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

Abstract

The invention discloses a battery residual value evaluation method and device, equipment, a medium and a product. The method comprises the following steps: acquiring a performance state parameter and an appearance state parameter of a battery; processing the performance state of the battery based on the performance state parameter to obtain a performance coefficient of the battery, and processing the appearance state of the battery based on the appearance state parameter to obtain an appearance coefficient of the battery; a residual value of the battery is evaluated based on the performance coefficient and the appearance coefficient. Therefore, the residual value of the battery can be comprehensively and accurately evaluated from the two aspects of performance and appearance.

Description

Technical Field

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

[0002] Due to the high cost of batteries and the certain environmental pollution caused by their degradation, batteries are usually recycled or disassembled.

[0003] In order to facilitate recycling or disassembly, the residual value of the battery needs to be evaluated.

[0004] Therefore, a method is needed to evaluate the residual value of a battery. Summary of the invention

[0005] The present application provides a battery residual value evaluation method, device, equipment, medium and product, which can comprehensively and accurately evaluate the residual value of the battery from two aspects of performance and appearance.

[0006] In a first aspect, the present application provides a method for evaluating the residual value of a battery, comprising: obtaining performance status parameters and appearance status parameters of a battery; processing the performance status of the battery based on the performance status parameters, especially quantifying the performance coefficient of the battery, and processing the appearance status of the battery based on the appearance status parameters, especially quantifying the appearance coefficient of the battery; evaluating the residual value of the battery based on the performance coefficient and the appearance coefficient.

[0007] Therefore, the battery performance coefficient can be obtained by processing the battery performance state based on the battery performance state parameters, and the battery appearance coefficient can be obtained by processing the battery appearance state based on the battery appearance state parameters, and then the battery residual value can be evaluated based on the performance coefficient and appearance coefficient. In this way, the battery residual value can be comprehensively and accurately evaluated from the two aspects of performance and appearance.

[0008] In some embodiments, the performance state of the battery is processed based on the performance state parameters to obtain the performance coefficient of the battery, including: determining coefficients corresponding to multiple performance state parameters respectively; normalizing the coefficients corresponding to multiple performance state parameters respectively to obtain the performance coefficient of the battery.

[0009] In this way, by normalizing the coefficients corresponding to the multiple performance status parameters, the performance coefficient of the battery can be determined more comprehensively and accurately.

[0010] In some embodiments, multiple performance status parameters include battery cycle times, and determining the coefficient corresponding to the battery cycle times includes: calculating the difference between the calibrated battery cycle times and the battery cycle times to obtain the remaining cycle times of the battery; calculating the ratio of the remaining cycle times to the calibrated cycle times to obtain the coefficient corresponding to the battery cycle times.

[0011] In this way, through the above process, the coefficient corresponding to the battery cycle number can be more accurately determined based on the calibrated battery cycle number and the battery cycle number of the battery.

[0012] In some embodiments, the plurality of performance status parameters include a remaining rated capacity, and determining a coefficient corresponding to the remaining rated capacity includes: calculating a ratio of the remaining rated capacity to a calibrated rated capacity of the battery to obtain a coefficient corresponding to the remaining rated capacity.

[0013] In this way, through the above process, the coefficient corresponding to the remaining rated capacity can be more accurately determined based on the calibrated rated capacity and the remaining rated capacity of the battery.

[0014] In some embodiments, multiple performance status parameters include the internal resistance of the battery cell, and determining the coefficient corresponding to the internal resistance of the battery cell includes: obtaining a first quantity of first battery cells that meet a first preset condition; calculating the difference between the total number of battery cells included in the battery and the first number to obtain a second number; calculating the ratio of the second number to the total number to obtain the coefficient corresponding to the internal resistance of the battery cell.

[0015] In this way, through the above process, based on the total number of battery cells included in the battery and the first number of first battery cells that meet the first preset condition, the coefficient corresponding to the internal resistance of the battery cell can be determined more accurately.

[0016] In some embodiments, obtaining a first number of first battery cells that meet a preset condition includes: obtaining the internal resistances of the battery cells corresponding to each of the multiple battery cells included in the battery; processing the internal resistances of the battery cells corresponding to each of the multiple battery cells to obtain an average internal resistance value; screening out the first battery cells that meet a first preset condition from the multiple battery cells based on the internal resistances of the battery cells corresponding to each of the multiple battery cells and the average internal resistance value, the first preset condition being that the difference between the internal resistance of the first battery cell and the average internal resistance value is greater than a first preset threshold; and determining the number of the first battery cells.

[0017] In this way, by acquiring and processing the internal resistances of the cells respectively corresponding to the plurality of cells, the first cells can be screened out more accurately, thereby accurately determining the first quantity of the first cells.

[0018] In some embodiments, the multiple performance status parameters include a battery cell internal resistance, and determining a coefficient corresponding to the battery cell internal resistance includes: when the battery cell internal resistance of any battery cell included in the battery exceeds a preset range, determining the coefficient corresponding to the battery cell internal resistance to be a first preset value.

[0019] In this way, by determining that the coefficient corresponding to the cell internal resistance is the first preset value when the cell internal resistance of any cell included in the battery exceeds the preset range, the coefficient corresponding to the cell internal resistance can be determined more accurately.

[0020] In some embodiments, multiple performance status parameters include the number of times a preset fault occurs in the battery, and determining the coefficient corresponding to the number of faults includes: calculating the difference between the calibrated number of faults and the number of faults of the battery to obtain the remaining number of faults of the battery; calculating the ratio of the remaining number of faults to the calibrated number of faults to obtain the coefficient corresponding to the number of faults.

[0021] In this way, through the above process, based on the calibrated fault number and the fault number of the battery, the coefficient corresponding to the fault number can be determined more accurately.

[0022] In some embodiments, coefficients corresponding to a plurality of performance status parameters are normalized to obtain a performance coefficient of the battery, including: obtaining preset weights corresponding to the plurality of coefficients; and performing weighted summation of the plurality of coefficients based on the preset weights corresponding to the plurality of coefficients to obtain the performance coefficient of the battery.

[0023] In this way, the coefficients corresponding to the multiple performance status parameters can be more accurately normalized by weighted summation.

[0024] In some embodiments, the performance status of the battery is processed based on the performance status parameters to obtain the performance coefficient of the battery, including: obtaining second preset conditions corresponding to multiple performance status parameters respectively; when there is a target performance status parameter among the multiple performance status parameters that meets the target preset condition, determining that the performance coefficient of the battery is a second preset value, and the target preset condition is the second preset condition corresponding to the target performance status parameter.

[0025] In this way, through the above process, by determining that the performance coefficient of the battery is the second preset value when any performance status parameter meets its corresponding second preset condition, the performance coefficient of the battery can be quickly determined based on the performance status parameter without quantization and normalization processing.

[0026] In some embodiments, the appearance status parameters include case status parameters and module status parameters, and the appearance status of the battery is processed based on the appearance status parameters to obtain an appearance coefficient of the battery, including: processing the case appearance status of the battery based on the case status parameters, especially quantifying it, to obtain a case damage coefficient, and processing the module appearance status of the battery based on the module status parameters, especially quantifying it, to obtain a module damage coefficient; processing the appearance status of the battery based on the case damage coefficient and the module damage coefficient, especially quantifying it, to obtain the appearance coefficient of the battery.

[0027] In this way, by quantifying the appearance status of the battery case and the module respectively, the appearance coefficient of the battery can be determined more comprehensively and accurately based on the case damage coefficient and the module damage coefficient.

[0028] In some embodiments, the box state parameter includes at least one of whether the box has scratches, whether the positive and negative terminal wires have rust, and whether the box is dented.

[0029] In this way, the battery case damage coefficient can be determined more comprehensively and accurately based on the above case state parameters.

[0030] In some embodiments, the module status parameter includes at least one of whether the module connector is detached, whether the module is dented, and whether there are scratches on the module surface.

[0031] In this way, the module damage coefficient of the battery can be determined more comprehensively and accurately based on the above module status parameters.

[0032] In some embodiments, evaluating the residual value of the battery based on the performance coefficient and the appearance coefficient includes: determining the residual value of the battery through a fuzzy control algorithm based on the performance coefficient and the appearance coefficient.

[0033] In this way, the residual value of the battery can be accurately evaluated through the fuzzy control algorithm.

[0034] In some embodiments, the residual value of the battery is determined based on the performance coefficient and the appearance coefficient through a fuzzy control algorithm, including: fuzzifying the performance coefficient and the appearance coefficient respectively to obtain fuzzy subsets corresponding to the performance coefficient and the appearance coefficient respectively; performing fuzzy reasoning on the fuzzy subsets corresponding to the performance coefficient and the appearance coefficient respectively based on fuzzy rules to obtain the fuzzy subset corresponding to the residual value; defuzzifying the fuzzy subset corresponding to the residual value to obtain the residual value of the battery.

[0035] In this way, by fuzzifying the performance coefficient and the appearance coefficient, performing fuzzy reasoning on the fuzzy subsets corresponding to the performance coefficient and the appearance coefficient respectively, and defuzzifying the fuzzy subset corresponding to the residual value, a more accurate battery residual value can be obtained, thereby further improving the accuracy of the battery residual value.

[0036] In some embodiments, the fuzzy rules include: when the performance coefficient is less than the first threshold and the appearance coefficient is less than the second threshold, determining that the residual value of the battery is less than the third threshold; when the performance coefficient is greater than the fourth threshold and the appearance coefficient is less than the fifth threshold, determining that the residual value of the battery is between the sixth threshold and the seventh threshold; when the performance coefficient is less than the eighth threshold and the appearance coefficient is greater than the ninth threshold, determining that the residual value of the battery is less than the tenth threshold; when the performance coefficient is greater than the eleventh threshold and the appearance coefficient is greater than the twelfth threshold, determining that the residual value of the battery is greater than the thirteenth threshold; the first threshold and the eighth threshold are both less than or equal to the fourth threshold, the first threshold and the eighth threshold are both less than or equal to the eleventh threshold, the second threshold and the fifth threshold are both less than or equal to the ninth threshold, the second threshold and the fifth threshold are both less than or equal to the twelfth threshold, the third threshold and the tenth threshold are both less than or equal to the sixth threshold, the thirteenth threshold is greater than or equal to the seventh threshold, and the sixth threshold is less than the seventh threshold.

[0037] In this way, based on the above reasonable fuzzy rules, the fuzzy subset corresponding to the residual value can be inferred more reasonably.

[0038] In some embodiments, after evaluating the residual value of the battery based on the performance coefficient and the appearance coefficient, the method also includes: obtaining the price of a target battery, the target battery being a battery of the same model as the battery and having a number of uses lower than a second preset threshold; estimating the price of the battery based on the price of the target battery and the residual value of the battery.

[0039] In this way, the price of the battery can be accurately estimated based on the price of the target battery and the residual value of the battery, thereby making it easier for users to understand the price of the battery.

[0040] In a second aspect, the present application provides a battery residual value evaluation device, including: a parameter acquisition module, used to obtain the performance status parameters and appearance status parameters of the battery; a processing module, used to process the performance status of the battery based on the performance status parameters to obtain the performance coefficient of the battery, and to process the appearance status of the battery based on the appearance status parameters to obtain the appearance coefficient of the battery; a residual value evaluation module, used to evaluate the residual value of the battery based on the performance coefficient and the appearance coefficient.

[0041] Therefore, the battery performance coefficient can be obtained by processing the battery performance state based on the battery performance state parameters, and the battery appearance coefficient can be obtained by processing the battery appearance state based on the battery appearance state parameters, and then the battery residual value can be evaluated based on the performance coefficient and appearance coefficient. In this way, the battery residual value can be comprehensively and accurately evaluated from the two aspects of performance and appearance.

[0042] In a third aspect, the present application provides an electronic device, the device comprising: a processor and a memory storing computer program instructions;

[0043] When the processor executes the computer program instructions, the method for evaluating the battery residual value as shown in any one of the embodiments of the first aspect is implemented.

[0044] In a fourth aspect, the present application provides a computer storage medium having computer program instructions stored thereon, and when the computer program instructions are executed by a processor, the battery residual value evaluation method shown in any one of the embodiments of the first aspect is implemented.

[0045] In a fifth aspect, an embodiment of the present application provides a computer program product. When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the battery residual value evaluation method shown in any one of the embodiments of the first aspect.

[0046] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Moreover, the same reference numerals are used throughout the drawings to represent the same components. In the drawings:

[0048] Figure 1 One of the flowcharts of a battery residual value evaluation method provided in some embodiments of the present application;

[0049] Figure 2 A membership function diagram of a performance coefficient provided in some embodiments of the present application;

[0050] Figure 3 A membership function diagram of an appearance coefficient provided in some embodiments of the present application;

[0051] Figure 4 A membership function diagram of a battery residual value provided in some embodiments of the present application;

[0052] Figure 5 A second flow chart of a method for evaluating the residual value of a battery provided in some embodiments of the present application;

[0053] Figure 6 A schematic diagram of the structure of a battery residual value evaluation device provided in some embodiments of the present application;

[0054] Figure 7A schematic diagram of the structure of an electronic device provided for some embodiments of the present application. DETAILED DESCRIPTION

[0055] The following embodiments of the technical solution of the present application are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application, and are therefore only used as examples, and cannot be used to limit the scope of protection of the present application.

[0056] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0057] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0058] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0059] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0060] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0061] As mentioned in the background technology, with the continuous advocacy of energy conservation and emission reduction and green travel in recent years, the number of new energy vehicles is also increasing. New energy vehicles use power batteries as energy sources and indirectly drive the vehicle by driving the motor. Power batteries are batteries that transmit electrical energy at high power, so they also have a life span. Once they reach their life span, they face the problem of recycling or disassembly. The cost of power batteries is high, and degradation will also cause certain environmental pollution. If the residual value of the power battery is evaluated based solely on the service life of the power battery, there will be unequal prices and waste of resources. Therefore, accurately evaluating the residual value of the power battery is very necessary for the recycling of the power battery, which can facilitate car owners to understand the residual value of the power battery, and buyers and sellers can also reasonably price according to the residual value of the power battery.

[0062] Therefore, a method is needed to evaluate the residual value of a battery.

[0063] In view of the above technical problems, in the embodiments of the present application, the battery performance state is processed based on the battery performance state parameters to obtain the battery performance coefficient, and the battery appearance state is processed based on the battery appearance state parameters to obtain the battery appearance coefficient, and then the battery residual value can be evaluated based on the performance coefficient and the appearance coefficient. In this way, the battery residual value can be comprehensively and accurately evaluated from the two aspects of performance and appearance.

[0064] First, combine Figure 1 The battery residual value evaluation method provided in the embodiment of the present application is described in detail.

[0065] Figure 1 A flow chart of a battery residual value evaluation method provided in an embodiment of the present application is shown. It should be noted that the battery residual value evaluation method can be applied to a battery residual value evaluation device.

[0066] like Figure 1 As shown, the battery residual value evaluation method may include the following steps:

[0067] S110, obtaining performance status parameters and appearance status parameters of the battery;

[0068] S120, processing the performance state of the battery based on the performance state parameter to obtain a performance coefficient of the battery, and processing the appearance state of the battery based on the appearance state parameter to obtain an appearance coefficient of the battery;

[0069] A130, evaluates the residual value of the battery based on performance coefficient and appearance coefficient.

[0070] Therefore, the battery performance coefficient can be obtained by processing the battery performance state based on the battery performance state parameters, and the battery appearance coefficient can be obtained by processing the battery appearance state based on the battery appearance state parameters, and then the battery residual value can be evaluated based on the performance coefficient and appearance coefficient. In this way, the battery residual value can be comprehensively and accurately evaluated from the two aspects of performance and appearance.

[0071] Regarding S110, the performance status parameter may represent the current performance status of the battery, and the appearance status parameter may represent the current appearance status of the battery.

[0072] The number of performance status parameters may be one or more. The number of appearance status parameters may include one or more.

[0073] For example, the battery may be a power battery of a vehicle. The performance status parameter may be recorded by a cloud big data module, and the appearance status parameter may be determined based on user input.

[0074] In some embodiments, the performance status parameter may include at least one of the number of battery cycles, remaining rated capacity, battery cell internal resistance, and number of failures.

[0075] Regarding S120, in order to facilitate the evaluation of the residual value of the battery, the performance status parameters and appearance status parameters of the battery may be quantified.

[0076] The performance coefficient can represent the current performance status of the battery. The appearance coefficient can represent the current appearance status of the battery.

[0077] In some embodiments, in order to more accurately determine the performance coefficient of the battery, the above-mentioned processing of the performance state of the battery based on the performance state parameter to obtain the performance coefficient of the battery may include:

[0078] Determine coefficients corresponding to a plurality of performance status parameters;

[0079] The coefficients corresponding to the multiple performance status parameters are normalized to obtain the performance coefficient of the battery.

[0080] Here, if there are multiple performance state parameters, each performance state parameter can be quantified to obtain a coefficient corresponding to each performance state parameter. Then, the coefficients corresponding to the multiple performance state parameters are normalized to obtain the performance coefficient of the battery.

[0081] In some embodiments, the plurality of performance status parameters may include at least two of the number of battery cycles, remaining rated capacity, battery cell internal resistance, and number of failures.

[0082] In this way, by normalizing the coefficients corresponding to the multiple performance status parameters, the performance coefficient of the battery can be determined more comprehensively and accurately.

[0083] In some embodiments, the above-mentioned multiple performance status parameters may include the number of battery cycles, and determining the coefficient corresponding to the number of battery cycles may include:

[0084] Calculate the difference between the rated battery cycle number and the battery cycle number of the battery to obtain the remaining cycle number of the battery;

[0085] The ratio of the remaining number of cycles to the calibrated number of cycles is calculated to obtain the coefficient corresponding to the number of battery cycles.

[0086] Here, the rated battery cycle number may be the total battery cycle number that the battery can cycle, and the battery cycle number may be the current battery cycle number of the battery.

[0087] For example, the calculation formula of the coefficient corresponding to the number of battery cycles may be:

[0088]

[0089] Wherein, C may be a coefficient corresponding to the number of battery cycles, 0<C<1, n1 may be the number of battery cycles, and N1 may be the calibrated number of battery cycles.

[0090] C=1 can indicate that the battery has just been packaged off the production line and has not yet been charged and discharged; C=0 can indicate that the number of charge and discharge cycles of the battery has reached the upper limit and the battery is close to being scrapped.

[0091] In this way, through the above process, the coefficient corresponding to the battery cycle number can be more accurately determined based on the calibrated battery cycle number and the battery cycle number of the battery.

[0092] In some embodiments, the plurality of performance status parameters may include a remaining rated capacity, and determining a coefficient corresponding to the remaining rated capacity may include:

[0093] The ratio of the remaining rated capacity to the calibrated rated capacity of the battery is calculated to obtain a coefficient corresponding to the remaining rated capacity.

[0094] Here, the calibrated rated capacity may be the rated capacity tested and marked before the battery leaves the factory, and the remaining rated capacity may be the rated capacity of the battery after capacity decay occurs during use.

[0095] Exemplarily, the coefficient corresponding to the remaining rated capacity may be:

[0096]

[0097] Wherein, D may be a coefficient corresponding to the remaining rated capacity, 0<D<1, n2 may be the remaining rated capacity, and N2 may be the calibrated rated capacity.

[0098] D=1 can indicate that the remaining rated capacity of the battery is equal to the calibrated rated capacity, and the battery has not been used for a long time and is still relatively new; D=0 can indicate that the remaining rated capacity of the battery is close to 0 and the battery is scrapped.

[0099] In this way, through the above process, the coefficient corresponding to the remaining rated capacity can be more accurately determined based on the calibrated rated capacity and the remaining rated capacity of the battery.

[0100] In some embodiments, the plurality of performance status parameters may include a cell internal resistance, and determining a coefficient corresponding to the cell internal resistance may include:

[0101] Acquire a first quantity of first battery cells that meet a first preset condition;

[0102] Calculate the difference between the total number of cells included in the battery and the first number to obtain a second number;

[0103] The ratio of the second quantity to the total quantity is calculated to obtain a coefficient corresponding to the internal resistance of the battery cell.

[0104] Here, the battery may include a plurality of cells. The first number may be the number of cells whose internal resistance has changed greatly compared to the initial value.

[0105] For example, the calculation formula of the coefficient corresponding to the internal resistance of the battery cell may be:

[0106]

[0107] Among them, G may be a coefficient corresponding to the internal resistance of the battery cell, 0<G<1, m may be a first number, and n may be a total number.

[0108] G=1 can indicate that the battery cell internal resistance is in normal condition and is basically consistent with the battery cell internal resistance when the battery leaves the factory; G=0 can indicate that the battery cell resistance is significantly different from the battery cell internal resistance when the battery leaves the factory.

[0109] In this way, through the above process, based on the total number of battery cells included in the battery and the first number of first battery cells that meet the first preset condition, the coefficient corresponding to the internal resistance of the battery cell can be determined more accurately.

[0110] In some embodiments, in order to more accurately determine the first quantity, the obtaining the first quantity of the first battery cells that meet the preset condition may include:

[0111] Obtain the internal resistance of each battery cell corresponding to each of the multiple battery cells included in the battery;

[0112] Process the internal resistances of the cells corresponding to the multiple cells respectively to obtain the average internal resistance value;

[0113] According to the internal resistances of the multiple battery cells respectively corresponding to the multiple battery cells and the average value of the internal resistances, a first battery cell that meets a first preset condition is selected from the multiple battery cells;

[0114] The number of first battery cells is determined.

[0115] Here, the first preset condition may be that the difference between the internal resistance of the first battery cell and the average internal resistance is greater than a first preset threshold value. The first preset threshold value may be set according to actual needs.

[0116] For example, the calculation formula of the average internal resistance may be:

[0117] a avg =(a1+a2+…+a n ) / n

[0118] Among them, a avg Can be the average value of internal resistance, a i It may be the internal resistance of the ith battery cell, where i=1, 2, ..., n, and n may be the total number of battery cells included in the battery.

[0119] In this way, by acquiring and processing the internal resistances of the cells respectively corresponding to the plurality of cells, the first cells can be screened out more accurately, thereby accurately determining the first quantity of the first cells.

[0120] In some embodiments, the plurality of performance status parameters may include a cell internal resistance, and determining a coefficient corresponding to the cell internal resistance may include:

[0121] When the internal resistance of any cell included in the battery exceeds a preset range, the coefficient corresponding to the internal resistance of the cell is determined to be a first preset value.

[0122] Here, the preset range may be a preset normal range of the battery cell internal resistance. If the battery cell internal resistance of a certain battery cell exceeds the preset range, it may indicate that the battery cell internal resistance of the battery cell is abnormal.

[0123] Exemplarily, when the internal resistance of any cell included in the battery exceeds a preset range, it can be considered that the internal resistance of the cell in the battery is abnormal. At this time, it can be determined that the coefficient corresponding to the internal resistance of the cell of the battery is 0.

[0124] In this way, by determining that the coefficient corresponding to the cell internal resistance is the first preset value when the cell internal resistance of any cell included in the battery exceeds the preset range, the coefficient corresponding to the cell internal resistance can be determined more accurately.

[0125] In some embodiments, the plurality of performance status parameters may include the number of failures of a preset battery failure, and determining the coefficient corresponding to the number of failures may include:

[0126] Calculate the difference between the calibrated fault times and the fault times of the battery to obtain the remaining fault times of the battery;

[0127] Calculate the ratio of the remaining fault times to the calibrated fault times to obtain the coefficient corresponding to the fault times.

[0128] Here, the preset fault may be a high-level fault, the calibrated fault number may be the total number of preset faults allowed to occur marked on the battery before it leaves the factory, and the fault number may be the number of preset faults that have currently occurred on the battery.

[0129] Exemplarily, the calculation formula of the coefficient corresponding to the number of failures may be:

[0130]

[0131] Among them, H can be the coefficient corresponding to the number of faults, 0<H<1, n3 can be the number of faults, and N3 can be the calibrated number of faults.

[0132] H=1 can indicate that the battery has basically not experienced any preset faults; H=0 can indicate that the number of preset faults that have occurred in the battery has exceeded the expected range, which has a serious impact on the safety and performance of the battery.

[0133] In this way, through the above process, based on the calibrated fault number and the fault number of the battery, the coefficient corresponding to the fault number can be determined more accurately.

[0134] In some embodiments, in order to more accurately normalize the coefficients corresponding to the multiple performance status parameters to obtain the performance coefficient of the battery, the following may be included:

[0135] Obtain preset weights corresponding to multiple coefficients respectively;

[0136] Based on the preset weights corresponding to the multiple coefficients, the multiple coefficients are weighted and summed to obtain the performance coefficient of the battery.

[0137] Here, the sum of the preset weights corresponding to the multiple coefficients may be 1. The specific values ​​of the preset weights corresponding to the multiple coefficients may be determined according to the type of battery.

[0138] For example, the calculation formula of the performance coefficient of the battery can be:

[0139] E=k1C+k2D+k3G+k4H

[0140] Among them, E can be the performance coefficient of the battery, 0<E<1, C can be the coefficient corresponding to the number of battery cycles, D can be the coefficient corresponding to the remaining rated capacity, G can be the coefficient corresponding to the internal resistance of the battery cell, H can be the coefficient corresponding to the number of failures, k1 can be the preset weight of the coefficient corresponding to the number of battery cycles, k2 can be the preset weight of the coefficient corresponding to the remaining rated capacity, k3 can be the preset weight of the coefficient corresponding to the internal resistance of the battery cell, and k4 can be the preset weight of the coefficient corresponding to the number of failures. k1+k2+k3+k4=1.

[0141] E=1 can indicate that the battery performance is extremely good; E=0 can indicate that the battery life has reached its upper limit and the battery is close to being scrapped.

[0142] Batteries from different manufacturers differ in materials, production processes and subsequent battery performance, so the weight distribution of k1, k2, k3, and k4 corresponding to batteries from different manufacturers is different.

[0143] In this way, the coefficients corresponding to the multiple performance status parameters can be more accurately normalized by weighted summation.

[0144] In addition, when a certain performance status parameter is within a preset range, the coefficient corresponding to the performance status parameter can be determined to be 0, and the preset weight corresponding to the performance status parameter can be determined to be 1. The performance status parameter being within the preset range can indicate that the performance status parameter is abnormal. In this way, when any performance status parameter is within the preset range, the coefficient corresponding to the performance status parameter is 0, the preset weight corresponding to the performance status parameter is 1, and the preset weights corresponding to other performance status parameters will all be 0. Therefore, the performance coefficient of the battery can be 0, indicating that the battery has no performance value.

[0145] In some embodiments, in order to more quickly determine the performance coefficient of the battery, the above-mentioned processing of the performance state of the battery based on the performance state parameter to obtain the performance coefficient of the battery may include:

[0146] Obtaining second preset conditions corresponding to the plurality of performance status parameters respectively;

[0147] When there is a target performance status parameter among the multiple performance status parameters that meets the target preset condition, the performance coefficient of the battery is determined to be a second preset value.

[0148] Here, the second preset condition may be that the performance status parameter is within a preset range. The performance status parameter being within the preset range may indicate that the performance status parameter is abnormal. Different performance status parameters may correspond to different second preset conditions.

[0149] The target performance status parameter may be at least one of a plurality of performance status parameters. The target preset condition may be a second preset condition corresponding to the target performance status parameter.

[0150] Exemplarily, the second preset value may be 0.

[0151] That is to say, if any performance status parameter satisfies its corresponding second preset condition, it can be indicated that the performance status parameter is abnormal, and it can be considered that the battery has no performance value, so the performance coefficient of the battery can be determined to be 0.

[0152] In this way, through the above process, by determining that the performance coefficient of the battery is the second preset value when any performance status parameter meets its corresponding second preset condition, the performance coefficient of the battery can be quickly determined based on the performance status parameter without quantization and normalization processing.

[0153] In some embodiments, in order to more comprehensively and accurately determine the appearance coefficient of the battery, the appearance state parameters may include box state parameters and module state parameters. The above-mentioned processing of the appearance state of the battery based on the appearance state parameters to obtain the appearance coefficient of the battery may include:

[0154] The appearance state of the battery box is processed based on the box state parameter to obtain the box damage coefficient, and the module appearance state of the battery box is processed based on the module state parameter to obtain the module damage coefficient;

[0155] The appearance state of the battery is processed based on the box damage coefficient and the module damage coefficient to obtain the appearance coefficient of the battery.

[0156] Here, the case state parameter can characterize the appearance state of the battery case. The module state parameter can characterize the appearance state of the battery module. The case damage coefficient can characterize the degree of damage to the appearance of the battery case. The module damage coefficient can characterize the degree of damage to the appearance of the battery module.

[0157] In some embodiments, in order to more comprehensively and accurately determine the battery case damage coefficient, the case state parameter may include at least one of whether the case has scratches, whether the positive and negative terminal wires have rust, and whether the case is dented.

[0158] In this way, the battery case damage coefficient can be determined more comprehensively and accurately based on the above case state parameters.

[0159] In some embodiments, in order to more comprehensively and accurately determine the module damage coefficient of the battery, the module status parameter may include at least one of whether the module connector is detached, whether the module is dented, and whether there are scratches on the module surface.

[0160] Thus, based on the above module status parameters, the module damage factor of the battery can be determined more comprehensively and accurately.

[0161] Specifically, the corresponding relationship between the box status parameters and the box damage factor, as well as the corresponding relationship between the module status parameters and the module damage factor, can be preset in advance. Then, based on the preset corresponding relationship, the box damage factor corresponding to the box status parameters of the battery and the module damage factor corresponding to the module status parameters of the battery are determined.

[0162] Exemplarily, the calculation formula for the appearance factor of the battery can be:

[0163] F = 1 - C1 - C2

[0164] Wherein, F can be the appearance factor of the battery, 0 < F < 1, C1 can be the box damage factor, 0 < C1 < 1, C2 can be the module damage factor, 0 < C2 < 1, and C1 + C2 < 1.

[0165] F = 1 can represent that the appearance of the battery is undamaged, and both the box appearance and the module appearance of the battery are normal; F = 0 can represent that the box appearance and / or the module appearance of the battery has obvious damage that is difficult to repair.

[0166] Thus, by quantifying the box appearance state and the module appearance state of the battery respectively, the appearance factor of the battery can be determined more comprehensively and accurately based on the box damage factor and the module damage factor.

[0167] Regarding S130, the residual value of the battery can be comprehensively evaluated based on the performance factor and the appearance factor of the battery.

[0168] Exemplarily, the residual value of the battery can be Z, 0 < Z < 1. The larger Z is, the higher the remaining value of the battery is; the smaller Z is, the lower the remaining value of the battery is.

[0169] In some embodiments, in order to accurately evaluate the residual value of the battery, S130 may include:

[0170] Based on the performance factor and the appearance factor, the residual value of the battery is determined by a fuzzy control algorithm.

[0171] Here, the input of the fuzzy control algorithm can be the performance factor and the appearance factor, and the output can be the residual value of the battery.

[0172] Specifically, the performance factor and the appearance factor can be fuzzified and fuzzy-inferred by using the fuzzy control algorithm, and then defuzzified to obtain the residual value of the battery.

[0173] Thus, the residual value of the battery can be accurately evaluated by the fuzzy control algorithm.

[0174] In some embodiments, in order to further improve the accuracy of the battery residual value, the above-mentioned determination of the battery residual value by a fuzzy control algorithm based on the performance coefficient and the appearance coefficient may include:

[0175] The performance coefficient and the appearance coefficient are fuzzified respectively to obtain the fuzzy subsets corresponding to the performance coefficient and the appearance coefficient respectively;

[0176] Based on fuzzy rules, fuzzy reasoning is performed on the fuzzy subsets corresponding to the performance coefficient and the appearance coefficient, and the fuzzy subset corresponding to the residual value is obtained;

[0177] The fuzzy subset corresponding to the residual value is defuzzified to obtain the residual value of the battery.

[0178] Here, the performance coefficient and the appearance coefficient can be mapped to the fuzzy subsets respectively through the membership function to obtain the fuzzy subsets corresponding to the performance coefficient and the appearance coefficient respectively. Then, based on the fuzzy rules, the fuzzy subsets corresponding to the performance coefficient and the appearance coefficient are subjected to fuzzy reasoning to obtain the fuzzy subsets corresponding to the residual value.

[0179] For example, the membership function curve of the performance coefficient can be as follows: Figure 2 As shown, EI can be the membership function curve of the low performance coefficient, which can be used to characterize the corresponding relationship between the low performance coefficient and the membership; SI can be the membership function curve of the lower performance coefficient, which can be used to characterize the corresponding relationship between the lower performance coefficient and the membership; MI can be the membership function curve of the medium performance coefficient, which can be used to characterize the corresponding relationship between the medium performance coefficient and the membership; BI can be the membership function curve of the higher performance coefficient, which can be used to characterize the corresponding relationship between the higher performance coefficient and the membership; HI can be the membership function curve of the high performance coefficient, which can be used to characterize the corresponding relationship between the high performance coefficient and the membership.

[0180] Therefore, the fuzzy subset corresponding to the performance coefficient can be:

[0181] Performance coefficient E∈[0,1]: low performance coefficient EI; relatively low performance coefficient SI; medium performance coefficient MI; relatively high performance coefficient BI; very high performance coefficient HI.

[0182] The membership function curve of the appearance coefficient can be expressed as Figure 3 As shown, SO can be the membership function curve of the low appearance coefficient, which can be used to characterize the corresponding relationship between the low appearance coefficient and the membership; MO can be the membership function curve of the medium appearance coefficient, which can be used to characterize the corresponding relationship between the medium appearance coefficient and the membership; HO can be the membership function curve of the high appearance coefficient, which can be used to characterize the corresponding relationship between the high appearance coefficient and the membership.

[0183] Therefore, the fuzzy subset corresponding to the appearance coefficient can be:

[0184] Appearance coefficient F∈[0,1]: low appearance coefficient SO; medium appearance coefficient MO; high appearance coefficient HO.

[0185] The membership function curve of the battery residual value can be expressed as Figure 4 As shown, VS can be the membership function curve of low battery residual value, which can be used to characterize the corresponding relationship between low battery residual value and membership; S can be the membership function curve of lower battery residual value, which can be used to characterize the corresponding relationship between lower battery residual value and membership; M can be the membership function curve of medium battery residual value, which can be used to characterize the corresponding relationship between medium battery residual value and membership; H can be the membership function curve of higher battery residual value, which can be used to characterize the corresponding relationship between higher battery residual value and membership; VH can be the membership function curve of high battery residual value, which can be used to characterize the corresponding relationship between high battery residual value and membership.

[0186] Therefore, the fuzzy subset corresponding to the residual value of the battery can be:

[0187] The residual value of the battery Z∈[0,1]: the residual value of the battery is low VS; the residual value of the battery is relatively low S; the residual value of the battery is medium M; the residual value of the battery is relatively high H; the residual value of the battery is very high VH.

[0188] In some embodiments, in order to construct a fuzzy subset corresponding to the residual value that is more reasonably inferred, the fuzzy rules may include:

[0189] When the performance coefficient is less than the first threshold and the appearance coefficient is less than the second threshold, determining that the residual value of the battery is less than a third threshold;

[0190] When the performance coefficient is greater than the fourth threshold and the appearance coefficient is less than the fifth threshold, determining that the residual value of the battery is between the sixth threshold and the seventh threshold;

[0191] When the performance coefficient is less than the eighth threshold and the appearance coefficient is greater than the ninth threshold, determining that the residual value of the battery is less than the tenth threshold;

[0192] When the performance coefficient is greater than the eleventh threshold and the appearance coefficient is greater than the twelfth threshold, determining that the residual value of the battery is greater than the thirteenth threshold;

[0193] The first threshold and the eighth threshold are both less than or equal to the fourth threshold, the first threshold and the eighth threshold are both less than or equal to the eleventh threshold, the second threshold and the fifth threshold are both less than or equal to the ninth threshold, the second threshold and the fifth threshold are both less than or equal to the twelfth threshold, the third threshold and the tenth threshold are both less than or equal to the sixth threshold, the thirteenth threshold is greater than or equal to the seventh threshold, and the sixth threshold is less than the seventh threshold.

[0194] Here, the first threshold, the second threshold, the third threshold, the fourth threshold, the fifth threshold, the sixth threshold, the seventh threshold, the eighth threshold, the ninth threshold, the tenth threshold, the eleventh threshold, the twelfth threshold, and the thirteenth threshold can all be set according to actual needs.

[0195] That is to say, if both the performance coefficient and the appearance coefficient of the battery are small, the battery performance is weak and the residual value of the battery is low; if the performance coefficient of the battery is large and the appearance coefficient is small, the battery cell performance can still maintain the original level, the battery module and the casing are damaged, since the performance of the battery is mainly determined by the battery cell performance, the battery residual value is medium; if the performance coefficient of the battery is small and the appearance coefficient is high, the battery cell performance is low, the battery module and the casing are not damaged, since the value of the battery is mainly determined by the battery performance, the battery residual value is low; if the performance coefficient and the appearance coefficient of the battery are both large, the battery performance is high, and the battery residual value is high.

[0196] In this way, based on the above reasonable fuzzy rules, the fuzzy subset corresponding to the residual value can be inferred more reasonably.

[0197] For example, based on the above fuzzy rules, a fuzzy control table as shown in Table 1 can be formulated. Based on the fuzzy control table, fuzzy reasoning can be performed on the fuzzy subsets corresponding to the performance coefficient E and the appearance coefficient F, respectively, to obtain the fuzzy subset corresponding to the residual value Z of the battery.

[0198] Table 1 - Fuzzy control table

[0199]

[0200] Then, the centroid method can be used to defuzzify the fuzzy subset corresponding to the residual value to obtain the residual value of the battery.

[0201] In this way, by fuzzifying the performance coefficient and the appearance coefficient, performing fuzzy reasoning on the fuzzy subsets corresponding to the performance coefficient and the appearance coefficient respectively, and defuzzifying the fuzzy subset corresponding to the residual value, a more accurate battery residual value can be obtained, thereby further improving the accuracy of the battery residual value.

[0202] In some embodiments, in order to facilitate the user to understand the price of the battery, after S130, the method may further include:

[0203] Get the price of the target battery;

[0204] The price of the battery is estimated based on the price of the target battery and the residual value of the battery.

[0205] Here, the target battery may be a battery of the same model as the battery and having a usage count lower than a second preset threshold. The second preset threshold may be set according to actual needs.

[0206] For example, the target battery may be a brand new battery of the same model as the battery, and the price of the target battery may be the current market price of a brand new battery of the same model as the battery. The price of the battery may be the current price of the battery.

[0207] For example, the calculation formula of the battery price may be:

[0208] N=Z*W

[0209] Among them, N can be the price of the battery, Z can be the residual value of the battery, and W can be the price of the target battery.

[0210] In this way, the price of the battery can be accurately estimated based on the price of the target battery and the residual value of the battery, thereby making it easier for users to understand the price of the battery.

[0211] In order to better describe the entire solution, based on the above embodiments, a specific example is given. Figure 5 As shown, the battery residual value evaluation method may include S501-S508. Among them:

[0212] S501, obtaining multiple performance status parameters of a battery.

[0213] S502, determining coefficients corresponding to a plurality of performance status parameters.

[0214] S503, performing weighted summation on the multiple coefficients based on preset weights corresponding to the multiple coefficients, to obtain a performance coefficient of the battery.

[0215] S504, obtaining the box state parameters and module state parameters of the battery.

[0216] S505, processing the appearance state of the battery case based on the case state parameter to obtain a case damage coefficient, and processing the appearance state of the module of the battery based on the module state parameter to obtain a module damage coefficient.

[0217] S506, processing the appearance state of the battery based on the box damage coefficient and the module damage coefficient to obtain the appearance coefficient of the battery.

[0218] S507, determining the residual value of the battery through a fuzzy control algorithm based on the performance coefficient and the appearance coefficient.

[0219] S508 , estimating the price of the battery based on the price of the target battery and the residual value of the battery.

[0220] The specific process and meaning of each step can be found in the above embodiments and will not be elaborated here.

[0221] In the embodiment of the present application, a battery residual value evaluation system can be constructed, and the battery residual value evaluation system can include a cloud big data module and a fuzzy control module. Taking the evaluation of the residual value of the power battery of an electric vehicle as an example, the performance status parameters such as the number of battery cycles, the remaining rated capacity, the internal resistance of each battery cell, and the number of failures of the power battery can be obtained based on the cloud big data module, and the preset weights of the four performance status parameters are allocated according to the type of battery, and the performance coefficient of the power battery is calculated. Based on the fuzzy control module, the residual value of the power battery is determined based on the performance coefficient and the appearance coefficient, thereby improving the accuracy of evaluating the residual value of the power battery.

[0222] Vehicles equipped with the battery residual value evaluation system can evaluate the residual value of power batteries when recycling power batteries of electric vehicles. Specifically, during the driving or power-off process of the vehicle, the performance status parameters of the power battery, such as the number of battery cycles, the remaining rated capacity, the internal resistance of each battery cell and the number of failures, can be uploaded to the cloud platform through the vehicle networking terminal (TBOX), and these performance status parameters can be quantified and normalized on the cloud platform. The box appearance parameters and module appearance parameters of the power battery can also be detected to analyze the appearance status of the power battery. Then, the performance coefficient and appearance coefficient of the battery are used as signal inputs, the membership function and fuzzy rules of the input signal are established, and the residual value of the corresponding power battery is output based on the fuzzy control algorithm, which finally realizes a reasonable evaluation of the residual value of the power battery, which can provide a strong reference basis for users and manufacturers when recycling or disassembling power batteries.

[0223] Based on the same inventive concept, the present application also provides a battery residual value evaluation device. Figure 6 The battery residual value evaluation device provided in the embodiment of the present application is described in detail.

[0224] Figure 6 A schematic structural diagram of a battery residual value evaluation device provided in one embodiment of the present application is shown.

[0225] like Figure 6 As shown, the battery residual value evaluation device may include:

[0226] Parameter acquisition module 601, used to acquire performance status parameters and appearance status parameters of the battery;

[0227] The processing module 602 is used to process the performance state of the battery based on the performance state parameter to obtain the performance coefficient of the battery, and to process the appearance state of the battery based on the appearance state parameter to obtain the appearance coefficient of the battery;

[0228] The residual value evaluation module 603 is used to evaluate the residual value of the battery based on the performance coefficient and the appearance coefficient.

[0229] Therefore, the battery performance coefficient can be obtained by processing the battery performance state based on the battery performance state parameters, and the battery appearance coefficient can be obtained by processing the battery appearance state based on the battery appearance state parameters, and then the battery residual value can be evaluated based on the performance coefficient and appearance coefficient. In this way, the battery residual value can be comprehensively and accurately evaluated from the two aspects of performance and appearance.

[0230] In some embodiments, in order to more accurately determine the performance coefficient of the battery, the processing module 602 may include:

[0231] A first determination submodule is used to determine coefficients corresponding to a plurality of performance status parameters respectively;

[0232] The normalization processing submodule is used to normalize the coefficients corresponding to multiple performance status parameters to obtain the performance coefficient of the battery

[0233] In some embodiments, the plurality of performance status parameters include the number of battery cycles. To more accurately determine the coefficient corresponding to the number of battery cycles, the first determination submodule may include:

[0234] A first calculation unit, used to calculate the difference between the calibrated battery cycle number and the battery cycle number of the battery, to obtain the remaining cycle number of the battery;

[0235] The second calculation unit is used to calculate the ratio of the remaining cycle number to the calibrated cycle number to obtain a coefficient corresponding to the battery cycle number.

[0236] In some embodiments, the plurality of performance status parameters include a remaining rated capacity. To more accurately determine a coefficient corresponding to the remaining rated capacity, the first determination submodule may include:

[0237] The third calculation unit is used to calculate the ratio of the remaining rated capacity to the calibrated rated capacity of the battery to obtain a coefficient corresponding to the remaining rated capacity.

[0238] In some embodiments, the plurality of performance status parameters include the internal resistance of the battery cell. In order to more accurately determine the coefficient corresponding to the internal resistance of the battery cell, the first determination submodule may include:

[0239] A first obtaining unit, used to obtain a first quantity of first battery cells that meet a first preset condition;

[0240] a fourth calculating unit, configured to calculate a difference between a total number of cells included in the battery and the first number to obtain a second number;

[0241] The fifth calculation unit is used to calculate the ratio of the second number to the total number to obtain a coefficient corresponding to the internal resistance of the battery cell.

[0242] In some embodiments, in order to more accurately determine the first quantity, the first obtaining unit may include:

[0243] An acquisition subunit is used to acquire the internal resistance of each battery cell corresponding to each of the multiple battery cells included in the battery;

[0244] The processing subunit is used to process the internal resistances of the cells corresponding to the multiple cells respectively to obtain an average internal resistance value;

[0245] A screening subunit, configured to screen out a first battery cell that meets a first preset condition from the multiple battery cells according to the battery cell internal resistances and the internal resistance average values ​​respectively corresponding to the multiple battery cells, wherein the first preset condition is that a difference between the battery cell internal resistance of the first battery cell and the internal resistance average value is greater than a first preset threshold value;

[0246] The determination subunit is used to determine the number of the first battery cells.

[0247] In some embodiments, the plurality of performance status parameters include the internal resistance of the battery cell. In order to more accurately determine the coefficient corresponding to the internal resistance of the battery cell, the first determination submodule may include:

[0248] The determination unit is used to determine that the coefficient corresponding to the internal resistance of any battery cell included in the battery is a first preset value when the internal resistance of the battery cell exceeds a preset range.

[0249] In some embodiments, the plurality of performance status parameters include the number of failures of a preset battery failure. In order to more accurately determine the coefficient corresponding to the number of failures, the first determination submodule may include:

[0250] a sixth calculation unit, used to calculate the difference between the calibrated fault times and the fault times of the battery, and obtain the remaining fault times of the battery;

[0251] The seventh calculation unit is used to calculate the ratio of the remaining fault times to the calibrated fault times to obtain a coefficient corresponding to the fault times.

[0252] In some embodiments, in order to more accurately normalize the coefficients corresponding to the multiple performance status parameters, the normalization processing submodule may include:

[0253] A second acquisition unit is used to acquire preset weights corresponding to the multiple coefficients respectively;

[0254] The eighth calculation unit is used to perform weighted summation on the multiple coefficients based on preset weights corresponding to the multiple coefficients to obtain the performance coefficient of the battery.

[0255] In some embodiments, in order to more quickly determine the performance coefficient of the battery, the processing module 602 may include:

[0256] An acquisition submodule, used to acquire second preset conditions corresponding to the plurality of performance status parameters respectively;

[0257] The second determination submodule is used to determine that the performance coefficient of the battery is a second preset value when there is a target performance state parameter that meets the target preset condition among the multiple performance state parameters, and the target preset condition is a second preset condition corresponding to the target performance state parameter.

[0258] In some embodiments, in order to more comprehensively and accurately determine the appearance coefficient of the battery, the appearance state parameters include the box state parameters and the module state parameters, and the processing module 602 may include:

[0259] A first processing submodule, configured to process the appearance state of a battery case based on the case state parameter to obtain a case damage coefficient, and to process the appearance state of a module of the battery based on the module state parameter to obtain a module damage coefficient;

[0260] The second processing submodule is used to process the appearance state of the battery based on the box damage coefficient and the module damage coefficient to obtain the appearance coefficient of the battery.

[0261] In some embodiments, in order to more comprehensively and accurately determine the battery case damage coefficient, the case state parameter includes at least one of whether the case has scratches, whether the positive and negative terminal wires have rust, and whether the case is dented.

[0262] In some embodiments, in order to more comprehensively and accurately determine the module damage coefficient of the battery, the module status parameter includes at least one of whether the module connector is detached, whether the module is dented, and whether there are scratches on the module surface.

[0263] In some embodiments, in order to accurately evaluate the residual value of the battery, the residual value evaluation module 603 may include:

[0264] The third determination submodule is used to determine the residual value of the battery through a fuzzy control algorithm based on the performance coefficient and the appearance coefficient.

[0265] In some embodiments, in order to further improve the accuracy of the battery residual value, the third determination submodule may include:

[0266] A fuzzification unit is used to fuzzify the performance coefficient and the appearance coefficient respectively to obtain fuzzy subsets corresponding to the performance coefficient and the appearance coefficient respectively;

[0267] A fuzzy reasoning unit is used to perform fuzzy reasoning on the fuzzy subsets corresponding to the performance coefficient and the appearance coefficient respectively based on fuzzy rules to obtain the fuzzy subset corresponding to the residual value;

[0268] The defuzzification unit is used to defuzzify the fuzzy subset corresponding to the residual value to obtain the residual value of the battery.

[0269] In some embodiments, in order to construct a fuzzy subset corresponding to the residual value that is more reasonably inferred, the fuzzy rules include:

[0270] When the performance coefficient is less than the first threshold and the appearance coefficient is less than the second threshold, determining that the residual value of the battery is less than a third threshold;

[0271] When the performance coefficient is greater than the fourth threshold and the appearance coefficient is less than the fifth threshold, determining that the residual value of the battery is between the sixth threshold and the seventh threshold;

[0272] When the performance coefficient is less than the eighth threshold and the appearance coefficient is greater than the ninth threshold, determining that the residual value of the battery is less than the tenth threshold;

[0273] When the performance coefficient is greater than the eleventh threshold and the appearance coefficient is greater than the twelfth threshold, determining that the residual value of the battery is greater than the thirteenth threshold;

[0274] The first threshold and the eighth threshold are both less than or equal to the fourth threshold, the first threshold and the eighth threshold are both less than or equal to the eleventh threshold, the second threshold and the fifth threshold are both less than or equal to the ninth threshold, the second threshold and the fifth threshold are both less than or equal to the twelfth threshold, the third threshold and the tenth threshold are both less than or equal to the sixth threshold, the thirteenth threshold is greater than or equal to the seventh threshold, and the sixth threshold is less than the seventh threshold.

[0275] In some embodiments, in order to facilitate the user to understand the price of the battery, the device may further include:

[0276] a price acquisition module, configured to acquire a price of a target battery after evaluating the residual value of the battery based on the performance coefficient and the appearance coefficient, the target battery being a battery of the same model as the battery and having a number of uses lower than a second preset threshold;

[0277] The price estimation module is used to estimate the price of the battery based on the price of the target battery and the residual value of the battery.

[0278] Figure 7 A schematic structural diagram of an electronic device provided by an embodiment of the present application is shown.

[0279] like Figure 7 As shown, the electronic device 7 is a structural diagram of an exemplary hardware architecture of an electronic device that can implement the battery residual value evaluation method and battery residual value evaluation device in the embodiment of the present application. The electronic device can refer to the electronic device in the embodiment of the present application.

[0280] The electronic device 7 may include a processor 701 and a memory 702 storing computer program instructions.

[0281] Specifically, the processor 701 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0282] The memory 702 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 702 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 702 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 702 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 702 is a non-volatile solid-state memory. In a specific embodiment, the memory 702 may include a read-only memory (ROM), a random access memory (RAM), a disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical, or other physical / tangible memory storage device. Therefore, typically, the memory 702 includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software including computer executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to one aspect of the present application.

[0283] The processor 701 implements any one of the battery residual value evaluation methods in the above embodiments by reading and executing computer program instructions stored in the memory 702 .

[0284] In one example, the electronic device may further include a communication interface 703 and a bus 704. Figure 7 As shown, the processor 701, the memory 702, and the communication interface 703 are connected via a bus 704 and communicate with each other.

[0285] The communication interface 703 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0286] Bus 704 includes hardware, software or both, and the parts of electronic equipment are coupled to each other.For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 704 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the application considers any suitable bus or interconnection.

[0287] The electronic device can execute the battery residual value evaluation method in the embodiment of the present application, thereby realizing the combination Figures 1 to 6 A method and apparatus for evaluating the residual value of a battery is described.

[0288] In addition, in combination with the battery residual value evaluation method in the above embodiment, the present application embodiment can provide a computer storage medium to implement. The computer storage medium stores computer program instructions; when the computer program instructions are executed by the processor, any of the battery residual value evaluation methods in the above embodiment is implemented.

[0289] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0290] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0291] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0292] The above reference is according to the method of the embodiment of the present application, the flow chart of the device (system) and the computer program product and / or the block diagram described various aspects of the present application.It should be understood that each square box in the flow chart and / or the block diagram and the combination of each square box in the flow chart and / or the block diagram can be realized by computer program instructions.These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the realization of the function / action specified in one or more square boxes of the flow chart and / or the block diagram.Such a processor can be but is not limited to a general-purpose processor, a special-purpose processor, a special application processor or a field programmable logic circuit.It can also be understood that each square box in the block diagram and / or the flow chart and the combination of the square boxes in the block diagram and / or the flow chart can also be realized by the dedicated hardware that performs the specified function or action, or can be realized by the combination of dedicated hardware and computer instructions.

[0293] Although the present application has been described with reference to preferred embodiments, various modifications may be made thereto and parts thereof may be replaced with equivalents without departing from the scope of the present application. In particular, the various technical features mentioned in the various embodiments may be combined in any manner as long as there is no structural conflict. The present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for evaluating the residual value of a battery, characterized in that: include: Obtain the performance status parameters and appearance status parameters of the battery; Processing the performance state of the battery based on the performance state parameter to obtain the performance coefficient of the battery, and processing the appearance state of the battery based on the appearance state parameter to obtain the appearance coefficient of the battery; The residual value of the battery is evaluated based on the performance coefficient and the appearance coefficient.

2. The method according to claim 1, characterized in that The processing of the performance state of the battery based on the performance state parameter to obtain the performance coefficient of the battery includes: Determine coefficients corresponding to a plurality of performance status parameters; The coefficients corresponding to the multiple performance status parameters are normalized to obtain the performance coefficient of the battery.

3. The method according to claim 2, characterized in that The multiple performance status parameters include the number of battery cycles, and determining a coefficient corresponding to the number of battery cycles includes: Calculating the difference between the calibrated battery cycle number of the battery and the battery cycle number to obtain the remaining cycle number of the battery; The ratio of the remaining number of cycles to the calibrated number of cycles is calculated to obtain a coefficient corresponding to the number of battery cycles.

4. The method according to claim 2, characterized in that: The multiple performance status parameters include a remaining rated capacity, and determining a coefficient corresponding to the remaining rated capacity includes: The ratio of the remaining rated capacity to the calibrated rated capacity of the battery is calculated to obtain a coefficient corresponding to the remaining rated capacity.

5. The method according to claim 2, characterized in that: The multiple performance status parameters include a cell internal resistance, and determining a coefficient corresponding to the cell internal resistance includes: Acquire a first quantity of first battery cells that meet a first preset condition; Calculate the difference between the total number of cells included in the battery and the first number to obtain a second number; The ratio of the second number to the total number is calculated to obtain a coefficient corresponding to the internal resistance of the battery cell.

6. The method according to claim 5, characterized in that The obtaining the first quantity of the first battery cells that meet the preset condition includes: Obtaining the internal resistance of each battery cell corresponding to each of the plurality of battery cells included in the battery; Processing the internal resistances of the battery cells respectively corresponding to the plurality of battery cells to obtain an average internal resistance value; According to the cell internal resistances respectively corresponding to the multiple cell sections and the internal resistance average value, a first cell satisfying the first preset condition is selected from the multiple cell sections, wherein the first preset condition is that a difference between the cell internal resistance of the first cell and the internal resistance average value is greater than a first preset threshold value; The number of the first battery cells is determined.

7. The method according to claim 2, characterized in that: The multiple performance status parameters include a cell internal resistance, and determining a coefficient corresponding to the cell internal resistance includes: When the internal resistance of any cell included in the battery exceeds a preset range, a coefficient corresponding to the internal resistance of the cell is determined to be a first preset value.

8. The method according to claim 2, characterized in that: The multiple performance status parameters include the number of failures of the battery at which a preset failure occurs, and determining a coefficient corresponding to the number of failures includes: Calculating the difference between the calibrated fault number of the battery and the fault number to obtain the remaining fault number of the battery; The ratio of the remaining fault number to the calibrated fault number is calculated to obtain a coefficient corresponding to the fault number.

9. The method according to any one of claims 2 to 8, characterized in that: The normalizing the coefficients corresponding to the plurality of performance status parameters to obtain the performance coefficient of the battery includes: Obtaining preset weights corresponding to the plurality of coefficients respectively; Based on the preset weights respectively corresponding to the multiple coefficients, the multiple coefficients are weightedly summed to obtain the performance coefficient of the battery.

10. The method according to claim 1, characterized in that The processing of the performance state of the battery based on the performance state parameter to obtain the performance coefficient of the battery includes: Obtaining second preset conditions corresponding to the plurality of performance status parameters respectively; When there is a target performance state parameter among the multiple performance state parameters that satisfies a target preset condition, the performance coefficient of the battery is determined to be a second preset value, and the target preset condition is a second preset condition corresponding to the target performance state parameter.

11. The method according to claim 1, characterized in that: The appearance state parameters include box state parameters and module state parameters, and the appearance state of the battery is processed based on the appearance state parameters to obtain the appearance coefficient of the battery, including: Processing the appearance state of the battery case based on the case state parameter to obtain a case damage coefficient, and processing the appearance state of the module of the battery based on the module state parameter to obtain a module damage coefficient; The appearance state of the battery is processed based on the box damage coefficient and the module damage coefficient to obtain the appearance coefficient of the battery.

12. The method according to claim 11, characterized in that The box state parameter includes at least one of whether the box has scratches, whether the positive and negative electrode terminal wires are rusted, and whether the box is dented.

13. The method according to claim 11, characterized in that The module status parameter includes at least one of whether the module connector is detached, whether the module is sunken, and whether there are scratches on the module surface.

14. The method according to claim 1, characterized in that The evaluating the residual value of the battery based on the performance coefficient and the appearance coefficient includes: Based on the performance coefficient and the appearance coefficient, the residual value of the battery is determined by a fuzzy control algorithm.

15. The method according to claim 14, characterized in that The determining the residual value of the battery by a fuzzy control algorithm based on the performance coefficient and the appearance coefficient includes: Fuzzifying the performance coefficient and the appearance coefficient respectively to obtain fuzzy subsets corresponding to the performance coefficient and the appearance coefficient respectively; Perform fuzzy reasoning on the fuzzy subsets corresponding to the performance coefficient and the appearance coefficient respectively based on fuzzy rules to obtain the fuzzy subset corresponding to the residual value; The fuzzy subset corresponding to the residual value is defuzzified to obtain the residual value of the battery.

16. The method according to claim 15, characterized in that The fuzzy rules include: When the performance coefficient is less than a first threshold and the appearance coefficient is less than a second threshold, determining that the residual value of the battery is less than a third threshold; When the performance coefficient is greater than a fourth threshold and the appearance coefficient is less than a fifth threshold, determining that the residual value of the battery is between a sixth threshold and a seventh threshold; When the performance coefficient is less than an eighth threshold and the appearance coefficient is greater than a ninth threshold, determining that the residual value of the battery is less than a tenth threshold; When the performance coefficient is greater than an eleventh threshold value and the appearance coefficient is greater than a twelfth threshold value, determining that the residual value of the battery is greater than a thirteenth threshold value; The first threshold and the eighth threshold are both less than or equal to the fourth threshold, the first threshold and the eighth threshold are both less than or equal to the eleventh threshold, the second threshold and the fifth threshold are both less than or equal to the ninth threshold, the second threshold and the fifth threshold are both less than or equal to the twelfth threshold, the third threshold and the tenth threshold are both less than or equal to the sixth threshold, the thirteenth threshold is greater than or equal to the seventh threshold, and the sixth threshold is less than the seventh threshold.

17. The method according to claim 1, characterized in that After evaluating the residual value of the battery based on the performance coefficient and the appearance coefficient, the method further includes: Obtaining a price of a target battery, wherein the target battery is a battery of the same model as the battery and having a usage count lower than a second preset threshold; The price of the battery is estimated based on the price of the target battery and the residual value of the battery.

18. A battery residual value evaluation device, characterized in that: The device comprises: A parameter acquisition module, used to obtain the performance status parameters and appearance status parameters of the battery; a processing module, configured to process the performance state of the battery based on the performance state parameter to obtain a performance coefficient of the battery, and to process the appearance state of the battery based on the appearance state parameter to obtain an appearance coefficient of the battery; A residual value evaluation module is used to evaluate the residual value of the battery based on the performance coefficient and the appearance coefficient.

19. An electronic device, characterized in that: The device comprises: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, the battery residual value evaluation method according to any one of claims 1 to 17 is implemented.

20. A computer storage medium, characterized in that The computer storage medium stores computer program instructions, and when the computer program instructions are executed by the processor, the battery residual value evaluation method according to any one of claims 1 to 17 is implemented.

21. A computer program product, characterized in that When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the battery residual value evaluation method as described in any one of claims 1 to 17.

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