Battery residual value evaluation method and device, equipment and storage medium

Abstract

The invention discloses a battery residual value evaluation method and device, equipment and a storage medium. The battery residual value evaluation method comprises the steps of obtaining a plurality of pieces of first cumulative distribution data corresponding to a plurality of evaluation indexes of a to-be-evaluated battery pack; for each evaluation index, integrating the plurality of first cumulative distribution data of the evaluation index to obtain second cumulative distribution data of the evaluation index; for each evaluation index, determining a score of the evaluation index by using the second cumulative distribution data of the evaluation index; and integrating the scores of the evaluation indexes to obtain a residual value evaluation score of the to-be-evaluated battery pack. Through the above mode, the accuracy of residual value evaluation of the to-be-evaluated battery pack can be improved.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and in particular to a method, device, equipment and storage medium for evaluating the residual value of a battery. Background Art

[0002] Battery residual value refers to the remaining economic value of a battery after it has been used for a period of time, relative to its original value or when it was in a new state. With the rapid development of the new energy vehicle industry, new energy vehicles are being chosen by more and more people, and lithium batteries are widely used in the new energy vehicle industry due to their high energy density, long cycle life and high charging efficiency. As the demand for lithium batteries has increased significantly, their residual value has become more and more concerned, which is not only related to the interests of consumers, but also will affect the sustainable development of the new energy industry. Therefore, how to accurately evaluate the residual value of lithium batteries has become a technical problem that needs to be solved urgently. Summary of the invention

[0003] The main technical problem solved by the present application is to provide a battery residual value evaluation method, device, equipment and computer-readable storage medium, which can improve the accuracy of the residual value evaluation of the battery pack to be evaluated.

[0004] In order to solve the above technical problems, a technical solution adopted in the present application is: to provide a battery residual value evaluation method, the method comprising: obtaining a number of first cumulative distribution data corresponding to a number of evaluation indicators of the battery pack to be evaluated; wherein, each first cumulative distribution data of the evaluation indicator is obtained based on the evaluation indicator data within the corresponding working condition duration, and each first cumulative distribution data of the evaluation indicator includes a number of first indicator values ​​of the evaluation indicator and a first cumulative distribution probability corresponding to each first indicator value; for each evaluation indicator, the first cumulative distribution data of the evaluation indicator are integrated to obtain the second cumulative distribution data of the evaluation indicator; wherein, the second cumulative distribution data of the evaluation indicator includes a number of second indicator values ​​of the evaluation indicator and a second cumulative distribution probability corresponding to each second indicator value; for each evaluation indicator, the score of the evaluation indicator is determined using the second cumulative distribution data of the evaluation indicator; and the residual value evaluation score of the battery pack to be evaluated is obtained by integrating the scores of each evaluation indicator.

[0005] Optionally, a plurality of first cumulative distribution data of the evaluation index are integrated to obtain the second cumulative distribution data of the evaluation index, including: determining a plurality of first parameter values; for each first cumulative distribution data of the evaluation index, generating a first interpolation function corresponding to the first cumulative distribution data using the first cumulative distribution data, and determining the second parameter value of the first cumulative distribution data with respect to each first parameter value using the first interpolation function of the first cumulative distribution data; for each first parameter value, integrating each second parameter value corresponding to the first parameter value to obtain a third parameter value corresponding to the first parameter value; wherein a first parameter value is a second indicator value, and the third parameter value corresponding to the first parameter value is the second cumulative distribution probability corresponding to the corresponding second indicator value; or, a first parameter value is a second cumulative distribution probability, and the third parameter value corresponding to the first parameter value is the second indicator value corresponding to the corresponding second cumulative distribution probability.

[0006] Optionally, the third parameter value corresponding to the first parameter value is obtained by combining the second parameter values ​​corresponding to the same first parameter value, including: for each second parameter value corresponding to the first parameter value, determining the weight coefficient of the second parameter value based on the operating time of the first cumulative distribution function corresponding to the second parameter value; and weighting each second parameter value using the weight coefficient of each second parameter value to obtain the third parameter value corresponding to the first parameter value.

[0007] Optionally, obtaining a number of first cumulative distribution data corresponding to a number of evaluation indicators of the battery pack to be evaluated, includes: obtaining a number of initial cumulative distribution data corresponding to a number of evaluation indicators of the battery pack to be evaluated; wherein each initial cumulative distribution data of the evaluation indicator includes a number of initial indicator values ​​of the evaluation indicator and an initial cumulative distribution probability corresponding to each initial indicator value; for each initial cumulative distribution data of each evaluation indicator, using the initial cumulative distribution data to generate a second interpolation function corresponding to the initial cumulative distribution data, and using the second interpolation function corresponding to the initial cumulative distribution data to interpolate the initial cumulative distribution data to obtain first cumulative distribution data corresponding to the initial cumulative distribution data.

[0008] Optionally, after obtaining a number of initial cumulative distribution data corresponding to a number of evaluation indicators of the battery pack to be evaluated, and before using the initial cumulative distribution data to generate a second interpolation function corresponding to the cumulative distribution data for each initial cumulative distribution data of each evaluation indicator, the method also includes: eliminating the initial cumulative distribution data that meets preset conditions from the number of initial cumulative distribution data; wherein the preset conditions include at least one of the following: the initial cumulative distribution data has no corresponding operating condition duration, and each initial indicator value of the initial cumulative distribution data is a null value.

[0009] Optionally, the score of the evaluation indicator is determined using the second cumulative distribution data of the evaluation indicator, including: using the second cumulative distribution data of the evaluation indicator to determine the target cumulative distribution probability; using the target cumulative distribution probability to determine the target evaluation value of the evaluation indicator; and using the target evaluation value to score to obtain the score of the evaluation indicator.

[0010] Optionally, the target cumulative distribution probability is used to determine the target evaluation value of the evaluation indicator, including: if the evaluation indicator belongs to the first category of indicators, the product of the target cumulative distribution probability and the total operating time of the evaluation indicator is used as the target evaluation value; if the evaluation indicator belongs to the second category of indicators, the target indicator value of the evaluation indicator corresponding to the target cumulative distribution probability is used as the target evaluation value; and / or, the target evaluation value is used to score to obtain the score of the evaluation indicator, including: determining the target scoring interval to which the target evaluation value belongs; wherein the target scoring interval is one of several preset scoring intervals, each preset scoring interval includes a corresponding upper limit evaluation value and a lower limit evaluation value; using the upper limit evaluation value of the target scoring interval, the score corresponding to the upper limit evaluation value, the lower limit evaluation value, and the score corresponding to the lower limit evaluation value, to generate a scoring function for the target scoring interval; using the scoring function of the target scoring interval to score the target evaluation value to obtain the score of the evaluation indicator.

[0011] Optionally, the scores of each evaluation indicator are integrated to obtain the residual value evaluation score of the battery pack to be evaluated, including: determining the first weight coefficients corresponding to each evaluation indicator; and weighting the scores of each evaluation indicator using the first weight coefficients to obtain the residual value evaluation score.

[0012] Optionally, determining the first weight coefficient corresponding to each evaluation indicator includes: obtaining the second weight coefficient of each evaluation indicator; determining the weight sum corresponding to each second weight coefficient; for each evaluation indicator, using the ratio of the second weight coefficient of the evaluation indicator to the weight sum as the first weight coefficient of the evaluation indicator.

[0013] Optionally, after obtaining the second cumulative distribution data of the evaluation indicator by integrating several first cumulative distribution data of the evaluation indicator for each evaluation indicator, and before determining the score of the evaluation indicator for each evaluation indicator by using the second cumulative distribution data of the evaluation indicator, the method also includes: for each evaluation indicator, fitting the first fitting line using the second cumulative distribution data of the evaluation indicator; for each evaluation indicator, extracting third cumulative distribution data from several first cumulative distribution data of the evaluation indicator, and fitting the second fitting line using the third cumulative distribution data; wherein the third cumulative distribution data includes several third indicator values ​​of the evaluation indicator and third cumulative distribution probabilities corresponding to each third indicator value; and verifying whether the second cumulative distribution data of the evaluation indicator is valid based on the similarity between the first fitting line and the second fitting line.

[0014] Optionally, third cumulative distribution data are extracted from several first cumulative distribution data of the evaluation index, including: determining the extraction ratios corresponding to each first cumulative distribution data based on the working condition duration corresponding to each first cumulative distribution data; and extracting data that meets the corresponding extraction ratio from each first cumulative distribution data to obtain third cumulative distribution data.

[0015] In order to solve the above technical problems, another technical solution adopted in the present application is: to provide a battery residual value evaluation device, which includes: an acquisition module, used to obtain a number of first cumulative distribution data corresponding to a number of evaluation indicators of the battery pack to be evaluated; wherein, each first cumulative distribution data of the evaluation indicator is obtained by using the evaluation indicator data within the corresponding working condition duration, and each first cumulative distribution data of the evaluation indicator includes a number of first indicator values ​​of the evaluation indicator and a first cumulative distribution probability corresponding to each first indicator value; a data integration module, used to integrate the first cumulative distribution data of the evaluation indicator for each evaluation indicator to obtain the second cumulative distribution data of the evaluation indicator; wherein the second cumulative distribution data of the evaluation indicator includes a number of second indicator values ​​of the evaluation indicator and a second cumulative distribution probability corresponding to each second indicator value; a first scoring module, used to determine the score of the evaluation indicator for each evaluation indicator using the second cumulative distribution data of the evaluation indicator; a second scoring module, used to integrate the scores of each evaluation indicator to obtain the residual value evaluation score of the battery pack to be evaluated.

[0016] To solve the above technical problems, another technical solution adopted in the present application is: to provide an electronic device, comprising a memory and a processor coupled to each other, the memory storing program instructions; the processor is used to execute the program instructions stored in the memory to implement the above battery residual value evaluation method.

[0017] In order to solve the above technical problems, another technical solution adopted in the present application is: providing a computer-readable storage medium, which is used to store program instructions, and the program instructions can be executed by a processor to implement the above battery residual value evaluation method.

[0018] The above scheme obtains several first cumulative distribution data corresponding to several evaluation indicators of the battery pack to be evaluated; for each evaluation indicator, the several first cumulative distribution data of the evaluation indicator are integrated to obtain the second cumulative distribution data of the evaluation indicator; for each evaluation indicator, the score of the evaluation indicator is determined using the second cumulative distribution data of the evaluation indicator; and the residual value evaluation score of the battery pack to be evaluated is obtained by integrating the scores of each evaluation indicator. Among them, each first cumulative distribution data of the evaluation indicator is obtained based on the evaluation indicator data within the corresponding working condition duration, and each first cumulative distribution data of the evaluation indicator includes several first indicator values ​​of the evaluation indicator and the first cumulative distribution probability corresponding to each first indicator value, and the second cumulative distribution data of the evaluation indicator includes several second indicator values ​​of the evaluation indicator and the second cumulative distribution probability corresponding to each second indicator value. For each evaluation index of the battery pack to be evaluated, since each first cumulative distribution data of the evaluation index is obtained based on the evaluation index data within the corresponding working condition duration, and the second cumulative distribution data of the evaluation index is obtained by integrating each first cumulative distribution data of the evaluation index, the second cumulative distribution data of the evaluation index can more comprehensively reflect the distribution of the evaluation index within the life cycle of the battery pack to be evaluated, thereby making the score of the evaluation index obtained using the second cumulative distribution data of the evaluation index more accurate. In addition, since the residual value evaluation score of the battery pack to be evaluated is obtained by integrating the scores of each evaluation index, the residual value evaluation score of the battery pack to be evaluated can also be made more accurate, and then the residual value evaluation score of the battery pack to be evaluated can be used to accurately evaluate the residual value of the battery pack to be evaluated. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart of an embodiment of a method for evaluating residual value of a battery provided by the present application;

[0020] Figure 2 is a schematic diagram of an embodiment of a data expansion result provided by the present application;

[0021] Figure 3 It is a flow chart of an embodiment of a method for determining second cumulative distribution data of an evaluation indicator provided by the present application;

[0022] Figure 4 is a schematic diagram of a first fitting line and a second fitting line provided in the present application;

[0023] Figure 5 It is a schematic diagram of the framework of an embodiment of a battery residual value evaluation device provided by the present application;

[0024] Figure 6 It is a schematic diagram of a framework of an embodiment of an electronic device provided by the present application;

[0025] Figure 7It is a schematic diagram of a framework of an embodiment of a computer-readable storage medium provided by the present application. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and effect of the present application clearer and more specific, the present application is further described in detail below with reference to the accompanying drawings and examples.

[0027] In the following description, for the purpose of explanation rather than limitation, specific details such as specific system structures, interfaces, and technologies are provided to facilitate a thorough understanding of the present application.

[0028] The term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there may be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, "many" in this article means two or more than two. In addition, the term "at least one" in this article means any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C, it can mean including any one or more elements selected from the set consisting of A, B and C. "Several" means at least one. The terms "first", "second", etc. in the specification and claims of this article and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0029] See also Figure 1 , Figure 1 1 is a flow chart of an embodiment of a method for evaluating the residual value of a battery provided in the present application. It should be noted that if there are substantially the same results, the method of the present application is not used as Figure 1 The process sequence shown is limited. Figure 1 As shown, the method comprises the following steps:

[0030] S11: Obtain a plurality of first cumulative distribution data corresponding to a plurality of evaluation indicators of the battery pack to be evaluated.

[0031] Several evaluation indicators of the battery pack to be evaluated refer to several indicators that affect the residual value of the battery pack to be evaluated. Exemplarily, several evaluation indicators of the battery pack to be evaluated include various indicators related to battery consistency (such as voltage consistency, capacity consistency, internal resistance consistency, etc.), various indicators related to battery status (such as remaining capacity, service life, battery temperature, discharge current, etc.) and the number of battery failures, etc. Among them, the types of battery failures include zero-level failures, first-level failures, second-level failures and third-level failures. Considering that the impact of zero-level failures and first-level failures on the battery pack is relatively small and can be basically ignored, therefore, in this embodiment, the number of battery failures may only include the total number of second-level failures and the total number of third-level failures that occur during the life cycle of the battery pack to be evaluated.

[0032] Each evaluation index of the battery pack to be evaluated includes a corresponding number of first cumulative distribution data, and each first cumulative distribution data of the evaluation index is obtained based on the evaluation index data within the corresponding operating condition duration. Each first cumulative distribution data of the evaluation index includes a number of first indicator values ​​of the corresponding evaluation index and the first cumulative distribution probability corresponding to each first indicator value. Among them, the operating condition duration is the user's vehicle usage time (the time from the user starting to drive the vehicle to the end of driving), the vehicle's charging time (from the start of charging to the end of charging), etc. The first cumulative distribution probability corresponding to the first indicator value refers to the probability that the corresponding evaluation index is less than or equal to the first indicator value.

[0033] In this embodiment, a number of initial cumulative distribution data corresponding to a number of evaluation indicators of the battery pack to be evaluated are first obtained, and then a number of first cumulative distribution data corresponding to each evaluation indicator are obtained using the initial cumulative distribution data corresponding to each evaluation indicator. Each initial cumulative distribution data of the evaluation indicator is obtained based on the evaluation indicator data within the corresponding working condition duration. Each initial cumulative distribution data of the evaluation indicator includes a number of initial indicator values ​​of the evaluation indicator and the initial cumulative distribution probability corresponding to each initial indicator value.

[0034] Each time a user uses a vehicle or charges the vehicle, evaluation index data for one or more evaluation indicators within the corresponding operating time will be generated, such as discharge current data for a discharge current indicator. The evaluation index data for the evaluation indicators within the operating time can be processed to obtain initial cumulative distribution data for the evaluation indicators corresponding to the operating time. Specifically, the evaluation index data for the evaluation indicators within the operating time are divided into quantiles to obtain several initial index values ​​for the evaluation indicators. 1 ,a 2 ,…,a n-1 ,a n ]; Based on the evaluation index data of the evaluation index during the working condition, the probability that the evaluation index is less than or equal to each initial index value is calculated respectively, that is, the initial cumulative distribution probability corresponding to each initial index value is calculated [CDF 1 ,CDF 2 ,…,CDF n-1 ,CDF n ]; several initial indicator values ​​obtained based on the evaluation indicator data about the evaluation indicator within the working condition duration and the initial cumulative distribution probabilities corresponding to each initial indicator value are used as an initial cumulative distribution data corresponding to the evaluation indicator.

[0035] In one embodiment, each vehicle will upload the evaluation index data and corresponding operating time generated each time the vehicle is used or charged to the power battery big data platform. The power battery big data platform will pre-process the evaluation index data of different operating time uploaded by each vehicle (such as only filtering the valid operating data), and process to obtain a number of initial cumulative distribution data corresponding to a number of evaluation indicators of the battery pack of each vehicle. When the residual value of the battery pack to be evaluated is evaluated, the initial cumulative distribution data corresponding to the evaluation indicators of the battery pack to be evaluated and the operating time corresponding to each initial cumulative distribution data can be obtained from the power battery big data platform.

[0036] Furthermore, in order to further improve the accuracy of the scores of the evaluation indicators determined subsequently, after obtaining a number of initial cumulative distribution data corresponding to each evaluation indicator of the battery pack to be evaluated, it is necessary to pre-process the initial cumulative distribution data corresponding to each evaluation indicator to eliminate invalid data in the initial cumulative distribution data of each evaluation indicator. Specifically, the initial cumulative distribution data that meets the preset conditions are eliminated from the initial cumulative distribution data. Among them, the preset conditions include at least one of the following: the initial cumulative distribution data has no corresponding working condition duration, and the initial indicator values ​​of the initial cumulative distribution data are null values.

[0037] In actual scenarios, the vehicle may not have uploaded the operating duration to the power battery big data platform, or the power battery big data platform may have sent the operating duration but did not receive the operating duration data, etc., resulting in the inability to obtain the operating duration corresponding to the initial cumulative distribution data. The initial cumulative distribution data without corresponding operating duration can be removed as invalid data. In actual scenarios, because the operating duration is too short, it is impossible to perform quantile segmentation on the relatively small number of evaluation index data within the operating duration, so there will be a situation where the initial index values ​​of the initial cumulative distribution data are all null values ​​(NaN). The initial cumulative distribution data with null initial index values ​​can be deleted as invalid data.

[0038] Furthermore, after obtaining several initial cumulative distribution data corresponding to several evaluation indicators of the battery pack to be evaluated and the operating duration corresponding to each initial cumulative distribution data, the several initial cumulative distribution data of each evaluation indicator can be stored in a list, and the operating duration of each initial cumulative distribution data can be stored in another list. The storage position of the operating duration of each initial cumulative distribution data corresponds one-to-one to the storage position of the corresponding initial cumulative distribution data.

[0039] Furthermore, in order to further improve the accuracy of the scores of the evaluation indicators determined subsequently, after obtaining a number of initial cumulative distribution data corresponding to each evaluation indicator of the battery pack to be evaluated, the rationality of the maximum initial indicator value of the corresponding evaluation indicator in each initial cumulative distribution data can also be judged. If the maximum initial indicator value exceeds the maximum threshold of the corresponding evaluation indicator, it is considered that the maximum initial indicator value is unreasonable and there is a jump abnormality, and it is set as the maximum threshold of the corresponding evaluation indicator. The maximum threshold of each evaluation indicator can be set according to the actual distribution of each evaluation indicator.

[0040] In one embodiment, after obtaining a number of initial cumulative distribution data corresponding to each evaluation index of the battery pack to be evaluated, the number of initial cumulative distribution data corresponding to each evaluation index is directly used as a number of first cumulative distribution data corresponding to the number of evaluation indexes of the battery pack to be evaluated. In this embodiment, the number of initial cumulative distribution data corresponding to each evaluation index is not processed, but directly used as a number of first cumulative distribution data corresponding to each evaluation index.

[0041] In another embodiment, considering that the amount of data in the initial cumulative distribution data of each evaluation index may be small, after obtaining a number of initial cumulative distribution data corresponding to each evaluation index of the battery pack to be evaluated, the number of initial cumulative distribution data corresponding to each evaluation index is respectively expanded to obtain a number of first cumulative distribution data corresponding to each evaluation index. The number of initial index values ​​in the initial cumulative distribution data is less than the number of first index values ​​in the corresponding first cumulative distribution data.

[0042] In this implementation, for each initial cumulative distribution data of each evaluation index, a second interpolation function corresponding to the initial cumulative distribution data is generated using the initial cumulative distribution data, and the second interpolation function corresponding to the initial cumulative distribution data is used to interpolate the initial cumulative distribution data to obtain first cumulative distribution data corresponding to the initial cumulative distribution data.

[0043] Specifically, a fitting (eg, linear fitting) is performed using a number of initial indicator values ​​included in the initial cumulative distribution data and initial cumulative distribution probabilities corresponding to each initial indicator value to obtain a second interpolation function corresponding to the initial cumulative distribution data.

[0044] Further, after generating the second interpolation function corresponding to the initial cumulative distribution data, data expansion processing may be performed on the initial cumulative distribution data by using any one of the following two interpolation schemes to obtain first cumulative distribution data corresponding to the initial cumulative distribution data.

[0045] In the first scheme, a number of first interpolation cumulative distribution probabilities are selected within the first cumulative distribution probability value range, and the first interpolation index value corresponding to each first interpolation cumulative distribution probability is calculated using the second interpolation function corresponding to the initial cumulative distribution data to achieve data expansion of the initial cumulative distribution data. Exemplarily, the first cumulative distribution probability value range is 0 to 1, and the number of selected first interpolation cumulative distribution probabilities is set according to actual data expansion requirements.

[0046] In the second scheme, several first interpolation index values ​​are selected within the first index value range, and the first interpolation cumulative distribution probability corresponding to each first interpolation index value is calculated using the second interpolation function corresponding to the initial cumulative distribution data, so as to achieve data expansion of the initial cumulative distribution data. Exemplarily, the first index value range is determined based on the evaluation index data within the working condition duration corresponding to the initial cumulative distribution data, the upper limit value of the first index value range is the maximum value of the evaluation index data within the working condition duration corresponding to the initial cumulative distribution data, and the lower limit value of the first index value range is the minimum value of the evaluation index data within the working condition duration corresponding to the initial cumulative distribution data. The number of selected first interpolation index values ​​is set according to the actual data expansion requirements.

[0047] Taking into account the possibility that the initial indicator values ​​in the initial cumulative distribution data are the same (for example, the battery pack is neither charged nor discharged when the vehicle stops, and the discharge current data remains unchanged), in this special case, if the first solution is used for data expansion, it may not be possible to obtain data on the relevant evaluation indicators over the entire life cycle of the battery pack to be evaluated. Figure 2 Schematic diagram of an embodiment of the data expansion result provided by this application. Figure 2 As shown, the x-axis of the coordinate system represents the index value of the evaluation index, and the y-axis represents the cumulative distribution probability. Assume that a certain evaluation index includes three first cumulative distribution data, which are respectively represented as data A, data B and data C. Data A, data B and data C are all obtained by expanding the corresponding initial cumulative distribution data using the first scheme mentioned above. Line 1 is the fitting result of data A, line 2 is the fitting result of data B, and line 3 is the fitting result of data C. Line 4 is the fitting result of the comprehensive data of data A, data B and data C. Since the initial index values ​​in the initial cumulative distribution data corresponding to data A are all 0, line 1 obtained after data expansion using the first scheme is a straight line with an index value of 0. Therefore, using the first scheme for data expansion will bring inevitable errors. In order to further improve the accuracy of the scores of the evaluation indicators determined subsequently, the second scheme can be used for data expansion in this embodiment.

[0048] Exemplarily, the data expansion processing of the first solution and the second solution can be implemented by calling the interpolate.interp1d function of Python.

[0049] Furthermore, after data expansion processing is performed on the initial cumulative distribution data, the first interpolation index values ​​and the initial index values ​​in the initial cumulative distribution data may be used as the first index values ​​of the corresponding first cumulative distribution data; and the first interpolation cumulative distribution probabilities and the initial cumulative distribution probabilities in the initial cumulative distribution data may be used as the first cumulative distribution probabilities of the corresponding first cumulative distribution data. Alternatively, only the first interpolation index values ​​may be used as the first index values ​​of the corresponding first cumulative distribution data; and only the first interpolation cumulative distribution probabilities may be used as the first cumulative distribution probabilities of the corresponding first cumulative distribution data.

[0050] S12: For each evaluation indicator, a plurality of first cumulative distribution data of the evaluation indicator are integrated to obtain second cumulative distribution data of the evaluation indicator.

[0051] The second cumulative distribution data of the evaluation index includes a plurality of second index values ​​of the evaluation index and second cumulative distribution probabilities corresponding to each second index value.

[0052] See also Figure 3 , Figure 3 1 is a flow chart of an embodiment of a method for determining the second cumulative distribution data of an evaluation indicator provided by the present application. Figure 3 As shown, the method comprises the following steps:

[0053] S301: Determine a number of first parameter values.

[0054] In one embodiment, a plurality of second interpolation index values ​​are selected as a plurality of first parameter values ​​within the second index value range of the evaluation index. Exemplarily, the upper limit value of the second index value range of the evaluation index is the maximum value of the evaluation index data of the evaluation index in all working condition durations, and the lower limit value of the second index value range of the evaluation index is the minimum value of the evaluation index data of the evaluation index in all working condition durations.

[0055] In another embodiment, a plurality of second interpolated cumulative distribution probabilities are selected within the second cumulative distribution probability value range as a plurality of first parameter values. Exemplarily, the second cumulative distribution probability value range is 0-1.

[0056] The number of first parameter values ​​can be set according to actual needs.

[0057] S302: For each first cumulative distribution data of the evaluation index, generate a first interpolation function corresponding to the first cumulative distribution data using the first cumulative distribution data, and determine a second parameter value of the first cumulative distribution data with respect to each first parameter value using the first interpolation function of the first cumulative distribution data.

[0058] Specifically, for each first cumulative distribution data of the evaluation index, fitting (for example, linear fitting) is performed using several first indicator values ​​included in the first cumulative distribution data and the first cumulative distribution probabilities corresponding to each first indicator value to obtain a first interpolation function corresponding to the first cumulative distribution data. Further, each first parameter value is substituted into the first interpolation function corresponding to the first cumulative distribution data to obtain the second parameter value of the first cumulative distribution data with respect to each first parameter value.

[0059] Exemplarily, step S301 to step S302 may be implemented by calling the interpolate.interp1d function of Python.

[0060] S303: For each first parameter value, synthesize each second parameter value of different first cumulative distribution data corresponding to the first parameter value to obtain a third parameter value corresponding to the first parameter value.

[0061] In one embodiment, the step of synthesizing the second parameter values ​​of different first cumulative distribution data corresponding to the first parameter value to obtain the third parameter value corresponding to the first parameter value includes: for each second parameter value corresponding to the first parameter value, based on the operating time of the first cumulative distribution function corresponding to the second parameter value, determining the weight coefficient of the second parameter value; and weighting each second parameter value using the weight coefficient of each second parameter value to obtain the third parameter value corresponding to the first parameter value. The longer the operating time, the larger the corresponding weight coefficient; the shorter the operating time, the smaller the corresponding weight coefficient.

[0062] In one example, the first parameter values ​​obtained in step S301 are the aforementioned second interpolation index values. For each second interpolation index value, the different second interpolation cumulative distribution probabilities corresponding to the second interpolation index value can be combined to obtain the third parameter value corresponding to the second interpolation index value. At this time, the third parameter value corresponding to the second interpolation index value is the comprehensive value of the different second interpolation cumulative distribution probabilities corresponding to the second interpolation index value.

[0063] Assume that a certain evaluation index includes two second cumulative distribution data, represented as data A' and data B'. Select several second interpolation index values ​​[b 1 ,b 2 ,…,b m-1 ,b m ], the second interpolation cumulative distribution probability of the calculated data A' corresponding to each second interpolation index value is [CDF 1 ,CDF 2 ,…,CDF m-1 ,CDF m], and the second interpolation cumulative distribution probability corresponding to each second interpolation index value of the calculated data B' is [CDF′ 1 ,CDF′ 2 ,…,CDF′ m-1 ,CDF′ m ], then after synthesizing the different second interpolation cumulative distribution probabilities corresponding to each second interpolation index value, we can get [CDF″ 1 ,CDF″ 2 ,…,CDF″ m-1 ,CDF″ m ]. Among them, CDF″ m CDF m and CDF′ m The comprehensive value of .

[0064] Furthermore, in this example, a first parameter value is used as a second index value in the second cumulative distribution data, and a third parameter value corresponding to the first parameter value is used as a second cumulative distribution probability corresponding to a corresponding second index value in the second cumulative distribution data.

[0065] In another example, the first parameter values ​​obtained in step S301 are the aforementioned second interpolation cumulative distribution probabilities. For each second interpolation cumulative distribution probability, different second interpolation index values ​​corresponding to the second interpolation cumulative distribution probability can be combined to obtain a third parameter value corresponding to the second interpolation cumulative distribution probability. In this case, the third parameter value corresponding to the second interpolation cumulative distribution probability is a comprehensive value of different second interpolation index values ​​corresponding to the second interpolation cumulative distribution probability.

[0066] Furthermore, in this example, a first parameter value is used as a second cumulative distribution probability in the second cumulative distribution data, and a third parameter value corresponding to the first parameter value is used as a second indicator value corresponding to the corresponding second cumulative distribution probability in the second cumulative distribution data.

[0067] The second cumulative distribution data of each evaluation indicator obtained through steps S301 to S303 can more comprehensively and intuitively reflect the distribution of the evaluation indicators within the life cycle of the battery pack to be evaluated.

[0068] Optionally, in order to improve the reliability of the subsequent determination of the scores of each evaluation indicator, so as to further improve the reliability of the residual value evaluation score of the battery pack to be evaluated, after obtaining the second cumulative distribution data of each evaluation indicator, the validity of the second cumulative distribution data of each evaluation indicator can also be verified separately by the Monte Carlo algorithm.

[0069] For each evaluation indicator, verifying the validity of the second cumulative distribution data of the evaluation indicator may include the following steps:

[0070] Step 1: Use the second cumulative distribution data of the evaluation index to fit and obtain a first fitting line.

[0071] Step 2: extracting third cumulative distribution data from the plurality of first cumulative distribution data of the evaluation index, and fitting the third cumulative distribution data of the evaluation index to obtain a second fitting line.

[0072] The third cumulative distribution data of the evaluation index includes a plurality of third index values ​​of the evaluation index and third cumulative distribution probabilities corresponding to each third index value.

[0073] The step of extracting the third cumulative distribution data from the first cumulative distribution data of the evaluation index includes: determining the extraction ratios corresponding to the first cumulative distribution data of the evaluation index based on the working condition durations corresponding to the first cumulative distribution data of the evaluation index; extracting data that meet the corresponding extraction ratios from the first cumulative distribution data of the evaluation index to obtain the third cumulative distribution data of the evaluation index. The longer the working condition duration, the greater the corresponding extraction ratio; the shorter the working condition duration, the smaller the corresponding extraction ratio.

[0074] Step three: based on the similarity between the first fitting line and the second fitting line, verify whether the second cumulative distribution data of the evaluation index is valid.

[0075] In one embodiment, the first fitting line and the second fitting line are located in the same two-dimensional coordinate system, and the x-axis of the two-dimensional coordinate system represents the index value, and the y-axis represents the cumulative distribution probability. A plurality of first points on the first fitting line are selected, and second points corresponding to the first points are selected on the second fitting line. The x-coordinate of each first point is the same as the x-coordinate of the second point corresponding to each first point, or the y-coordinate of each first point is the same as the y-coordinate of the second point corresponding to each first point.

[0076] In the case where the x-coordinate of each first point is the same as the x-coordinate of the second point corresponding to each first point, for each first point, based on the absolute value of the first difference between the y-coordinate of the first point and the y-coordinate of the second point corresponding to the first point, it is determined whether the first point is an abnormal point. Specifically, if the absolute value of the first difference is greater than or equal to the first difference threshold, the first point is determined to be an abnormal point; if the absolute value of the first difference is less than the first difference threshold, the first point is determined not to be an abnormal point. The first difference threshold can be set according to actual needs.

[0077] In the case where the y coordinate of each first point is the same as the y coordinate of the second point corresponding to each first point, for each first point, based on the second difference absolute value between the x coordinate of the first point and the x coordinate of the second point corresponding to the first point, determine whether the first point is an abnormal point. Specifically, if the second difference absolute value is greater than or equal to the second difference threshold, the first point is determined to be an abnormal point; if the second difference absolute value is less than the second difference threshold, the first point is determined not to be an abnormal point. The second difference threshold can be set according to actual needs.

[0078] Furthermore, the proportion of abnormal points in the plurality of first points is counted. Specifically, the number of abnormal points in the plurality of first points is unified, and the ratio of the number of abnormal points to the total number of first points is determined as the proportion of abnormal points.

[0079] If the proportion of abnormal points is less than the preset proportion threshold, the first fitting line is considered to be similar to the second fitting line, that is, the second cumulative distribution data of the evaluation index is verified to be valid. At this time, it can be considered that the method of obtaining the second cumulative distribution data of the evaluation index in steps S301 to S303 is effective and reasonable.

[0080] If the proportion of abnormal points is greater than or equal to the preset proportion threshold, it is considered that the first fitting line is not similar to the second fitting line, that is, the second cumulative distribution data of the evaluation index is invalid. At this time, it may be that the generated first interpolation function is unreasonable. The interpolation type can be adjusted (for example, linear interpolation can be adjusted to other types of interpolation) and the second cumulative distribution data of the evaluation index can be obtained again.

[0081] Figure 4 is a schematic diagram of the first fitting line and the second fitting line provided in this application. Figure 4 As shown, the first fitting line and the second fitting line fit well, indicating that the method of obtaining the second cumulative distribution data of the evaluation index in step S301 to step S303 is effective and reasonable.

[0082] S13: For each evaluation indicator, using the second cumulative distribution data of the evaluation indicator, determine the score of the evaluation indicator.

[0083] In step S13, determining the score of the evaluation indicator by using the second cumulative distribution data of the evaluation indicator may include the following steps:

[0084] Step 1: Determine the target cumulative distribution probability using the second cumulative distribution data of the evaluation index.

[0085] The target cumulative distribution probability is determined based on big data analysis. For example, several initial cumulative distribution data corresponding to several evaluation indicators of all battery packs on the market and the operating time corresponding to the distribution of each initial cumulative distribution data are obtained from the power battery big data platform, and the second cumulative distribution data of each battery pack regarding each evaluation indicator is obtained using the method recorded in this embodiment. The second cumulative distribution data of all battery packs regarding the same evaluation indicator are integrated using the method recorded in this embodiment to obtain a piece of comprehensive cumulative distribution data of all battery packs regarding the same evaluation indicator, and different evaluation indicators correspond to one piece of comprehensive cumulative distribution data. The comprehensive cumulative distribution data of the evaluation indicator includes several fourth indicator values ​​and fourth cumulative distribution probabilities corresponding to each fourth indicator value. By analyzing the comprehensive cumulative distribution data of the evaluation indicator, the target cumulative distribution probability that needs to be paid attention to can be obtained, and then the target cumulative distribution probability and the target indicator value corresponding to the target cumulative distribution probability are found from the second cumulative distribution data of the evaluation indicator.

[0086] Step 2: Use the target cumulative distribution probability to determine the target evaluation value of the evaluation indicator.

[0087] If the evaluation index belongs to the first category, the product of the target cumulative distribution probability and the total working time of the evaluation index is used as the target evaluation value of the evaluation index. If the evaluation index belongs to the second category, the target indicator value of the evaluation index corresponding to the target cumulative distribution probability is used as the target evaluation value.

[0088] Exemplarily, the first category of indicators includes current-related evaluation indicators and temperature-related evaluation indicators, and the second category of indicators includes other evaluation indicators except the current-related indicators and the temperature-related indicators.

[0089] Step three: Use the target evaluation value to score and get the score of the evaluation indicator.

[0090] In one embodiment, the target evaluation value is scored by piecewise linear interpolation. This embodiment may include the following sub-steps:

[0091] Sub-step one: determine the target scoring interval to which the target evaluation value belongs.

[0092] The target scoring interval is one of several preset scoring intervals, and each preset scoring interval includes a corresponding upper limit evaluation value and a lower limit evaluation value.

[0093] Sub-step two, using the upper limit evaluation value of the target scoring interval, the score corresponding to the upper limit evaluation value, the lower limit evaluation value and the score corresponding to the lower limit evaluation value, to generate a scoring function for the target scoring interval.

[0094] Sub-step three, using the scoring function of the target scoring interval to score the target evaluation value to obtain the score of the evaluation indicator.

[0095] Specifically, the target evaluation value is substituted into the scoring function of the target scoring interval to obtain the score of the evaluation index.

[0096] S14: The scores of various evaluation indicators are combined to obtain a residual value evaluation score of the battery pack to be evaluated.

[0097] Step S14 includes: determining first weight coefficients corresponding to the respective evaluation indicators; and weighting the scores of the respective evaluation indicators using the first weight coefficients to obtain a residual value evaluation score of the battery pack to be evaluated.

[0098] The size of the first weight coefficient of the evaluation index reflects the degree of influence of the evaluation index on the residual value evaluation. The larger the first weight coefficient of the evaluation index, the higher the degree of influence on the residual value evaluation; the smaller the first weight coefficient of the evaluation index, the lower the degree of influence on the residual value evaluation. The first weight coefficients of different evaluation indicators can be the same or different.

[0099] Combined with big data analysis, the preset weight coefficients corresponding to all evaluation indicators related to the battery residual value can be obtained.

[0100] In one implementation, the first weight coefficient of each evaluation indicator involved in the residual value evaluation of the battery pack to be evaluated is directly assigned to a corresponding preset weight coefficient.

[0101] In another embodiment, considering that in actual scenarios, due to technical problems (for example, the battery packs of certain models cannot obtain data of certain evaluation indicators), historical data loss and other reasons, it may be impossible to obtain data on all evaluation indicators of the battery pack to be evaluated, that is, the battery pack to be evaluated may lack data of certain evaluation indicators. In order to further improve the applicability and rationality of the residual value evaluation of the battery pack to be evaluated, the weight coefficients of the evaluation indicators of the battery pack to be evaluated can be adjusted so that the sum of the weight coefficients of the evaluation indicators of the battery pack to be evaluated is a preset value. The preset value is 1.

[0102] Sub-step 1: Obtain the second weight coefficient of each evaluation indicator.

[0103] The second weight coefficient of the evaluation indicator is the preset weight coefficient corresponding to the evaluation indicator obtained in combination with big data analysis.

[0104] Sub-step 2: determining the sum of weights corresponding to each second weight coefficient.

[0105] Sub-step three: for each evaluation indicator, the ratio of the second weight coefficient of the evaluation indicator to the weight sum is used as the first weight coefficient of the evaluation indicator.

[0106] Furthermore, after determining the first weight coefficients corresponding to the respective evaluation indicators, the residual value evaluation score of the battery pack to be evaluated is determined by the following formula:

[0107]

[0108] Where n represents the total number of evaluation indicators of the battery pack to be evaluated, s i represents the score of the i-th evaluation indicator, w i Represents the first weight coefficient of the i-th evaluation indicator.

[0109] In this embodiment, a number of first cumulative distribution data corresponding to a number of evaluation indicators of the battery pack to be evaluated are obtained; for each evaluation indicator, the first cumulative distribution data of the evaluation indicator are integrated to obtain the second cumulative distribution data of the evaluation indicator; for each evaluation indicator, the score of the evaluation indicator is determined using the second cumulative distribution data of the evaluation indicator; the residual value evaluation score of the battery pack to be evaluated is obtained by integrating the scores of each evaluation indicator. Among them, each first cumulative distribution data of the evaluation indicator is obtained based on the evaluation indicator data within the corresponding working condition duration, and each first cumulative distribution data of the evaluation indicator includes a number of first indicator values ​​of the evaluation indicator and the first cumulative distribution probability corresponding to each first indicator value, and the second cumulative distribution data of the evaluation indicator includes a number of second indicator values ​​of the evaluation indicator and the second cumulative distribution probability corresponding to each second indicator value. For each evaluation index of the battery pack to be evaluated, since each first cumulative distribution data of the evaluation index is obtained based on the evaluation index data within the corresponding working condition duration, and the second cumulative distribution data of the evaluation index is obtained by integrating each first cumulative distribution data of the evaluation index, the second cumulative distribution data of the evaluation index can more comprehensively reflect the distribution of the evaluation index within the life cycle of the battery pack to be evaluated, thereby making the score of the evaluation index obtained using the second cumulative distribution data of the evaluation index more accurate. In addition, since the residual value evaluation score of the battery pack to be evaluated is obtained by integrating the scores of each evaluation index, the residual value evaluation score of the battery pack to be evaluated can also be made more accurate, and then the residual value evaluation score of the battery pack to be evaluated can be used to accurately evaluate the residual value of the battery pack to be evaluated.

[0110] At present, there are mainly two ways to determine the residual value of the battery pack to be evaluated. In the first way, the residual value of the battery pack to be evaluated is detected by an external detector or a cyberphysical system. The first method requires additional equipment, which is not realistic for large-scale testing and is more suitable for laboratory testing. In the second way, the characteristic data related to the battery pack to be evaluated is obtained, and the residual value of the battery pack to be evaluated is predicted using a machine learning model. The second method has high requirements for the selection and debugging of the machine learning model, and the accuracy of the machine learning model will directly affect the accuracy of the results.

[0111] In this embodiment, the use of aggregated CDF technology for analysis based on the overall data can construct a battery residual value assessment model from a macro perspective, better reveal the global change trends and characteristics of each evaluation indicator of the battery pack to be evaluated, and enhance the credibility of the residual value assessment. At the same time, the aggregated CDF can standardize the originally scattered data and convert it into a cumulative probability distribution between 0 and 1. This conversion helps to eliminate the impact of data noise and outliers. In addition, starting from all the battery pack data in the actual market, the use of big data analysis technology to design a market-oriented scoring standard can effectively avoid the errors and scenario limitations that may be caused by the model.

[0112] See also Figure 5 , Figure 5 1 is a schematic diagram of a battery residual value evaluation device according to an embodiment of the present application. In this embodiment, the battery residual value evaluation device 50 includes: an acquisition module 51 , a data integration module 52 , a first scoring module 53 and a second scoring module 54 .

[0113] Among them, the acquisition module 51 is used to obtain several first cumulative distribution data corresponding to several evaluation indicators of the battery pack to be evaluated; wherein, each first cumulative distribution data of the evaluation indicator is obtained by using the evaluation indicator data within the corresponding working condition duration, and each first cumulative distribution data of the evaluation indicator includes several first indicator values ​​of the evaluation indicator and the first cumulative distribution probability corresponding to each first indicator value. The data integration module 52 is used to integrate the several first cumulative distribution data of the evaluation indicator for each evaluation indicator to obtain the second cumulative distribution data of the evaluation indicator; wherein the second cumulative distribution data of the evaluation indicator includes several second indicator values ​​of the evaluation indicator and the second cumulative distribution probability corresponding to each second indicator value. The first scoring module 53 is used to determine the score of the evaluation indicator for each evaluation indicator using the second cumulative distribution data of the evaluation indicator. The second scoring module 54 is used to integrate the scores of each evaluation indicator to obtain the residual value evaluation score of the battery pack to be evaluated.

[0114] Optionally, the data integration module 52 is used to determine several first parameter values; for each first cumulative distribution data of the evaluation index, the first cumulative distribution data is used to generate a first interpolation function corresponding to the first cumulative distribution data, and the first interpolation function of the first cumulative distribution data is used to determine the second parameter value of the first cumulative distribution data with respect to each first parameter value; for each first parameter value, the second parameter values ​​corresponding to the first parameter value are integrated to obtain a third parameter value corresponding to the first parameter value; wherein a first parameter value is a second indicator value, and the third parameter value corresponding to the first parameter value is the second cumulative distribution probability corresponding to the corresponding second indicator value; or, a first parameter value is a second cumulative distribution probability, and the third parameter value corresponding to the first parameter value is the second indicator value corresponding to the corresponding second cumulative distribution probability.

[0115] Optionally, the data integration module 52 is used to determine the weight coefficient of the second parameter value for each second parameter value corresponding to the first parameter value based on the operating condition duration of the first cumulative distribution function corresponding to the second parameter value; and to weight each second parameter value using the weight coefficient of each second parameter value to obtain a third parameter value corresponding to the first parameter value.

[0116] Optionally, the acquisition module 51 is used to obtain a number of initial cumulative distribution data corresponding to a number of evaluation indicators of the battery pack to be evaluated; wherein each initial cumulative distribution data of the evaluation indicator includes a number of initial indicator values ​​of the evaluation indicator and an initial cumulative distribution probability corresponding to each initial indicator value; for each initial cumulative distribution data of each evaluation indicator, the initial cumulative distribution data is used to generate a second interpolation function corresponding to the initial cumulative distribution data, and the initial cumulative distribution data is interpolated using the second interpolation function corresponding to the initial cumulative distribution data to obtain first cumulative distribution data corresponding to the initial cumulative distribution data.

[0117] Optionally, the acquisition module 51 is also used to eliminate initial cumulative distribution data that meets preset conditions from a number of initial cumulative distribution data; wherein the preset conditions include at least one of the following: the initial cumulative distribution data has no corresponding operating condition duration, and each initial indicator value of the initial cumulative distribution data is a null value.

[0118] Optionally, the first scoring module 53 is used to determine the target cumulative distribution probability using the second cumulative distribution data of the evaluation indicator; determine the target evaluation value of the evaluation indicator using the target cumulative distribution probability; and score using the target evaluation value to obtain the score of the evaluation indicator.

[0119] Optionally, the first scoring module 53 is used to use the target cumulative distribution probability to determine the target evaluation value of the evaluation indicator, including: if the evaluation indicator belongs to the first category of indicators, then the product of the target cumulative distribution probability and the total operating time of the evaluation indicator is used as the target evaluation value; if the evaluation indicator belongs to the second category of indicators, then the target indicator value of the evaluation indicator corresponding to the target cumulative distribution probability is used as the target evaluation value; and / or, using the target evaluation value to score to obtain the score of the evaluation indicator, including: determining the target scoring interval to which the target evaluation value belongs; wherein the target scoring interval is one of several preset scoring intervals, each preset scoring interval includes a corresponding upper limit evaluation value and a lower limit evaluation value; using the upper limit evaluation value of the target scoring interval, the score corresponding to the upper limit evaluation value, the lower limit evaluation value, and the score corresponding to the lower limit evaluation value to generate a scoring function for the target scoring interval; using the scoring function of the target scoring interval to score the target evaluation value to obtain the score of the evaluation indicator.

[0120] Optionally, the second scoring module 54 is used to determine first weight coefficients corresponding to respective evaluation indicators; and weight the scores of respective evaluation indicators using the first weight coefficients to obtain a residual value evaluation score.

[0121] Optionally, the second scoring module 54 is used to obtain a second weight coefficient for each evaluation indicator; determine the weight sum corresponding to each second weight coefficient; and for each evaluation indicator, use the ratio of the second weight coefficient of the evaluation indicator to the weight sum as the first weight coefficient of the evaluation indicator.

[0122] Optionally, the battery residual value evaluation device 50 further includes a verification module 55. The verification module 55 is used to obtain a first fitting line by fitting the second cumulative distribution data of the evaluation indicator for each evaluation indicator; for each evaluation indicator, extract third cumulative distribution data from a plurality of first cumulative distribution data of the evaluation indicator, and obtain a second fitting line by fitting the third cumulative distribution data; wherein the third cumulative distribution data includes a plurality of third indicator values ​​of the evaluation indicator and a third cumulative distribution probability corresponding to each third indicator value; and verify whether the second cumulative distribution data of the evaluation indicator is valid based on the similarity between the first fitting line and the second fitting line.

[0123] Optionally, the verification module 55 is used to determine the extraction ratios corresponding to each first cumulative distribution data based on the operating condition duration corresponding to each first cumulative distribution data; and extract data that meets the corresponding extraction ratio from each first cumulative distribution data to obtain third cumulative distribution data.

[0124] It should be noted that the device of this embodiment can execute the steps in the above method. For detailed description of the relevant content, please refer to the above method part, which will not be repeated here.

[0125] See also Figure 6 , Figure 6 6 is a schematic diagram of a framework of an electronic device according to an embodiment of the present application. In this embodiment, the electronic device 60 includes a memory 61 and a processor 62 .

[0126] The processor 62 may also be referred to as a CPU (Central Processing Unit). The processor 62 may be an integrated circuit chip having signal processing capabilities. The processor 62 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor may be a microprocessor or the processor 62 may also be any conventional processor 62, etc.

[0127] The memory 61 in the electronic device 60 is used to store program instructions required for the processor 62 to run.

[0128] The processor 62 is used to execute program instructions to implement the battery residual value evaluation method in this application.

[0129] See also Figure 7 , Figure 7 It is a schematic diagram of the framework of an embodiment of a computer-readable storage medium provided in the present application. The computer-readable storage medium 70 of the embodiment of the present application stores a program instruction 71, and when the program instruction 71 is executed, the battery residual value evaluation method provided in the present application is implemented. Among them, the program instruction 71 can form a program file and be stored in the above-mentioned computer-readable storage medium 70 in the form of a software product, so that a computer device (which can be a personal computer, a server, or a network device, etc.) executes all or part of the steps of each implementation method of the present application. The aforementioned computer-readable storage medium 70 includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a disk or an optical disk, or a terminal device such as a computer, a server, a mobile phone, and a tablet.

[0130] The above scheme obtains several first cumulative distribution data corresponding to several evaluation indicators of the battery pack to be evaluated; for each evaluation indicator, the several first cumulative distribution data of the evaluation indicator are integrated to obtain the second cumulative distribution data of the evaluation indicator; for each evaluation indicator, the score of the evaluation indicator is determined using the second cumulative distribution data of the evaluation indicator; and the residual value evaluation score of the battery pack to be evaluated is obtained by integrating the scores of each evaluation indicator. Among them, each first cumulative distribution data of the evaluation indicator is obtained based on the evaluation indicator data within the corresponding working condition duration, and each first cumulative distribution data of the evaluation indicator includes several first indicator values ​​of the evaluation indicator and the first cumulative distribution probability corresponding to each first indicator value, and the second cumulative distribution data of the evaluation indicator includes several second indicator values ​​of the evaluation indicator and the second cumulative distribution probability corresponding to each second indicator value. For each evaluation index of the battery pack to be evaluated, since each first cumulative distribution data of the evaluation index is obtained based on the evaluation index data within the corresponding working condition duration, and the second cumulative distribution data of the evaluation index is obtained by integrating each first cumulative distribution data of the evaluation index, the second cumulative distribution data of the evaluation index can more comprehensively reflect the distribution of the evaluation index within the life cycle of the battery pack to be evaluated, thereby making the score of the evaluation index obtained using the second cumulative distribution data of the evaluation index more accurate. In addition, since the residual value evaluation score of the battery pack to be evaluated is obtained by integrating the scores of each evaluation index, the residual value evaluation score of the battery pack to be evaluated can also be made more accurate, and then the residual value evaluation score of the battery pack to be evaluated can be used to accurately evaluate the residual value of the battery pack to be evaluated.

[0131] In some embodiments, the functions or modules included in the device provided by the embodiments of the present disclosure can be used to execute the method described in the above method embodiments. The specific implementation can refer to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0132] The above description of various embodiments tends to emphasize the differences between the various embodiments. The same or similar aspects can be referenced to each other, and for the sake of brevity, they will not be repeated herein.

[0133] In the several embodiments provided in the present application, it should be understood that the disclosed methods, devices and systems can be implemented in other ways. For example, the device implementation described above is only schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0134] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0135] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0136] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. With this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) or a processor to perform all or part of the steps of each implementation method of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program code.

[0137] The above description is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly used in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for evaluating the residual value of a battery, characterized in that: The method comprises: Acquire a plurality of first cumulative distribution data corresponding to a plurality of evaluation indicators of the battery pack to be evaluated; wherein each of the first cumulative distribution data of the evaluation indicators is obtained based on the evaluation indicator data within the corresponding working condition duration, and each of the first cumulative distribution data of the evaluation indicators includes a plurality of first indicator values ​​of the evaluation indicators and a first cumulative distribution probability corresponding to each of the first indicator values; For each of the evaluation indicators, the plurality of first cumulative distribution data of the evaluation indicator are integrated to obtain second cumulative distribution data of the evaluation indicator; wherein the second cumulative distribution data of the evaluation indicator includes a plurality of second indicator values ​​of the evaluation indicator and second cumulative distribution probabilities corresponding to each of the second indicator values; For each of the evaluation indicators, using the second cumulative distribution data of the evaluation indicator, determining a score of the evaluation indicator; The scores of the evaluation indicators are combined to obtain the residual value evaluation score of the battery pack to be evaluated.

2. The method according to claim 1, characterized in that The step of synthesizing the plurality of first cumulative distribution data of the evaluation index to obtain second cumulative distribution data of the evaluation index comprises: determining a number of first parameter values; For each of the first cumulative distribution data of the evaluation index, generating a first interpolation function corresponding to the first cumulative distribution data by using the first cumulative distribution data, and determining a second parameter value of the first cumulative distribution data with respect to each of the first parameter values ​​by using the first interpolation function of the first cumulative distribution data; For each of the first parameter values, synthesizing each of the second parameter values ​​corresponding to the first parameter value to obtain a third parameter value corresponding to the first parameter value; Among them, one of the first parameter values ​​is one of the second index values, and the third parameter value corresponding to the first parameter value is the second cumulative distribution probability corresponding to the corresponding second index value; or, one of the first parameter value is one of the second cumulative distribution probability, and the third parameter value corresponding to the first parameter value is the second index value corresponding to the corresponding second cumulative distribution probability.

3. The method according to claim 2, characterized in that Combining the second parameter values ​​corresponding to the same first parameter value to obtain a third parameter value corresponding to the first parameter value includes: For each second parameter value corresponding to the first parameter value, determining a weight coefficient of the second parameter value based on the operating condition duration of the first cumulative distribution function corresponding to the second parameter value; and, Each of the second parameter values ​​is weighted by using the weight coefficient of each of the second parameter values ​​to obtain the third parameter value corresponding to the first parameter value.

4. The method according to claim 1, wherein the step of obtaining a plurality of first cumulative distribution data corresponding to a plurality of evaluation indicators of the battery pack to be evaluated comprises: Acquire a plurality of initial cumulative distribution data corresponding to a plurality of evaluation indicators of the battery pack to be evaluated; wherein each of the initial cumulative distribution data of the evaluation indicator includes a plurality of initial indicator values ​​of the evaluation indicator and an initial cumulative distribution probability corresponding to each of the initial indicator values; For each of the initial cumulative distribution data of each of the evaluation indicators, a second interpolation function corresponding to the initial cumulative distribution data is generated using the initial cumulative distribution data, and the initial cumulative distribution data is interpolated using the second interpolation function corresponding to the initial cumulative distribution data to obtain the first cumulative distribution data corresponding to the initial cumulative distribution data.

5. The method according to claim 4, characterized in that After obtaining a plurality of initial cumulative distribution data respectively corresponding to a plurality of evaluation indicators of the battery pack to be evaluated, and before generating a second interpolation function corresponding to the cumulative distribution data using the initial cumulative distribution data for each of the evaluation indicators, the method further includes: Eliminating the initial cumulative distribution data that meets a preset condition from the plurality of initial cumulative distribution data; Wherein, the preset condition includes at least one of the following: the initial cumulative distribution data has no corresponding operating condition duration, and each of the initial indicator values ​​of the initial cumulative distribution data is a null value.

6. The method according to claim 1, characterized in that The step of determining the score of the evaluation indicator by using the second cumulative distribution data of the evaluation indicator comprises: Determine a target cumulative distribution probability using the second cumulative distribution data of the evaluation indicator; Determine the target evaluation value of the evaluation indicator by using the target cumulative distribution probability; The target evaluation value is used for scoring to obtain a score for the evaluation indicator.

7. The method according to claim 6, characterized in that The step of determining the target evaluation value of the evaluation indicator by using the target cumulative distribution probability includes: If the evaluation index belongs to the first category of indicators, the product of the target cumulative distribution probability and the total operating time of the evaluation index is used as the target evaluation value; If the evaluation indicator belongs to the second category of indicators, the target indicator value of the evaluation indicator corresponding to the target cumulative distribution probability is used as the target evaluation value; And / or, the scoring using the target evaluation value to obtain the score of the evaluation indicator includes: Determine a target scoring interval to which the target evaluation value belongs; wherein the target scoring interval is one of a plurality of preset scoring intervals, each of the preset scoring intervals including a corresponding upper limit evaluation value and a lower limit evaluation value; Generate a scoring function for the target scoring interval using the upper limit evaluation value of the target scoring interval, the score corresponding to the upper limit evaluation value, the lower limit evaluation value, and the score corresponding to the lower limit evaluation value; The target evaluation value is scored using the scoring function of the target scoring interval to obtain the score of the evaluation indicator.

8. The method according to claim 1, characterized in that: The comprehensive scores of the evaluation indicators are used to obtain the residual value evaluation score of the battery pack to be evaluated, including: Determine the first weight coefficient corresponding to each of the evaluation indicators; The scores of the evaluation indicators are weighted using the first weight coefficients to obtain the residual value evaluation score.

9. The method according to claim 8, characterized in that The determining of the first weight coefficients corresponding to the evaluation indicators respectively includes: Obtaining a second weight coefficient for each of the evaluation indicators; Determine a sum of weights corresponding to each of the second weight coefficients; For each of the evaluation indicators, a ratio of the second weight coefficient of the evaluation indicator to the weight sum is used as the first weight coefficient of the evaluation indicator.

10. The method according to claim 1, characterized in that After obtaining the second cumulative distribution data of the evaluation indicator by synthesizing the plurality of first cumulative distribution data of the evaluation indicator for each of the evaluation indicators, and before determining the score of the evaluation indicator by using the second cumulative distribution data of the evaluation indicator for each of the evaluation indicators, the method further includes: For each of the evaluation indicators, a first fitting line is obtained by fitting the second cumulative distribution data of the evaluation indicator; For each of the evaluation indicators, third cumulative distribution data is extracted from the plurality of first cumulative distribution data of the evaluation indicator, and a second fitting line is obtained by fitting the third cumulative distribution data; wherein the third cumulative distribution data includes a plurality of third indicator values ​​of the evaluation indicator and third cumulative distribution probabilities corresponding to each of the third indicator values; Based on the similarity between the first fitting line and the second fitting line, it is verified whether the second cumulative distribution data of the evaluation index is valid.

11. The method according to claim 10, characterized in that The step of extracting the third cumulative distribution data from the plurality of first cumulative distribution data of the evaluation index comprises: Determine, based on the operating time duration corresponding to each of the first cumulative distribution data, the extraction ratios corresponding to each of the first cumulative distribution data; Data that meets the corresponding extraction ratio is extracted from each of the first cumulative distribution data to obtain the third cumulative distribution data.

12. A battery residual value evaluation device, characterized in that: The device comprises: An acquisition module, used to acquire a plurality of first cumulative distribution data corresponding to a plurality of evaluation indicators of a battery pack to be evaluated; wherein each of the first cumulative distribution data of the evaluation indicators is obtained by using the evaluation indicator data within the corresponding working condition duration, and each of the first cumulative distribution data of the evaluation indicators includes a plurality of first indicator values ​​of the evaluation indicators and a first cumulative distribution probability corresponding to each of the first indicator values; A data integration module is used for integrating the plurality of first cumulative distribution data of the evaluation indicator for each of the evaluation indicators to obtain second cumulative distribution data of the evaluation indicator; wherein the second cumulative distribution data of the evaluation indicator includes a plurality of second indicator values ​​of the evaluation indicator and second cumulative distribution probabilities corresponding to each of the second indicator values; A first scoring module, configured to determine, for each of the evaluation indicators, a score of the evaluation indicator by using the second cumulative distribution data of the evaluation indicator; The second scoring module is used to comprehensively calculate the scores of the evaluation indicators to obtain a residual value evaluation score of the battery pack to be evaluated.

13. An electronic device, characterized in that: comprising a memory and a processor coupled to each other, The memory stores program instructions; The processor is used to execute the program instructions stored in the memory to implement the method according to any one of claims 1 to 11.

14. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store program instructions, and the program instructions can be executed by a processor to implement the method according to any one of claims 1 to 11.

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