New energy vehicle second-hand vehicle power battery health state evaluation method and system

Abstract

The application provides a new energy vehicle second-hand power battery health state evaluation method and system, and relates to the new energy vehicle battery evaluation technical field. In the application, complete charging process data and actual driving segment data are first obtained; the time difference of each cell voltage reaching the cutoff condition in the constant voltage stage at the end of each charging is analyzed to determine a first health factor; then, from the actual driving segment data, the segment in which the power battery pack continuously discharges at an average discharge rate higher than a first threshold value is extracted, the standard deviation of the temperature of each cell in the corresponding segment is counted to determine a second health factor, and finally, according to the values of the first health factor and the second health factor, in combination with the cumulative charging cycle number and the factory rated capacity of the power battery pack, a power battery health state comprehensive evaluation report is generated. The technical scheme provided by the application realizes multi-dimensional comprehensive evaluation of the health state of the second-hand power battery, and improves the accuracy and reliability of the evaluation result.

Description

Technical Field

[0001] This application relates to the field of new energy vehicle battery evaluation technology, and in particular to a method and system for evaluating the health status of power batteries in used new energy vehicles. Background Technology

[0002] As new energy vehicles circulate more frequently in the used car market, the need for accurate and non-invasive assessment of the health status of the vehicle's core component, the power battery, is becoming increasingly urgent.

[0003] The current technical solution is an evaluation method based on historical charging data in the cloud. This method obtains historical charging records from the vehicle's cloud server, extracts characteristic parameters of the charging curve, such as the duration of the constant current charging phase or the voltage change rate, and compares them with the battery's standard charging model to estimate the battery's health.

[0004] However, this method still has limitations. Its evaluation is mainly based on charging process data, which is greatly affected by external charging conditions, and fails to effectively combine the battery's actual performance in real-world driving, especially under high-power discharge conditions. This makes it difficult for the evaluation results to comprehensively reflect the battery's performance consistency, thermal management efficiency, and other dynamic degradation characteristics under complex usage scenarios, resulting in a relatively singular evaluation dimension. Summary of the Invention

[0005] This application provides a method and system for assessing the health status of power batteries in used new energy vehicles, in order to solve the problems of incomplete assessment dimensions and insufficient adaptability to actual driving conditions in the prior art.

[0006] Firstly, this application provides a method for assessing the health status of power batteries in used new energy vehicles, including: The system acquires complete charging process data for the target vehicle for a preset number of times before the used car evaluation, as well as actual driving segment data of the target vehicle during the same period of charging. The complete charging process data includes the voltage and temperature sequence of each cell in the power battery pack during each charging, and the actual driving segment data includes the average discharge rate of the power battery pack and the temperature of each cell within the corresponding driving mileage. Based on the complete charging process data, the time difference between the voltage of each cell reaching the cutoff condition during the constant voltage stage at the end of each charge is analyzed, and the first health factor reflecting the consistency of the cells is determined according to the distribution of the time difference. From the actual driving segment data, segments in which the power battery pack continuously discharges at an average discharge rate higher than the first threshold are extracted, and the standard deviation of the temperature of each cell in the corresponding segment is calculated. Based on the evolution trend of the standard deviation, a second health factor reflecting the thermal management performance under high load is determined. Based on the values ​​of the first health factor and the second health factor, combined with the cumulative number of charging cycles and the factory rated capacity of the power battery pack, a comprehensive health status assessment report of the power battery is generated.

[0007] Optionally, acquire complete charging process data for the target vehicle for a preset number of times before the used car appraisal, as well as actual driving segment data of the target vehicle during the same period of the charging behavior, including: Obtain the historical operating data of the target vehicle prior to the evaluation time point from the cloud server associated with the target vehicle; In the historical operation data, charging events that meet the start and end conditions are identified. The start condition is that the vehicle charging port is connected to an external power source, and the end condition is that the power supply current from the external power source to the target vehicle is continuously lower than a set value for a first time length. The process between one start condition and one end condition is defined as one complete charging process. Extract the data corresponding to the complete charging process of a preset number of times from the historical operation data as the complete charging process data; For each complete charging process extracted, the moment when the starting condition occurred is located in the historical operating data, and a driving period of a preset duration is traced back, during which the operating data of the power battery pack is extracted as the actual driving segment data.

[0008] Optionally, based on the complete charging process data, the time difference between the voltage of each cell reaching the cutoff condition during the constant voltage phase at the end of each charge is analyzed, and a first health factor reflecting cell consistency is determined according to the distribution of the time difference, including: The charging end is located in the complete charging process data. In the charging end, the same voltage cutoff condition is set for each cell in the power battery pack. The voltage cutoff condition is that the voltage reading of the cell reaches a first percentage of the charging set voltage. Record the moment when the voltage reading of each cell first reaches the voltage cutoff condition during the final stage of charging. Calculate the difference between the earliest and latest moment when the voltage cutoff condition is reached among all cells. Use the difference as the charging time difference that characterizes the consistency of cell voltage during the current charging process. Obtain the charging time difference corresponding to the most recent preset number of complete charging processes, and calculate the concentration measure of the charging time difference values; The first health factor is determined based on the concentration level metric.

[0009] Optionally, the moment when the voltage reading of each cell first reaches the voltage cutoff condition during the final stage of charging is recorded, and the difference between the earliest and latest moment when the voltage cutoff condition is reached among all cells is calculated. This difference is used as the charging time difference characterizing the voltage consistency of the cells during the current charging process, including: During the final stage of charging, the voltage readings of each cell in the power battery pack are determined sequentially according to the time sequence of the data recorded during the complete charging process. When any voltage reading is found to meet the voltage cutoff condition, the subsequent judgment of the cell is stopped, and the corresponding acquisition time point of the voltage reading in the complete charging process data is recorded as the voltage compliance time of the cell. After obtaining the voltage compliance time of all cells in the power battery pack, the earliest voltage compliance time is selected from all voltage compliance times as the minimum time, and the latest voltage compliance time is selected as the maximum time. The charging time difference is obtained by calculating the time length difference between the maximum time and the minimum time.

[0010] Optionally, from the actual driving segment data, segments in which the power battery pack continuously discharges at an average discharge rate higher than a first threshold are extracted. The standard deviation of the temperature of each cell within the corresponding segment is calculated. Based on the evolution trend of the standard deviation, a second health factor reflecting the thermal management performance under high load is determined, including: By traversing the actual driving segment data, the time interval in which the total current reading of the power battery pack is continuously higher than the preset rate threshold is identified, and the time interval that meets the preset condition is determined as a high-load discharge segment. The temperature readings of all cells in the power battery pack are obtained within the time range of the high-load discharge segment. The average temperature of the temperature readings of each cell within the high-load discharge segment is calculated. Based on the average temperature of all cells, a comprehensive temperature dispersion value is calculated. All high-load discharge segments are aggregated to obtain a comprehensive temperature dispersion value associated with each charging process; Arrange the comprehensive temperature dispersion values ​​of each group corresponding to the most recent preset number of charging processes according to the time sequence of the charging process, and analyze the monotonicity of the comprehensive temperature dispersion values ​​of each group as the charging process sequence changes. The second health factor is determined based on the degree of monotonicity.

[0011] Optionally, according to the time sequence of the charging process, the comprehensive temperature dispersion values ​​corresponding to the most recent preset number of charging processes are arranged, and the monotonicity of the comprehensive temperature dispersion values ​​of each group as the charging process sequence changes is analyzed, including: Sort the most recent preset number of charging processes in chronological order of when they occurred; For each of the sorted charging processes, find the comprehensive temperature dispersion value corresponding to all high-load discharge segments associated with each of the sorted charging processes, and calculate the average value of the comprehensive temperature dispersion value as the comprehensive temperature dispersion level value. The discrete temperature levels corresponding to each charging process are arranged in chronological order according to the charging process to obtain a sequence of discrete temperature levels. Compare adjacent values ​​in the comprehensive temperature discrete level value sequence one by one, and count the number of comparisons in which the latter value is greater than the former value; The number of comparisons is calculated as a percentage of the total number of adjacent comparisons in the sequence, and this percentage is used as the degree of monotonicity.

[0012] Optionally, based on the values ​​of the first health factor and the second health factor, combined with the cumulative number of charging cycles and the factory rated capacity of the power battery pack, a comprehensive health status assessment report of the power battery is generated, including: The values ​​of the first health factor, the second health factor, and the cumulative number of charging cycles are used as input parameters. The input parameters are input into a predefined score mapping rule, and an initial score is obtained by querying the score mapping rule. The score mapping rule defines the output score corresponding to different combinations of the input parameters. The initial score is associated with the factory rated capacity of the power battery pack, and the initial score is adjusted and calculated based on the factory rated capacity to obtain a comprehensive battery health status score in percentage form. The overall battery health status score is compared with multiple pre-set score intervals to determine the target score interval into which the overall battery health status score falls, and the corresponding evaluation level is determined according to the pre-set level label of each target score interval. The initial score is obtained through the score mapping rule, and during the adjustment calculation based on the factory rated capacity, the input parameter that has the greatest impact on the overall score of the battery health status is identified, and the battery characteristics represented by the input parameter are determined as the key judgment basis. The evaluation level, the overall battery health status score, the cumulative number of charging cycles, the values ​​of the first health factor and the second health factor, and the key judgment criteria are combined to output a comprehensive evaluation report on the health status of the power battery.

[0013] Secondly, this application provides a health status assessment system for used power batteries of new energy vehicles, including: The acquisition module is used to acquire complete charging process data of the target vehicle for a preset number of times before the used car evaluation, as well as actual driving segment data of the target vehicle during the same period of the charging behavior. The complete charging process data includes the voltage and temperature sequence of each cell in the power battery pack during each charging, and the actual driving segment data includes the average discharge rate of the power battery pack and the temperature of each cell within the corresponding driving mileage. The analysis module is used to analyze the time difference between the voltage of each cell reaching the cutoff condition during the constant voltage stage at the end of each charge based on the complete charging process data, and to determine the first health factor reflecting the consistency of the cells based on the distribution of the time difference. The extraction module is used to extract segments from the actual driving segment data where the power battery pack continuously discharges at an average discharge rate higher than a first threshold, calculate the standard deviation of the temperature of each cell in the corresponding segment, and determine a second health factor reflecting the thermal management performance under high load based on the evolution trend of the standard deviation. The generation module is used to generate a comprehensive health status assessment report of the power battery based on the values ​​of the first health factor and the second health factor, combined with the cumulative number of charging cycles and the factory rated capacity of the power battery pack.

[0014] Thirdly, this application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement the method for assessing the health status of a used power battery of a new energy vehicle as described in the first aspect above.

[0015] Fourthly, this application provides a computer storage medium storing a computer program, which, when executed by a computer, implements a method for assessing the health status of a used power battery in a new energy vehicle as described in the first aspect.

[0016] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1A flowchart of a method for assessing the health status of a used power battery in a new energy vehicle, as provided in this application, is shown. Figure 2 This application provides a schematic diagram of the structure of a health status assessment system for used power batteries of new energy vehicles. Figure 3 A schematic diagram of the structure of a computing device provided in this application is shown. Detailed Implementation

[0019] To enable those skilled in the art to better understand the present application, the technical solution of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0020] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be performed in the order they appear herein, or may be performed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be performed sequentially or in parallel. It should be noted that the terms "first," "second," etc., used herein are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit the first and second to different types.

[0021] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] Figure 1 This application provides a flowchart of a method for assessing the health status of used power batteries in new energy vehicles, such as... Figure 1 As shown, the method includes: Step 101: Obtain complete charging process data of the target vehicle for a preset number of times before the used car evaluation, as well as actual driving segment data of the target vehicle during the same period of the charging behavior.

[0023] Optionally, step 101 may specifically include: Step 1011: Obtain the historical operating data of the target vehicle before the evaluation time point from the cloud server associated with the target vehicle.

[0024] Step 1012: In the historical operation data, identify charging events that meet the start condition and the end condition. The start condition is that the vehicle charging port is connected to an external power source, and the end condition is that the power supply current from the external power source to the target vehicle is continuously lower than a set value for a first time length. The process between one start condition and one end condition is defined as a complete charging process.

[0025] Step 1013: Extract the data corresponding to the complete charging process of a preset number of times from the historical operation data as the complete charging process data.

[0026] Step 1014: For each extracted complete charging process, locate the moment when the starting condition occurred in the historical operation data, trace back a preset driving period, and extract the operation data of the power battery pack during the driving period as the actual driving segment data.

[0027] In this step, the complete charging process data refers to the time-series data set of the target vehicle before the time of the used car evaluation, which includes the entire process from the start of charging by connecting to an external power source to the charging current dropping to near zero and thus completing the charging. This data usually includes parameters such as the voltage, current, temperature, and state of charge of the power battery pack at each time point during the charging process.

[0028] Actual driving segment data refers to the battery pack operation data during actual vehicle driving within a preset time period (e.g., 24 hours before charging) before each complete charging process is extracted. This data reflects the battery usage conditions and load history immediately preceding the current charging.

[0029] A cloud server is a remote computer system that is connected to the target vehicle via a network to receive and store various operational data uploaded by the vehicle at regular intervals or triggered by events.

[0030] Historical operating data refers to the collection of operating status information of the target vehicle obtained from the cloud server and recorded in chronological order over a period of time. It typically includes data from multiple dimensions such as vehicle status, charging status, battery parameters, and mileage.

[0031] A charging event refers to an independent and clearly defined charging behavior. In the scenario of this application, its starting condition is defined as the moment when the vehicle charging port is physically connected to an external power source and the charging circuit is turned on, and its ending condition is defined as the moment when the power supply current of the external power source to the vehicle's power battery pack is continuously lower than a set small current threshold (e.g., 1 ampere) and reaches a first time length (e.g., 5 minutes). The entire process from the starting moment to the ending moment is determined as a complete charging process.

[0032] In this step, a query request is first sent to the cloud server uniquely bound to the target vehicle through an application programming interface or a dedicated data channel to obtain the vehicle's historical operating data covering a sufficiently long time range before the specified evaluation time point. Secondly, in the acquired historical operation data, the vehicle status field is scanned by the program algorithm to identify all record points that meet the charging event start conditions, that is, the moment when the charging port is connected and charging begins. Then, starting from each starting point, the data is scanned backward to find the record point that meets the conditions for the end of the charging event, that is, the moment when the charging current is continuously lower than the set threshold for a first time length; thus, each starting point is paired with its corresponding ending point, and multiple non-overlapping complete charging process time periods are divided on the historical timeline. Next, from all the identified complete charging processes, according to a preset strategy (e.g., selecting the N closest to the evaluation time point), a preset number of complete charging processes are extracted, and all historical running data subsets within the corresponding time periods of these processes are extracted as complete charging process data for subsequent analysis; then, for each extracted complete charging process, the moment when its starting condition occurs is located on the timeline, and using that moment as a reference point, a preset duration is traced back, which defines a driving period; Finally, from the historical operating data corresponding to this driving period, the data sequence formed by extracting the relevant operating parameters of the power battery pack (such as discharge current, voltage, temperature, etc.) is used as the actual driving segment data during the same period as this charging behavior.

[0033] For example, taking a pure electric vehicle of brand A as an example, assuming the preset number of times is 5 and the preset traceability driving time is 2 hours.

[0034] First, the system accesses the cloud data platform corresponding to the vehicle via the vehicle identification number to obtain the vehicle's historical operating data over the past six months. Then, it scans the historical data to identify charging events that meet certain conditions. For example, if the program finds a charging gun connection log record and then finds a record of an input current of less than 0.5A for 3 consecutive minutes about 4 hours later, it marks the data segment between these two records as a complete charging process. According to this rule, a total of 8 recent complete charging processes of the vehicle are identified. Next, the five most recent charging times from these eight times are selected. During these five charging processes, the voltage and temperature readings of the 192 cells recorded by the battery management system are extracted every second to form complete charging process data. For each of these five charging times, the specific time point when the charging gun is connected is located. Assuming that the first charging started at 20:00 on March 1st, the program will capture all driving data of the vehicle from 18:00 to 20:00 on March 1st, especially the total current value of the power battery pack and the temperature value of each cell. This part of the data is the actual driving segment data corresponding to the first charging. This operation is repeated for the other four charging processes, and finally, a total of five sets of charging process data and their corresponding five sets of actual driving segment data are obtained.

[0035] Step 102: Based on the complete charging process data, analyze the time difference between the voltage of each cell reaching the cutoff condition during the constant voltage stage at the end of each charge, and determine the first health factor reflecting the consistency of the cells based on the distribution of the time difference.

[0036] Optionally, step 102 may specifically include: Step 1021: Locate the final stage of charging in the complete charging process data. In the final stage of charging, set the same voltage cutoff condition for each cell in the power battery pack. The voltage cutoff condition is that the voltage reading of the cell reaches a first percentage of the set charging voltage.

[0037] Step 1022: Record the moment when the voltage reading of each cell first reaches the voltage cutoff condition during the final stage of charging; calculate the difference between the earliest and latest times when the voltage cutoff condition is reached among all cells; and use the difference as the charging time difference characterizing the consistency of cell voltage during the current charging process.

[0038] Optionally, step 1022 may specifically include: During the final stage of charging, the voltage readings of each cell in the power battery pack are sequentially determined according to the chronological order of the complete charging process data recording. When any voltage reading meets the voltage cutoff condition, the subsequent determination of the cell is stopped, and the acquisition time point corresponding to the voltage reading in the complete charging process data is recorded as the voltage attainment time of the cell. After obtaining the voltage attainment times of all cells in the power battery pack, the earliest voltage attainment time is selected as the minimum time, and the latest voltage attainment time is selected as the maximum time. The time difference between the maximum time and the minimum time is calculated to obtain the charging time difference.

[0039] Step 1023: Obtain the charging time difference corresponding to the most recent preset number of complete charging processes, and calculate the concentration measure of the charging time difference values.

[0040] Step 1024: Determine the first health factor based on the concentration degree metric.

[0041] In this step, the final stage of charging refers to the later stage of the power battery charging process, when the charging strategy enters a specific phase where the voltage is constant and the charging current gradually decreases until the charging ends.

[0042] The voltage cutoff condition is a uniformly set voltage target value for each battery cell, used to determine whether the battery cell is fully charged at the end of the charging process. Its specific value is usually a certain percentage of the battery pack's charging set voltage, such as 95%.

[0043] The voltage threshold moment is the precise time when the voltage reading of a particular cell first reaches or exceeds the voltage cutoff condition at the end of the charging process. The charging time difference is an indicator calculated for a single complete charging process. Its value is equal to the time length between the latest and earliest voltage threshold moments for all cells during that charging cycle. This difference directly reflects the difference in the charging speed of all cells; the smaller the difference, the better the voltage consistency of the cells.

[0044] The concentration measure is a statistical analysis of a series of charging time difference values ​​corresponding to the most recent preset number of charging processes. It is used to measure the size of the fluctuation range of this set of time difference data, such as the standard deviation of this set of data.

[0045] The first health factor is a numerical value used to comprehensively reflect the consistent aging status of battery pack cells. It is calculated based on the concentration degree measure value. This factor is inversely proportional to the consistency level; that is, the smaller the fluctuation reflected by the concentration degree measure value, the better the value of the first health factor.

[0046] In this step, firstly, for each complete charging process data acquired, the program will automatically identify the end of the charging process. This stage usually corresponds to the interval where the charging current curve transitions from a rapid decline to a gradual decline plateau. At the same time, a uniform voltage cutoff condition is set for this evaluation, for example, 95% of the charging set voltage is used as the threshold. This threshold will be applied to each cell in the battery pack. Next, the core calculation process begins. It iterates through the voltage data points of each cell arranged in chronological order during the final stage of charging. When it detects that the voltage value of a cell is not lower than the previously set voltage cutoff condition for the first time, the program records the time corresponding to this data point as the voltage target moment for that cell. After completing the above judgment and recording for all cells, the program finds the earliest and latest voltage target moments from all the recorded voltage target moments and calculates the difference between these two moments. This difference is defined as the charging time difference for this charging process. Then, obtain multiple charging time differences corresponding to the most recent preset number of charging processes (e.g., the most recent 5 times), calculate the concentration measure of this set of charging time difference values, such as calculating their standard deviation, to quantify the fluctuation of cell consistency during these charging processes. Finally, based on the calculated concentration measure, the specific value of the first health factor is determined through a preset mapping relationship. This mapping relationship is designed such that the smaller the concentration measure, the better the health status represented by the mapped first health factor value.

[0047] Following the previous example, the program has obtained 5 sets of complete charging process data for the target brand A vehicle.

[0048] First, the data from the first charge is processed. In the current data sequence of this charge, the program identifies the stage where the current value slowly decreases from about 20 amps to close to 0 amps, and determines this stage as the end of the charging process. The charging voltage setting for this vehicle model is 403.2 volts. The program sets the first percentage to 95%, and calculates the voltage cutoff condition as 403.2 volts × 0.95 = 383.04 volts. Next, the program scans the voltage data of the 192 cells at the end of the charging process. For example, if cell number 1 reaches 383.1 volts at 140 minutes after charging begins, exceeding the 383.04 volt threshold, the program records the voltage achievement time as 140 minutes. Cell number 2 reaches the threshold at 145 minutes, and this is recorded as 145 minutes. Following this rule, the cell that reaches the threshold latest (e.g., cell number 188) is recorded at 152 minutes. The program finds the earliest achievement time of 140 minutes and the latest achievement time of 152 minutes, and calculates the charging time difference for the first charge as 152 minus 140, which equals 12 minutes. The program repeats the above calculation for the other four charging processes, obtaining five charging time differences, for example, these five values ​​are [12, 15, 18, 20, 25] (unit: minutes). Then, the central tendency of these five charging time differences is calculated. Here, we take the calculation of the sample standard deviation as an example. The calculation formula is as follows:

[0049] in, Indicates the sample standard deviation (a measure of concentration). Indicates the first One charging time difference; This represents the sample mean; Indicates the number of samples.

[0050] Substitute the data as follows:

[0051] Calculate the sum of squared deviations:

[0052] Substituting into the formula:

[0053] Finally, according to the preset mapping rules, this concentration measure of approximately 4.95 is converted into a first health factor value that characterizes cell consistency. For example, the first health factor is 85 obtained through a linear transformation formula.

[0054] Step 103: Extract segments from the actual driving segment data where the power battery pack continuously discharges at an average discharge rate higher than the first threshold, calculate the standard deviation of the temperature of each cell in the corresponding segment, and determine a second health factor reflecting the thermal management performance under high load based on the evolution trend of the standard deviation.

[0055] Optionally, step 103 may specifically include: Step 1031: Traverse the actual driving segment data, identify the time interval in which the total current reading of the power battery pack is continuously higher than the preset rate threshold, and determine the time interval that meets the preset conditions as a high-load discharge segment.

[0056] Step 1032: Obtain the temperature readings of all cells in the power battery pack within the time range of the high-load discharge segment, calculate the average temperature of the temperature readings of each cell within the high-load discharge segment, and calculate the comprehensive temperature dispersion value based on the average temperature of all cells.

[0057] Step 1033: Aggregate all high-load discharge segments to obtain a comprehensive temperature dispersion value associated with each charging process.

[0058] Step 1034: Arrange the comprehensive temperature dispersion values ​​of each group corresponding to the most recent preset number of charging processes according to the time sequence of the charging process, and analyze the monotonicity of the changes of the comprehensive temperature dispersion values ​​of each group with the order of the charging process.

[0059] Optionally, step 1034 may specifically include: The most recent preset number of charging processes are sorted according to the chronological order of their occurrence. For each sorted charging process, the comprehensive temperature dispersion value corresponding to all high-load discharge segments associated with that process is identified, and the average value of this comprehensive temperature dispersion value is calculated as the comprehensive temperature dispersion level value. The comprehensive temperature dispersion level values ​​corresponding to each charging process are arranged according to the chronological order of the charging processes to obtain a comprehensive temperature dispersion level value sequence. Adjacent values ​​in the sequence are compared sequentially, and the number of times the latter value is greater than the former value is counted. The ratio of this number of comparisons to the total number of adjacent comparisons in the sequence is calculated to obtain a percentage value, which is used as the degree of monotonicity.

[0060] Step 1035: Determine the second health factor based on the degree of monotonicity.

[0061] In this step, the high-load discharge segment refers to a period of time in which the output current of the power battery pack is continuously maintained at a high level, as identified from the actual driving segment data. The determination is based on the fact that the average discharge rate (i.e., the ratio of current to battery rated capacity) converted from the total current reading of the battery pack is continuously higher than a preset first threshold, and this state is maintained for a preset minimum time.

[0062] The average temperature is a value calculated by averaging all temperature readings of a cell within a certain high-load discharge segment, representing the average temperature level of the cell within that segment.

[0063] The overall temperature dispersion value is an index calculated for a high-load discharge segment. It reflects the degree of difference between the average temperatures of all cells in the battery pack within that segment. The larger the value, the greater the average temperature difference between the cells within that segment, and the worse the heat dissipation uniformity may be.

[0064] The overall temperature dispersion level is calculated as the average of the overall temperature dispersion values ​​for all high-load discharge segments associated with a single charge, and is used to represent the overall temperature dispersion level during the driving period prior to that charge.

[0065] Monotonicity is a statistic used to quantify the degree to which the comprehensive temperature discrete level value sequence corresponding to the most recent preset number of charging processes shows a monotonically increasing trend over time. For example, it can be measured by calculating the proportion of times the later term in the sequence is greater than the earlier term.

[0066] The second health factor is a value used to comprehensively reflect the aging trend of the thermal management performance of the battery pack under high load. It is calculated based on the monotonicity degree. This factor is related to the degradation trend of thermal management performance. That is, the more obvious the increasing trend of temperature dispersion level reflected by the monotonicity degree, the worse the value of the second health factor is.

[0067] In this step, firstly, each segment of actual driving data is traversed, and the average discharge rate is calculated in real time by dividing the total current reading of the power battery pack by the rated capacity of the battery. Then, continuous time intervals with an average discharge rate that are consistently higher than a preset first threshold (e.g., 1C) and last for more than another preset duration (e.g., 30 seconds) are identified, and each such interval is defined as an independent high-load discharge segment. Next, for each identified high-load discharge segment, the temperature readings of all cells within that segment's time range are obtained. The average of all temperature readings for each cell within that segment is calculated individually to obtain a value representing the average temperature of that cell within that segment. Then, based on the average temperature of all the cells, a statistical value measuring their dispersion from one another is calculated, namely the comprehensive temperature dispersion value, such as calculating the standard deviation of this set of temperature averages. Then, the program gathers together all the high-load discharge segments identified for a certain segment of actual driving data (corresponding to a charging process) and their calculated comprehensive temperature dispersion values ​​to form a data set associated with that charging process; Then, the most recent preset number of charging processes (e.g., 5 times) are sorted according to their chronological order, and the average value of all associated comprehensive temperature dispersion values ​​is calculated for each charging process to obtain the comprehensive temperature dispersion level value corresponding to that charging process. After arranging these comprehensive temperature dispersion level values ​​into a sequence in chronological order, the program evaluates the trend by analyzing the monotonicity of this sequence. Specifically, the program compares the size of two adjacent values ​​in the sequence in turn, counts the number of times the later value is greater than the previous value, and then divides this number by the total number of possible adjacent comparisons in the sequence to obtain a percentage value as the degree of monotonicity. Finally, based on the calculated monotonicity level, the specific value of the second health factor is determined through a preset mapping relationship. This mapping relationship is designed such that the higher the monotonicity level, the worse the health status represented by the mapped second health factor value.

[0068] Continuing from the previous example, the program has obtained 5 sets of actual driving segment data for the target brand A vehicle, whose power battery has a rated capacity of 60 amp-hours.

[0069] The program first processes the driving segment data corresponding to the first charge (March 1, 18:00 to 20:00). The program reads the total current data of the power battery per second within this segment and divides it by 60 amp-hours to obtain the instantaneous discharge rate; the first threshold is set to 1C, and the minimum duration is 30 seconds; the program identifies that within this 2-hour driving period, there are 3 time periods that meet the condition that the discharge rate is continuously higher than 1C and exceeds 30 seconds, such as [18:15:10, 18:16:05], [18:40:20, 18:41:15] and [19:20:30, 19:22:10]. These three time periods are identified as three high-load discharge segments. For the first high-load discharge segment (lasting 55 seconds), the program acquires the temperature readings per second for 192 cells within those 55 seconds. First, it calculates the average temperature of each cell over those 55 seconds; assuming cell 1 has an average temperature of 35.2 degrees Celsius, cell 2 has 34.8 degrees Celsius, and so on, resulting in 192 average temperatures. Then, it calculates the standard deviation of these 192 average temperatures as the overall temperature dispersion value for that segment; assuming the calculated standard deviation is 1.5 degrees Celsius. Simultaneously, the program performs the same calculation for the other two segments, assuming the overall temperature dispersion values ​​are 1.8 degrees Celsius and 2.1 degrees Celsius, respectively. These three values ​​are then associated with the first charging process. The program then repeats the segment identification and dispersion calculation process for the driving segments corresponding to the other four charging processes, ultimately obtaining five sets of data. For example, the associated values ​​for the first charging are {1.5, 1.8, 2.1}, the second is {1.7, 2.0}, the third is {2.2, 2.5}, the fourth is {2.4}, and the fifth is {2.8, 3.0}. Next, the program calculates the comprehensive temperature dispersion level value corresponding to each charging, that is, averages each set of values: the first is (1.5+1.8+2.1) / 3=1.8, the second is (1.7+2.0) / 2=1.85, the third is (2.2+2.5) / 2=2.35, the fourth is 2.4, and the fifth is (2.8+3.0) / 2=2.9. Arranging them in chronological order, the comprehensive temperature dispersion level value sequence is obtained as [1.8, 1.85, 2.35, 2.4, 2.9]. Then, the monotonicity of the sequence is analyzed: adjacent values ​​are compared sequentially, 1.85>1.8 is counted once, 2.35>1.85 is counted once, 2.4>2.35 is counted once, and 2.9>2.4 is counted once, for a total of 4 times, where the later term is greater than the earlier term; the total number of adjacent comparisons is 4 (there are 4 pairs of adjacent relationships among 5 values), therefore the monotonicity is 4 divided by 4 equals 1, which is 100%; Finally, based on this monotonicity of 1 (or 100%), it is converted into a specific second health factor value that characterizes the thermal management performance under high load through a preset mapping rule. For example, the second health factor is 72 obtained through a linear transformation formula.

[0070] Step 104: Based on the values ​​of the first health factor and the second health factor, and combined with the cumulative number of charging cycles and the factory rated capacity of the power battery pack, generate a comprehensive assessment report on the health status of the power battery.

[0071] Optionally, step 104 may specifically include: Step 1041: The values ​​of the first health factor, the second health factor, and the cumulative number of charging cycles are used as input parameters.

[0072] Step 1042: Input the input parameters into a predefined score mapping rule, and obtain an initial score by querying the score mapping rule. The score mapping rule defines the output score corresponding to different combinations of the input parameters.

[0073] Step 1043: Associate the initial score with the factory rated capacity of the power battery pack, and adjust the initial score based on the factory rated capacity to obtain a comprehensive battery health status score in percentage form.

[0074] Step 1044: Compare the overall battery health status score with multiple pre-set score intervals to determine the target score interval into which the overall battery health status score falls, and determine the corresponding evaluation level according to the pre-set level label for each target score interval.

[0075] Step 1045: During the process of obtaining the initial score through the score mapping rule and adjusting the calculation based on the factory rated capacity, the input parameter that has the greatest impact on the overall score of the battery health status is identified, and the battery characteristics represented by the input parameter are determined as the key judgment basis.

[0076] Step 1046: Combine the assessment level, the comprehensive battery health status score, the cumulative number of charging cycles, the values ​​of the first health factor and the second health factor, and the key judgment criteria to output a comprehensive assessment report on the health status of the power battery.

[0077] In this step, the input parameters refer to the raw evaluation values ​​used to calculate the overall score. In this method, these specifically include the first health factor obtained from charging consistency analysis, the second health factor obtained from driving thermal management analysis, and the cumulative number of charging cycles of the power battery pack read from the vehicle system.

[0078] The score mapping rule is a predefined logical correspondence stored in the system. It clarifies what kind of initial score each combination of the first health factor value, the second health factor value, and the cumulative number of charging cycles corresponds to.

[0079] The initial score is an intermediate rating obtained by querying the aforementioned score mapping rules, which comprehensively reflects the battery aging state represented by the input parameters. The overall battery health status score is a final overall health score expressed on a percentage basis, calculated by further adjusting the initial score in conjunction with the basic specification parameter of the power battery pack's factory rated capacity.

[0080] The target score range is a predefined numerical range used to categorize and classify the overall percentage score, for example, 90 to 100 points are divided into one range.

[0081] The assessment level is a text label that is linked to each target score range and is used to visually represent the health status, such as excellent, good, average, and needs attention.

[0082] The key criterion is to analyze the contribution of each input parameter to the calculation of the comprehensive score and identify the battery characteristic dimension represented by the input parameter that has the greatest impact on the final score, such as cell consistency or high-load heat dissipation performance.

[0083] The comprehensive health status assessment report of the power battery is the final output of this method. It is a structured document that includes the assessment level, the comprehensive health status score of the battery, the cumulative number of charging cycles, the specific values ​​of the first health factor and the second health factor, and the key judgment criteria.

[0084] In this step, the first health factor value, the second health factor value calculated in the previous step, and the cumulative number of charging cycles obtained from the vehicle data are prepared as a set of input parameters. Next, this set of input parameters is substituted into a predefined score mapping rule for querying. This rule can be a multidimensional lookup table or a rule engine containing multiple calculation conditions. Its core function is to match or calculate an initial score that characterizes the basic aging degree of the battery based on the specific values ​​of the three input parameters. The initial score is then correlated with the factory rated capacity of the power battery pack and calculated according to a preset adjustment rule, such as applying different adjustment coefficients to batteries with different capacity ranges, thereby converting the initial score into a comprehensive battery health status score in percentage form between 0 and 100, making the scores of batteries with different capacities comparable. The comprehensive score is then compared one by one with multiple pre-set consecutive score intervals in the system to determine which score interval it falls into. Based on the pre-set level label of that interval, the evaluation level corresponding to this evaluation is automatically determined. At the same time, during the entire calculation process of the initial score through the score mapping rule and the adjustment calculation based on the factory rated capacity, the program will simultaneously track and analyze the magnitude of the influence of the numerical changes of the three input parameters—the first health factor, the second health factor, and the cumulative number of charging cycles—on the final comprehensive battery health status score. By comparing their contribution to the score change, the parameter with the largest contribution is identified and the battery performance aspect it represents is determined as the key judgment criterion. Finally, all the result elements obtained from the above steps, including the assessment level, the overall battery health status score, the cumulative number of charging cycles, the value of the first health factor, the value of the second health factor, and the key judgment criteria, are combined and formatted according to the preset report template to generate a structured comprehensive assessment report of the power battery health status and output it.

[0085] Continuing from the previous example, the program has calculated that the first health factor of the target brand A vehicle is 85, the second health factor is 72, and the data from the cloud shows that the cumulative charging cycle of the vehicle's power battery pack is 800 times, and the factory rated capacity is 60 amp-hours.

[0086] First, these values ​​are used as input parameters. Then, these parameters are input into a predefined score mapping rule, which exists in the form of a lookup table. For example, the rule defines that when the first health factor is in the range of 80-89, the second health factor is in the range of 70-79, and the number of cycles is in the range of 750-850, the corresponding initial score is 78. The initial score obtained through query is 78. Then, capacity adjustment calculation is performed. The preset rule is: for batteries with a rated capacity greater than or equal to 50 Ah and less than 70 Ah, the adjustment coefficient is 1.05. The battery health status comprehensive score is calculated as follows: initial score × adjustment coefficient = 78 × 1.05 = 81.9. The program then rounds it up to 82 points (out of 100). The score of 82 was then compared with the preset score range, which was assumed to be: 90-100 points for "Excellent", 80-89 points for "Good", 70-79 points for "Average", and below 70 points for "Needs Attention". The score of 82 fell into the 80-89 range, so the evaluation level was determined to be "Good". At the same time, during the calculation process, the contribution of each input parameter to the final score of 82 was analyzed. It was assumed that by analyzing the contribution weight, it was found that the value of the second health factor had the most significant negative effect on the score result. Therefore, the key judgment criterion was determined to be "high load heat dissipation performance". Finally, all the above information is combined to generate a comprehensive health status assessment report for the power battery, which includes: assessment level "good", battery health status comprehensive score "82", cumulative charging cycle count "800", first health factor "85", second health factor "72", and key judgment criterion "high load heat dissipation performance".

[0087] Figure 2 This application provides a structural schematic diagram of a health status assessment system for used power batteries of new energy vehicles, as shown in the diagram. Figure 2 As shown, the system includes: The acquisition module 21 is used to acquire complete charging process data of the target vehicle for a preset number of times before the used car evaluation, as well as actual driving segment data of the target vehicle during the same period of the charging behavior. The complete charging process data includes the voltage and temperature sequence of each cell in the power battery pack during each charging. The actual driving segment data includes the average discharge rate of the power battery pack and the temperature of each cell within the corresponding driving mileage. Analysis module 22 is used to analyze the time difference between the voltage of each cell reaching the cutoff condition during the constant voltage stage at the end of each charge based on the complete charging process data, and to determine the first health factor reflecting the consistency of the cells based on the distribution of the time difference. Extraction module 23 is used to extract segments from the actual driving segment data in which the power battery pack continuously discharges at an average discharge rate higher than a first threshold, calculate the standard deviation of the temperature of each cell in the corresponding segment, and determine a second health factor reflecting the thermal management performance under high load based on the evolution trend of the standard deviation. The generation module 24 is used to generate a comprehensive health status assessment report of the power battery based on the values ​​of the first health factor and the second health factor, combined with the cumulative number of charging cycles and the factory rated capacity of the power battery pack.

[0088] Figure 2 The aforementioned health status assessment system for used power batteries of new energy vehicles can perform... Figure 1 The implementation principle and technical effects of the method for assessing the health status of a used power battery in a new energy vehicle, as described in the illustrated embodiment, will not be repeated here. The specific methods by which each module and unit of the new energy vehicle used power battery health status assessment system in the above embodiments are described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0089] In one possible design, Figure 2 The illustrated embodiment of a health status assessment system for used power batteries of new energy vehicles can be implemented as a computing device, such as... Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32; The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0090] The processing component 32 is used for the above Figure 1 The embodiment describes a method for assessing the health status of power batteries in used new energy vehicles.

[0091] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0092] Storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0093] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0094] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0095] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0096] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0097] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The embodiment shown is a method for assessing the health status of a used power battery in a new energy vehicle.

[0098] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0099] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0100] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0101] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for assessing the health status of power batteries in used new energy vehicles, characterized in that, include: The system acquires complete charging process data for the target vehicle for a preset number of times before the used car evaluation, as well as actual driving segment data of the target vehicle during the same period of charging. The complete charging process data includes the voltage and temperature sequence of each cell in the power battery pack during each charging, and the actual driving segment data includes the average discharge rate of the power battery pack and the temperature of each cell within the corresponding driving mileage. Based on the complete charging process data, the time difference between the voltage of each cell reaching the cutoff condition during the constant voltage stage at the end of each charge is analyzed, and the first health factor reflecting the consistency of the cells is determined according to the distribution of the time difference. From the actual driving segment data, segments in which the power battery pack continuously discharges at an average discharge rate higher than the first threshold are extracted, and the standard deviation of the temperature of each cell in the corresponding segment is calculated. Based on the evolution trend of the standard deviation, a second health factor reflecting the thermal management performance under high load is determined. Based on the values ​​of the first health factor and the second health factor, combined with the cumulative number of charging cycles and the factory rated capacity of the power battery pack, a comprehensive health status assessment report of the power battery is generated.

2. The method according to claim 1, characterized in that, Obtain complete charging process data for the target vehicle for a preset number of times before the used car appraisal, as well as actual driving segment data of the target vehicle during the same period of charging, including: Obtain the historical operating data of the target vehicle prior to the evaluation time point from the cloud server associated with the target vehicle; In the historical operation data, charging events that meet the start and end conditions are identified. The start condition is that the vehicle charging port is connected to an external power source, and the end condition is that the power supply current from the external power source to the target vehicle is continuously lower than a set value for a first time length. The process between one start condition and one end condition is defined as one complete charging process. Extract the data corresponding to the complete charging process of a preset number of times from the historical operation data as the complete charging process data; For each complete charging process extracted, the moment when the starting condition occurred is located in the historical operating data, and a driving period of a preset duration is traced back, during which the operating data of the power battery pack is extracted as the actual driving segment data.

3. The method according to claim 1, characterized in that, Based on the complete charging process data, the time difference between each cell's voltage reaching the cutoff condition during the constant voltage phase at the end of each charge is analyzed. Based on the distribution of these time differences, a first health factor reflecting cell consistency is determined, including: The charging end is located in the complete charging process data. In the charging end, the same voltage cutoff condition is set for each cell in the power battery pack. The voltage cutoff condition is that the voltage reading of the cell reaches a first percentage of the charging set voltage. Record the moment when the voltage reading of each cell first reaches the voltage cutoff condition during the final stage of charging. Calculate the difference between the earliest and latest moment when the voltage cutoff condition is reached among all cells. Use the difference as the charging time difference that characterizes the consistency of cell voltage during the current charging process. Obtain the charging time difference corresponding to the most recent preset number of complete charging processes, and calculate the concentration measure of the charging time difference values; The first health factor is determined based on the concentration level metric.

4. The method according to claim 3, characterized in that, Record the moment when the voltage reading of each cell first reaches the voltage cutoff condition during the final stage of charging. Calculate the difference between the earliest and latest moment when the voltage cutoff condition is reached among all cells. Use this difference as the charging time difference characterizing the voltage consistency of the cells during the current charging process, including: During the final stage of charging, the voltage readings of each cell in the power battery pack are determined sequentially according to the time sequence of the data recorded during the complete charging process. When any voltage reading is found to meet the voltage cutoff condition, the subsequent judgment of the cell is stopped, and the corresponding acquisition time point of the voltage reading in the complete charging process data is recorded as the voltage compliance time of the cell. After obtaining the voltage compliance time of all cells in the power battery pack, the earliest voltage compliance time is selected from all voltage compliance times as the minimum time, and the latest voltage compliance time is selected as the maximum time. The charging time difference is obtained by calculating the time length difference between the maximum time and the minimum time.

5. The method according to claim 1, characterized in that, From the actual driving segment data, segments in which the power battery pack continuously discharges at an average discharge rate higher than a first threshold are extracted. The standard deviation of the temperature of each cell within the corresponding segment is calculated. Based on the evolution trend of the standard deviation, a second health factor reflecting the thermal management performance under high load is determined, including: By traversing the actual driving segment data, the time interval in which the total current reading of the power battery pack is continuously higher than the preset rate threshold is identified, and the time interval that meets the preset condition is determined as a high-load discharge segment. The temperature readings of all cells in the power battery pack are obtained within the time range of the high-load discharge segment. The average temperature of the temperature readings of each cell within the high-load discharge segment is calculated. Based on the average temperature of all cells, a comprehensive temperature dispersion value is calculated. All high-load discharge segments are aggregated to obtain a comprehensive temperature dispersion value associated with each charging process; Arrange the comprehensive temperature dispersion values ​​of each group corresponding to the most recent preset number of charging processes according to the time sequence of the charging process, and analyze the monotonicity of the comprehensive temperature dispersion values ​​of each group as the charging process sequence changes. The second health factor is determined based on the degree of monotonicity.

6. The method according to claim 5, characterized in that, Arrange the comprehensive temperature dispersion values ​​for each group corresponding to the most recent preset number of charging processes according to the chronological order of the charging process, and analyze the monotonicity of the changes in the comprehensive temperature dispersion values ​​of each group as a function of the charging process sequence, including: Sort the most recent preset number of charging processes in chronological order of when they occurred; For each of the sorted charging processes, find the comprehensive temperature dispersion value corresponding to all high-load discharge segments associated with each of the sorted charging processes, and calculate the average value of the comprehensive temperature dispersion value as the comprehensive temperature dispersion level value. The discrete temperature levels corresponding to each charging process are arranged in chronological order according to the charging process to obtain a sequence of discrete temperature levels. Compare adjacent values ​​in the comprehensive temperature discrete level value sequence one by one, and count the number of comparisons in which the latter value is greater than the former value; The number of comparisons is calculated as a percentage of the total number of adjacent comparisons in the sequence, and this percentage is used as the degree of monotonicity.

7. The method according to claim 1, characterized in that, Based on the values ​​of the first health factor and the second health factor, combined with the cumulative charging cycle count and factory rated capacity of the power battery pack, a comprehensive health status assessment report for the power battery is generated, including: The values ​​of the first health factor, the second health factor, and the cumulative number of charging cycles are used as input parameters. The input parameters are input into a predefined score mapping rule, and an initial score is obtained by querying the score mapping rule. The score mapping rule defines the output score corresponding to different combinations of the input parameters. The initial score is associated with the factory rated capacity of the power battery pack, and the initial score is adjusted and calculated based on the factory rated capacity to obtain a comprehensive battery health status score in percentage form. The overall battery health status score is compared with multiple pre-set score intervals to determine the target score interval into which the overall battery health status score falls, and the corresponding evaluation level is determined according to the pre-set level label of each target score interval. The initial score is obtained through the score mapping rule, and during the adjustment calculation based on the factory rated capacity, the input parameter that has the greatest impact on the overall score of the battery health status is identified, and the battery characteristics represented by the input parameter are determined as the key judgment basis. The evaluation level, the overall battery health status score, the cumulative number of charging cycles, the values ​​of the first health factor and the second health factor, and the key judgment criteria are combined to output a comprehensive evaluation report on the health status of the power battery.

8. A health status assessment system for used power batteries of new energy vehicles, characterized in that, include: The acquisition module is used to acquire complete charging process data of the target vehicle for a preset number of times before the used car evaluation, as well as actual driving segment data of the target vehicle during the same period of the charging behavior. The complete charging process data includes the voltage and temperature sequence of each cell in the power battery pack during each charging, and the actual driving segment data includes the average discharge rate of the power battery pack and the temperature of each cell within the corresponding driving mileage. The analysis module is used to analyze the time difference between the voltage of each cell reaching the cutoff condition during the constant voltage stage at the end of each charge based on the complete charging process data, and to determine the first health factor reflecting the consistency of the cells based on the distribution of the time difference. The extraction module is used to extract segments from the actual driving segment data where the power battery pack continuously discharges at an average discharge rate higher than a first threshold, calculate the standard deviation of the temperature of each cell in the corresponding segment, and determine a second health factor reflecting the thermal management performance under high load based on the evolution trend of the standard deviation. The generation module is used to generate a comprehensive health status assessment report for the power battery based on the values ​​of the first health factor and the second health factor, combined with the cumulative number of charging cycles and the factory rated capacity of the power battery pack.

9. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement the health status assessment method for used power batteries of new energy vehicles as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The device contains a computer program, which, when executed by a computer, implements a method for assessing the health status of a used power battery for new energy vehicles as described in any one of claims 1 to 7.

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