehicle is judged by the characteristics of the curve;

[0032] The cumulative risk change rate is and , reflecting the safety status of different working conditions during battery operation, and defining relative risk as:

[0033]

[0034] The vehicle operation safety consistency coefficient describes the relative risk of the vehicle The smaller the value, the safer the vehicle;

[0035] Perform risk enhancement perception on the risk score vector. The risk enhancement perception calculation formula is as follows:

[0036]

[0037] Where, Represents the cumulative risk up to the present moment, represents the cumulative risk up to the last observation moment, Represents the maximum risk in the current observation period, which is determined by the current absolute risk Q value. It is a measure of the speed of risk change. If there is no obvious risk change in the current observation period, .

[0038] As an implementable preferred solution, it also includes quantifying risk characteristics on a time scale, locating high-risk periods, outputting quantitative analysis results of high-risk vehicle safety status characteristics and local information of high-risk segments, based on which risk tracing and fault diagnosis can be performed.

[0039] In a second aspect, an embodiment of the present disclosure provides a method for correcting an initial bias of a battery cell voltage, characterized in that the method utilizes the above-mentioned system for correcting an initial bias of a battery cell voltage.

[0040] In a third aspect, an embodiment of the present disclosure provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor implements the above-mentioned method for correcting the initial bias of the battery cell voltage when executing the program.

[0041] In a fourth aspect, an embodiment of the present disclosure provides a storage medium storing a computer program. When the computer program is executed by a processor, the above-mentioned method for correcting the initial bias of a battery cell voltage can be implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 Schematic diagram of the logic of the method for correcting the initial bias of the battery cell voltage of the present invention.

[0043] Figure 2 This is a comparison diagram before and after correction of the initial offset of the battery cell voltage.

[0044] Figure 3 This is a local feature map of the high-risk segment of abnormal self-discharge at an earlier time.

[0045] Figure 4 This is a local feature map of a high-risk segment of abnormal self-discharge at a later time.

[0046] Figure 5 It is the local feature map of the segment with abnormally high risk of capacity decay.

[0047] Figure 6 Local feature maps of high-risk segments for connection anomaly judgment.

[0048] Figure 7 Local feature maps of high-risk segments for sampling anomaly judgment.

[0049] Figure 8 Schematic diagram of the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the technical solution and advantages of the present application clearer, the technical solution of the present invention will be further described in detail below with reference to the accompanying drawings. It will be understood that the specific embodiments described herein are only partial embodiments of the present invention, which are only used to explain the present application, rather than to limit the present application. It should be noted that the technical features or combinations of technical features described in the following embodiments should not be considered to be isolated, and they can be combined with each other to achieve better technical effects. The same reference numerals appearing in the drawings of the following embodiments represent the same features or components, which can be applied to different embodiments.

[0051] In addition, unless otherwise defined, technical or scientific terms used in the description of the present invention should have the common meanings understood by those skilled in the art in the art to which the present invention belongs.

[0052] The present invention will be further described in detail below with reference to the accompanying drawings:

[0053] Description of reference numerals: electronic device 500 , processor 501 , communication interface 502 , memory 503 , bus 504 .

[0054] Step S100, obtaining continuous cell voltage operation data from a cell voltage monitoring device or system and performing data preprocessing, including:

[0055] Step S101 , perform data cleaning and conversion, process null values and parameters beyond physical boundaries, convert timestamps into a standard time format, and unify parameter units.

[0056] Step S102, performing data aggregation, includes:

[0057] Step S102-1: Align the parameter field and the sampling frequency. The specific operations of sampling frequency alignment include:

[0058] Determine the sampling frequency of the current vehicle data, calculate the sampling time difference, take the median of the difference sequence as the target sampling period, and thus obtain the sampling frequency.

[0059] Determine the start and end times of the abnormal sampling frequency period.

[0060] Perform linear interpolation on the abnormal sampling frequency period (such as jump, uneven interval, etc.), and use the method of linear interpolation to supplement or correct the data.

[0061] Step S102-2, unify the static information (such as manufacturer, battery type, etc.), and associate the static information with the dynamic sampling data (including vehicle operation data and experimental test data).

[0062] Step S103, perform data standardization processing, standardize the field classification and field names, and store them in association with the static information table and the local file.

[0063] Step S200, process the abnormal data, and perform the initial bias correction of the cell voltage, referring to Figure 1 , including:

[0064] Step S201, select a specific continuous number of cell voltage data as the initial data set to ensure the continuity and integrity of the data. In this embodiment, the first 5000 cell voltage data are selected as the initial data set.

[0065] Step S202, calculate the mean value of the sum of squares of each cell data A , including:

[0066] Step S202-1, represent the first 5000 cell voltage operation data as a vector .

[0067] Step S202-2, calculate the square of each cell data .

[0068] Step S202-3, calculate the sum of squares, and the formula is as follows:

[0069]

[0070] Among them, is the sum of squares of the i-th cell, is the number of selected cell operation data.

[0071] Calculate the mean value, and the formula is as follows:

[0072]

[0073] Among them, A represents the mean value.

[0074] Step S203, form an m*1-dimensional vector Calculate the mean value to obtain the normalization standard b, specifically including:

[0075] Step S203-1, the sum of the squares of all cells forms a m*1 dimensional vector , .

[0076] Step S203-2, calculate vector The mean of , get the normalized standard b, the formula is as follows:

[0077]

[0078] in, is the number of battery cells.

[0079] Step S204: Calculate the bias correction coefficient. The formula is as follows:

[0080]

[0081] in, Represents the bias correction coefficient.

[0082] In step S205, the bias correction coefficient is applied to the voltage of each cell for correction. The formula is as follows:

[0083]

[0084] Reference Figure 2 (a) is the cell voltage before correction, and (b) is the cell voltage after correction.

[0085] Step S300, extracting key data features, includes:

[0086] Step S301 : Observe the changing trends of cell voltage, probe temperature, current and other signals along with SOC (State of Charge) on a time scale to explore inherent risk characteristics and their evolution trends.

[0087] Step S302 , extracting key data features, including median pressure difference (Vi-Vm), equivalent internal resistance (Vi / I), pressure difference velocity (ΔVi), etc.

[0088] Step S400: Quantitative description of risk characteristics, specifically including:

[0089] Step S401: Based on the stability, the risk characteristics of the key data characteristics are quantitatively described. When the safety factors conform to the normal distribution, the mathematical expectation of the key data characteristic parameters represents the steady state of the system, and the variance reflects the stability characteristics of the system. is a key data characteristic parameter that affects safety, such as voltage change rate, voltage range, temperature range, etc., and it is assumed to obey the Gaussian normal distribution function within a certain observation time window:

[0090]

[0091] Where x is the characteristic parameter data, and u and σ are the mean and standard deviation of the characteristic parameter within the selected observation time window.

[0092] Then the probability that the key data characteristic parameter appears within a certain fixed observation time window T (measuring the change scale of the signal) Can be expressed as:

[0093]

[0094] Let = , then there is

[0095]

[0096] This probability depends on the local variance of the signal change σ 2 And the fixed observation time window T .

[0097] Step S402, quantitatively describe the risk characteristics of the key data characteristics based on the correlation. During the vehicle operation, there is an inevitable correlation between different parameters, and we define this normal correlation as safety and quantify this safety state by calculating the correlation coefficient η . Correspondingly, using 1 - η Can obtain the quantified value of the risk state. The calculation methods of the correlation coefficient include:

[0098] Current And temperature Local co - correlation coefficient :

[0099]

[0100] Where m is the data length.

[0101] Local co - correlation coefficient of the total voltage V and SOC:

[0102]

[0103] Overall correlation coefficient between single - cell voltages:

[0104] Assume that there are n cell voltage data at a certain time period, including data at m moments, which can be expressed as the following matrix:

[0105]

[0106] Overall correlation coefficient of the n - column vectors Defined as:

[0107] .

[0108] Step S403, based on consistency, quantitatively describe the risk characteristics of key data features. An automotive battery system consists of several battery cells. The better the consistency, the higher the battery safety. The higher the consistency, the smaller the entropy value, and vice versa, the smaller the consistency, the higher the entropy value. Therefore, the issue of cell consistency can be transformed into how to describe n in the n dimensional space x R n and x are all non - negative numbers. The higher the system consistency characterized by a certain safety factor, the safer the system. The three entropy value definitions for describing the battery system include:

[0109] The extreme value entropy is defined as:

[0110]

[0111] The compromise entropy is defined as:

[0112]

[0113] The variance entropy is defined as:

[0114]

[0115] In the three expressions, 0 ≤ η ≤ 1, η The closer x is to 1, the better the consistency among the variables of the vector, and vice versa, the worse the consistency, and the greater the risk for the battery system. Therefore, 1 - η can be defined as risk.

[0116] Use extreme value entropy, compromise entropy, and variance entropy to describe the change regularity and risk of the range voltage, range temperature, etc. within a characteristic observation time window. On the one hand, the risk described by these entropy values has strong anti - interference ability for the system, good robustness and simple calculation, and can achieve alarm in combination with the dynamic threshold model. On the other hand, based on these parameter definitions, more complex comprehensive models can be evolved.

[0117] Step S500, risk characteristic quantitative identification, includes:

[0118] Step S501, perform risk characteristic quantification. Taking a specific time, such as 5 minutes, as the characteristic observation time window, calculate the safety quantification value corresponding to a certain moment within the characteristic observation time window , the observation risk probability is defined as:

[0119] .

[0120] Step S502: Calculate the risk probability score And draw the risk accumulation curve, the formula is as follows:

[0121]

[0122] After obtaining the risk accumulation curve, the safety status of the vehicle is judged by the characteristics of the curve.

[0123] The cumulative risk change rate is and , It is the cumulative value of the 80th to 99th percentiles after ranking the cumulative risk change rate. The cumulative value of the 1st to 60th percentile is the relative risk Reflects the safety status of different working conditions during battery operation, defined as:

[0124]

[0125] The vehicle operation safety consistency coefficient describes the relative risk of the vehicle. Relatively small, and The smaller the value, the safer the vehicle.

[0126] At the same time, the absolute risk Q and risk frequency R of the vehicle are defined, where Q represents the absolute amplitude of the risk characteristic of the vehicle at the local location, and R represents the number of absolute risks greater than the specified probability threshold.

[0127] Risk enhancement perception is performed on the risk integral vector, converting the risk accumulation characteristics that originally accumulated infinitely over time into a comprehensive risk probability value between [0,1], and then using this to describe the vehicle's safety status described from the perspective of specific safety features. The risk enhancement perception calculation formula is as follows:

[0128]

[0129] In the formula Represents the cumulative risk up to the present moment, represents the cumulative risk up to the last observation moment, Represents the maximum risk in the current observation period, which is determined by the current absolute risk Q value. It is a measure of the speed of risk change. If there is no obvious risk change in the current observation period, .

[0130] Step S600 locates high-risk periods based on the risk quantification characteristics on a time scale, outputs quantitative analysis results of high-risk vehicle safety status characteristics and local information of high-risk segments, and performs risk tracing and fault diagnosis. Specifically, it includes:

[0131] Step S601, self-discharge abnormal fault judgment, collect voltage, current, SOC and other information of 2000 sampling points before and after the high-risk time to form a local feature map of the high-risk segment, refer to Figure 3 , the voltage of abnormal cells is lower than that of normal cells during the charge and discharge process, which means that under the same working conditions, abnormal cells discharge faster than other cells and charge slower than other cells. Figure 4 By comparing the high-risk points at different times, it can be seen that the voltage difference of the abnormal battery cell gradually increases in the high SOC charging section, and the self-discharge phenomenon tends to deteriorate further. The occurrence of the above phenomenon can be determined as an abnormal self-discharge fault in the vehicle.

[0132] Step S602: determine the abnormal capacity decay. Collect the voltage, current and other information of 2000 sampling points before and after the high-risk time to form a local feature map of the high-risk segment. Figure 5 During the discharge process, the voltage of the cell (red) in the high SOC segment is higher than that of the other cells, and the voltage in the low SOC segment is lower than that of the other cells. During the entire discharge process, there is an obvious intersection between the voltage drop process of this cell and that of the other cells. At the same time, during the charging process, under the same charging conditions, this cell reaches the charge cut-off voltage faster, and the BMS controls the charging process to end, resulting in the voltage of the other cells being lower than that of this cell as a whole at the end of charging. The above phenomenon can be used to determine that the cell capacity has declined abnormally.

[0133] Step S603: Connection abnormality judgment, collect voltage, current and other information of 2000 sampling points before and after the high-risk time to form a local feature map of the high-risk segment, refer to Figure 6 The voltage of the red cell fluctuates more than the other cells during driving. During parking and charging, the voltage is significantly higher than that of the other cells, indicating a significant voltage overcharge and under discharge. This phenomenon indicates a cell connection anomaly.

[0134] Step S704: Sampling abnormality judgment, collecting the single cell voltage, high risk point time, abnormal cell number and other information of a total of 2000 sampling points before and after the high risk moment to form a local feature map of the high risk segment. Figure 7 , the voltages of two adjacent battery cells shift in two opposite directions, that is, compared with other normal battery cells, the voltages of the two battery cells are obviously one high and one low, and there is no problem of high charging and low discharge in a single battery cell. The above phenomenon can be judged as sampling abnormality.

[0135] The embodiments of the present disclosure also provide a system for correcting the initial bias of the cell voltage, and this system employs the above-mentioned method for correcting the initial bias of the cell voltage.

[0136] The embodiments of the present disclosure also provide a storage medium, in which a computer program is stored. When the computer program is executed by a processor, all steps of the above-mentioned method for correcting the initial bias of the cell voltage can be implemented.

[0137] Those of ordinary skill in the art can understand that all or part of the processes in the method for correcting the initial bias of the cell voltage can be completed by instructing relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of various embodiments of the method for correcting the initial bias of the cell voltage. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0138] The embodiments of the present application also provide an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of the above-mentioned method for correcting the initial bias of the cell voltage are implemented. In the embodiments of the present application, the processor is the control center of the computer system, and can be the processor of a physical machine or the processor of a virtual machine.

[0139] Refer to Figure 8, the electronic device 500 includes: at least one processor 501, at least one communication interface 502, at least one memory 503, and at least one bus 504. Among them, the bus 504 is used to realize the connection and communication between these components, the communication interface 502 is used to communicate signaling or data with other node devices, and the memory 503 stores machine-readable instructions executable by the processor 501. When the electronic device 500 runs, the processor 501 communicates with the memory 503 through the bus 504, and when the machine-readable instructions are called by the processor 501, the steps of the above-mentioned initial bias correction method for the cell voltage are executed.

[0140] The above content is only an embodiment of the present invention. Specific structures and characteristics and other common knowledge in the solution are not described in detail here. Those of ordinary skill in the art know all the common technical knowledge in the technical field to which the invention belongs before the application date or the priority date, can know all the prior arts in this field, and have the ability to apply the conventional experimental means before this date. Those of ordinary skill in the art can, under the inspiration given in this application, combine their own abilities to complete and implement this solution. Some typical well-known structures or well-known methods should not become an obstacle for those of ordinary skill in the art to implement this application. It should be noted that for those skilled in the art, without departing from the structure of the present invention, several deformations and improvements can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the implementation effect of the present invention and the practicality of the patent. The protection scope required by this application should be based on the content of its claims, and the specific implementation manners and the like described in the specification can be used to interpret the content of the claims.

Claims

1. A method for correcting the initial bias of the cell voltage, characterized in that: Including selecting a specific continuous number of cell voltage data as the initial data set; Calculate the square of each cell data , and find the sum of squares. The formula is as follows: Among them, is the sum of squares of i battery cells, is the number of selected operating data of battery cells; Calculate the mean value , the formula is as follows: The sum of the squares of all the battery cells forms a m*1 dimensional vector , calculate the mean value of the vector , and obtain the normalization standard b. The formula is as follows: Among them, is the number of battery cells; Calculating the bias correction coefficient, with the formula as follows: Among them, represents the offset correction coefficient; Applying the bias correction coefficient to correct the voltage of each cell, with the formula as follows: Among them, represents the corrected cell voltage.

2. The method for correcting the initial bias of the cell voltage according to claim 1, wherein: Including performing data preprocessing on the obtained continuous cell voltage operation data, including the following: Performing data cleaning and conversion, processing null values and parameters beyond physical boundaries, converting the time stamp to the standard time format, and unifying parameter units; Performing data aggregation, aligning parameter fields and sampling frequencies, unifying static information, and associating static information with dynamic sampling data; Performing data standardization processing, standardizing field classifications and field names, and associating and storing them with local files through a static information table.

3. The method for correcting the initial bias of the cell voltage according to claim 2, characterized in that: Aligning the sampling frequencies, including the following: Determining the sampling frequency of the current vehicle data, calculating the sampling time difference, taking the median of the difference sequence as the target sampling period, and obtaining the sampling frequency; Determining the start and end times of the abnormal sampling frequency period; Performing linear interpolation on the abnormal sampling frequency period.

4. The method for correcting the initial bias of the cell voltage according to claim 1, wherein: Extract key data features from the corrected cell voltage operation data, quantitatively describe the risk features of the key data features based on stability, and reflect the system stability degree through mathematical expectation and variance; quantitatively describe the risk features of the key data features based on correlation, and calculate the correlation coefficient η Quantify the safety state with 1 - η Obtain the quantified value of the risk state; Quantitatively describing the risk characteristics of key data features based on consistency, and describing the consistency between cells through entropy values.

5. The method for correcting the initial bias of the cell voltage according to claim 4, characterized in that: It also includes quantifying risk characteristics, using a specific time as the characteristic observation time window, and calculating the safety quantification value corresponding to a certain moment within the characteristic observation time window , and the observed defined risk probability is as follows: 。 6. The method for correcting the initial offset of the cell voltage according to claim 5, characterized in that: It also includes performing risk characteristic identification and calculating the risk probability integral and plotting a risk cumulative curve, with the formula as follows: After obtaining the risk cumulative curve, judging the safety state of the vehicle through the characteristics of the curve; Obtained by the cumulative risk change rate and , which reflects the safety state degree of different working conditions during the battery operation process. The relative risk is defined as: is the vehicle operation safety consistency coefficient, which describes the relative risk of the vehicle The smaller the value, the safer the vehicle; Performing risk-enhanced perception on the risk integral vector, and the risk-enhanced perception calculation formula is as follows: In the formula, represents the cumulative risk up to the current moment, represents the cumulative risk up to the previous observation moment, represents the maximum risk within the current observation period, which is determined by the current absolute risk Q value, is a measure of the rate of change of risk. When there is no obvious risk change in the current observation period, .

7. The method for correcting the initial bias of the cell voltage according to claim 6, wherein: It also includes locating high-risk time periods for the risk quantification characteristics on the time scale, outputting the quantitative analysis results of the high-risk vehicle safety state characteristics and the local information of the high-risk segments, and performing risk traceability and fault diagnosis based on this.

8. A method for correcting the initial bias of the cell voltage, characterized in that: This method uses a cell voltage initial bias correction system described in any one of claims 1-7.

9. An electronic device, characterized in that: Including a memory, a processor, and a computer program stored on the memory and capable of running on the processor. When the processor executes the program, it implements a cell voltage initial bias correction method described in any one of claims 1-7.

10. A storage medium, characterized in that: A computer program is stored in the storage medium. When the computer program is executed by the processor, it can implement a cell voltage initial bias correction method described in any one of claims 1-7.

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