Battery remaining life prediction method and prediction model construction method, system and equipment

Abstract

The invention provides a battery residual life prediction method and a prediction model construction method, system and device. The construction method comprises the following steps: acquiring a battery aging data set corresponding to a plurality of sample batteries; obtaining a target cycle number interval based on the battery aging data set; preprocessing the battery aging data set based on the target cycle index interval to obtain training sample data; and inputting the training sample data into a preset model to train and obtain a battery remaining life prediction model. According to the method, only the target cycle index interval needs to be determined, and the training sample data is constructed according to the charging and discharging data corresponding to the target cycle index interval, so that the sample data is greatly simplified, the model training is simpler, and meanwhile, the actual residual life of the battery can be accurately predicted.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of batteries, in particular to a battery remaining life prediction method and a prediction model construction method, system and device. BACKGROUND

[0002] Battery remaining useful life (RUL) is a key indicator for safe operation of electric vehicles and energy storage power stations, and is used to represent the remaining charge and discharge cycle times of the battery. The End of Life (EOL) of a power battery is marked by the attenuation of the battery capacity to a certain value of the rated capacity. There are various research programs for estimating the remaining life of the battery. The most commonly used semi-empirical model is used to fit the battery capacity attenuation curve, including the use of exponential models, polynomial models, etc. However, relying entirely on empirical models can cause large errors, so a certain correction is usually made according to the actual observed values, such as adding a temperature correction exponential model.

[0003] In addition, another solution is to extract battery aging parameters and establish a battery electrochemical or equivalent circuit model for analysis, to determine the battery health status and remaining life by the aging trend of the key parameters of the battery, including the growth of the solid electrolyte interface, lithium deposition, active material loss, impedance increase, etc. This solution has the advantage of being interpretable, and can determine the attenuation mechanism of the battery. At present, various heuristic algorithms have been developed for research on battery feature extraction. However, this solution relies on a relatively accurate battery model and parameter extraction algorithm. Current research can identify the parameters of a 1 Ah battery in one hour, and can reduce the RMSE (root mean square error) to 14 mv (millivolts). However, when there are a large number of batteries, the calculation is relatively large and resource-consuming.

[0004] With the continuous accumulation of real battery operation data and simulation data, various data-driven based schemes begin to be studied. For example, using the autoregressive integrated moving average (ARIMA) model to predict the state of health (SOH), a kind of life prediction model based on double unscented particle filtering (UPF) and least squares support vector machines (LSSVM) algorithm. EMD (Empirical Mode Decomposition) and grey relational analysis (GRA) are used to extract the time series characteristics of the data, and the deep RNN (recursive neural network) is input, the RUL of the battery is modeled, and the early charge-discharge data before the battery attenuation is used to estimate the RUL of the battery.

[0005] The above various schemes need a large amount of sample data to construct a network model when predicting the RUL of the ion battery, or need to spend a lot of time and resources to build an electrochemical model.

[0006] Therefore, a new battery remaining useful life prediction scheme is needed. SUMMARY

[0007] Therefore, the embodiments of the present specification provide a battery remaining useful life prediction method and a prediction model construction method, system and device, which solve the defects in the prior art that a large amount of sample data is needed to construct a network model when predicting the RUL of the battery, or a large amount of time and resources are needed to build an electrochemical model.

[0008] The embodiments of the present specification provide the following technical solutions:

[0009] In a first aspect, the embodiments of the present specification provide a construction method of a battery remaining useful life prediction model, and the construction method comprises:

[0010] Obtain a battery aging data set corresponding to a plurality of sample batteries; wherein the battery aging data set comprises: a corresponding sample remaining useful life and sample charge-discharge data of a plurality of sample batteries from the beginning of the life to the end of the life;

[0011] Obtain a target cycle interval based on the battery aging data set; the target cycle interval is the interval between two different charge-discharge cycle numbers;

[0012] preprocess the battery aging data set based on the target cycle interval to obtain training sample data;

[0013] input the training sample data into a preset model to obtain the battery remaining life prediction model through training;

[0014] The battery remaining life prediction model is used to predict the actual remaining life corresponding to the target battery capacity feature data of an actual battery input.

[0015] Further, the step of obtaining the target cycle interval based on the battery aging data set comprises:

[0016] based on the sample charge-discharge data corresponding to the sample battery, obtaining first battery capacity data corresponding to a first preset battery cycle number and second battery capacity data corresponding to a second preset battery cycle number of the sample battery;

[0017] The second preset battery cycle number is greater than the first preset battery cycle number.

[0018] obtain the first target sample battery remaining life corresponding to the second battery capacity data of the sample battery;

[0019] The target cycle interval is calculated based on the first battery capacity data, the second battery capacity data, and the first target sample battery remaining life.

[0020] Further, the step of calculating the target cycle interval based on the first battery capacity data, the second battery capacity data, and the first target sample battery remaining life comprises:

[0021] calculate the capacity difference between the second battery capacity data and the first battery capacity data;

[0022] take the variance of the capacity difference to obtain sample voltage data;

[0023] based on the sample voltage data and the first target sample battery remaining life, obtain a Pearson correlation coefficient using a Pearson correlation coefficient method;

[0024] When the absolute value of the Pearson correlation coefficient is greater than a preset correlation value, the current cycle interval is determined as the target cycle interval.

[0025] Further, the step of preprocessing the battery aging data set based on the target cycle interval to obtain training sample data comprises:

[0026] obtaining a preset voltage interval, a third preset battery cycle number and a fourth preset battery cycle number corresponding to the sample battery;

[0027] The fourth preset battery cycle number is obtained based on a sum of the third preset battery cycle number and the target cycle number interval.

[0028] The preset voltage interval is divided based on a preset voltage interval, and third battery capacity data and fourth battery capacity data corresponding to the divided preset voltage interval are obtained.

[0029] The third battery capacity data is capacity data of the sample battery corresponding to the third preset battery cycle number, and the fourth battery capacity data is capacity data of the sample battery corresponding to the fourth preset battery cycle number.

[0030] Based on the third battery capacity data and the fourth battery capacity data, sample battery capacity feature data of a plurality of preset dimensions of the sample battery is obtained.

[0031] Based on the sample battery capacity feature data and the second target sample battery remaining life corresponding to the fourth battery capacity data of the sample battery, the training sample data is obtained.

[0032] Further, the preset model includes a Gradient Boosting Decision Tree (GBDT) model.

[0033] And / or, the battery capacity corresponding to the process of the plurality of sample batteries from the beginning of the life to the end of the life decays from the rated capacity to 80% of the rated capacity.

[0034] And / or, the battery aging data set includes the remaining life of the sample battery under the constant current charging and variable frequency discharging working condition.

[0035] Further, the preset voltage interval is within the full charge and full discharge voltage interval of the sample battery.

[0036] And / or, the target cycle number interval includes a target charging cycle number interval or a target discharging cycle number interval.

[0037] In a second aspect, the embodiments of the present specification also provide a battery remaining life prediction method, the method comprising:

[0038] Obtaining initial charging and discharging data corresponding to an actual battery;

[0039] Pretreating the initial charging and discharging data to obtain target battery capacity feature data.

[0040] input the target battery capacity feature data into a battery residual life prediction model to output an actual residual life corresponding to the actual battery;

[0041] The battery residual life prediction model is constructed based on the method for constructing a battery residual life prediction model.

[0042] In a third aspect, the embodiments of the present specification also provide a system for constructing a battery residual life prediction model, and the system comprises:

[0043] a data set acquisition module configured to acquire battery aging data sets corresponding to a plurality of sample batteries; wherein the battery aging data sets comprise sample residual life and sample charge-discharge data corresponding to the plurality of sample batteries from the beginning of the life to the end of the life;

[0044] a cycle interval acquisition module configured to acquire a target cycle interval based on the battery aging data sets; the target cycle interval is an interval between two different charge-discharge cycle numbers;

[0045] a sample data acquisition module configured to preprocess the battery aging data sets based on the target cycle interval to acquire training sample data;

[0046] a model training module configured to input the training sample data into a preset model to train the battery residual life prediction model;

[0047] The battery residual life prediction model is configured to predict an actual residual life corresponding to target battery capacity feature data of an actual battery inputted.

[0048] Further, the cycle interval acquisition module comprises:

[0049] a first capacity data acquisition unit configured to acquire first battery capacity data corresponding to a first preset battery cycle number and second battery capacity data corresponding to a second preset battery cycle number based on the sample charge-discharge data corresponding to the sample batteries;

[0050] The second preset battery cycle number is greater than the first preset battery cycle number.

[0051] a residual life acquisition unit configured to acquire a first target sample battery residual life corresponding to the second battery capacity data of the sample batteries;

[0052] a cycle interval acquisition unit configured to calculate the target cycle interval based on the first battery capacity data, the second battery capacity data, and the first target sample battery residual life.

[0053] Further, the cycle interval acquisition unit is configured to calculate a capacity difference value of the second battery capacity data and the first battery capacity data;

[0054] Logarithmize a variance of the capacity difference value to obtain sample voltage data;

[0055] Using a Pearson correlation coefficient method, the target cycle interval is calculated based on the sample voltage data and the first target sample battery remaining life.

[0056] Further, the sample data acquisition module comprises:

[0057] A preset voltage interval acquisition unit is configured to obtain a preset voltage interval corresponding to the sample battery, a third preset battery cycle number and a fourth preset battery cycle number;

[0058] The fourth preset battery cycle number is obtained based on a sum of the third preset battery cycle number and the target cycle interval.

[0059] A second capacity data acquisition unit is configured to divide the preset voltage interval based on a preset voltage interval, and obtain third battery capacity data and fourth battery capacity data corresponding to the divided preset voltage interval;

[0060] The third battery capacity data is a capacity data corresponding to the third preset battery cycle number of the sample battery, and the fourth battery capacity data is a capacity data corresponding to the fourth preset battery cycle number of the sample battery.

[0061] A feature data acquisition unit is configured to obtain sample battery capacity feature data of a plurality of preset dimensions of the sample battery based on the third battery capacity data and the fourth battery capacity data.

[0062] A training sample data acquisition unit is configured to obtain the training sample data based on the sample battery capacity feature data and a second target sample battery remaining life corresponding to the fourth battery capacity data of the sample battery.

[0063] Further, the preset model comprises a GBDT model.

[0064] And / or, the battery capacity corresponding to the process from the beginning of the sample battery life to the end of the sample battery life decays from the rated capacity to 80% of the rated capacity.

[0065] And / or, the battery aging data set comprises the remaining life of the sample battery under the constant current charging and variable frequency discharging working condition.

[0066] Further, the preset voltage interval is within a full charge full discharge voltage interval of the sample battery.

[0067] Further, the target cycle number interval includes a target charge cycle number interval or a target discharge cycle number interval.

[0068] In a fourth aspect, the embodiments of the present specification also provide a battery remaining life prediction system, the system comprising:

[0069] An initial data acquisition module is configured to acquire initial charge-discharge data corresponding to an actual battery.

[0070] A target data acquisition module is configured to preprocess the initial charge-discharge data to obtain target battery capacity feature data.

[0071] A prediction module is configured to input the target battery capacity feature data into a battery remaining life prediction model to output an actual remaining life corresponding to the actual battery.

[0072] The battery remaining life prediction model is constructed based on the construction system of the battery remaining life prediction model as described above.

[0073] In a fifth aspect, the embodiments of the present specification also provide an electronic device, comprising a memory, a processor, and a computer program stored on the memory and configured to run on the processor, wherein the processor executes the computer program to implement the construction method of the battery remaining life prediction model or the battery remaining life prediction method as described above.

[0074] In a sixth aspect, the embodiments of the present specification also provide a computer readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the construction method of the battery remaining life prediction model or the battery remaining life prediction method as described above.

[0075] On the basis of conforming to the common knowledge in the art, the above-mentioned preferred conditions can be combined arbitrarily, i.e., to obtain each preferred example of the present application.

[0076] Compared with the prior art, the above-mentioned at least one technical solution adopted by the embodiments of the present specification can achieve at least the following beneficial effects:

[0077] The application obtains a target cycle number interval through a battery aging data set, pre-processes the battery aging data set based on the target cycle number interval, obtains training sample data, inputs the training sample data into a preset model, trains to obtain a battery remaining life prediction model, and predicts an actual remaining life corresponding to an actual battery through the battery remaining life prediction model. Only the target cycle number interval needs to be determined, training sample data is constructed according to the charge-discharge data corresponding to the target cycle number interval, the sample data is greatly simplified, the whole model training is simpler, and the actual remaining life of the battery can be accurately predicted. BRIEF DESCRIPTION OF DRAWINGS

[0078] In order to more clearly illustrate the technical solutions of the embodiments of the application, the drawings needed to be used in the embodiments will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0079] Figure 1 FIG. 1 is a first flowchart of a battery remaining life prediction model construction method provided by Embodiment 1 of the application;

[0080] Figure 2 FIG. 2 is a second flowchart of the battery remaining life prediction model construction method provided by Embodiment 1 of the application;

[0081] Figure 3 FIG. 3 is a third flowchart of the battery remaining life prediction model construction method provided by Embodiment 1 of the application;

[0082] Figure 4 FIG. 4 is a schematic diagram of a corresponding relationship between sample voltage data and a first target sample battery remaining life in the battery remaining life prediction model construction method provided by Embodiment 1 of the application;

[0083] Figure 5 FIG. 5 is a fourth flowchart of the battery remaining life prediction model construction method provided by Embodiment 1 of the application;

[0084] Figure 6 FIG. 6 is a schematic diagram of a prediction result in the battery remaining life prediction model construction method provided by Embodiment 1 of the application;

[0085] Figure 7 FIG. 7 is a schematic diagram of a prediction result error in the battery remaining life prediction model construction method provided by Embodiment 1 of the application;

[0086] Figure 8 FIG. 8 is a flowchart of a battery remaining life prediction method provided by Embodiment 2 of the application;

[0087] Figure 9 FIG. 1 shows a schematic diagram of a structure of a system for constructing a battery remaining life prediction model according to an embodiment of the present application;

[0088] Figure 10 FIG. 2 shows a schematic diagram of a structure of a battery remaining life prediction system according to an embodiment of the present application;

[0089] Figure 11 FIG. 3 shows a schematic diagram of a structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0090] The embodiments of the present application will be described in detail with reference to the drawings, wherein:

[0091] The above embodiments are only some of the embodiments of the present application, but not all of them. Based on the embodiments of the present application, any person skilled in the art can obtain all other embodiments of the present application without creative effort, which shall all fall within the scope of the present application.

[0092] It should be noted that various aspects of the embodiments described below are within the scope of the claims. It should be apparent that the aspects described herein can be implemented in a wide variety of forms, and that any specific structure and / or functions described herein are merely illustrative. Based on the teachings herein one skilled in the art should be able to implement or use the aspects described herein without undue effort. The aspects described herein can be implemented in any of a variety of ways.

[0093] It should also be noted that the drawings included in the present disclosure are included to illustrate potentially complex structures associated with the application. However, the drawings are included to add clarity and conciseness to the detailed description drawn to the application, and are included as the best known embodiment of the application. However, the application can be practiced by using alternative structures and / or methods in some instances, and / or by using structures and / or methods that are different from those described to the extent that such alternative structures and / or methods would fall within the scope of the present application. Also, the included drawings are included to illustrate some potential uses of the application. However, the drawings are included to add clarity and conciseness to the detailed description drawn to the application, and are included as the best known embodiment of the application. However, the application can be practiced by using alternative structures and / or methods in some instances, and / or by using structures and / or methods that are different from those described to the extent that such alternative structures and / or methods would fall within the scope of the present application.

[0094] Also in the following description, specific details are given to provide thorough understanding of examples. However, one skilled in the relevant art will understand that the examples can be practiced without these specific details.

[0095] The inventors have found through research and improvement exploration that the existing battery remaining life prediction technology has a fundamental contradiction between data demand and prediction efficiency: a model based on full life cycle data driving needs a large amount of samples, resulting in complex and time-consuming training, while a model relying on electrochemical mechanism is difficult to be practical due to huge parameter identification calculation amount.

[0096] Based on this, the embodiments of the present specification propose a battery remaining life prediction scheme: the overall idea is: by digging the essential law of battery aging data, it is found that the capacity change of a specific cycle interval (target cycle interval N) is strongly related to the remaining life, and then only the capacity difference of two key cycle nodes (such as the 10th and 100th cycles) is extracted in the full charge and discharge voltage interval according to the fine voltage interval (such as 1mV), and a low-dimensional high information density feature vector is constructed; input this simplified feature into a lightweight GBDT model for training, and construct a data amount of less than 1% to replace the full cycle data, while significantly reducing the calculation resources and data dependence, and realizing high-precision battery remaining life prediction, breaking the shackles of traditional technology that high precision must rely on big data.

[0097] The technical solutions provided by the embodiments of the present application are described below with reference to the accompanying drawings.

[0098] Embodiment 1

[0099] The present embodiment provides a method for constructing a battery remaining life prediction model, as shown in Figure 1 The construction method comprises:

[0100] S1, obtaining a battery aging data set corresponding to a plurality of sample batteries. The battery aging data set includes the sample remaining life and the sample charge-discharge data corresponding to the sample battery from the beginning of the life to the end of the life.

[0101] S2, obtaining a target cycle interval based on the battery aging data set; the target cycle interval is the interval between two different charge-discharge cycle times.

[0102] S3, preprocessing the battery aging data set based on the target cycle interval to obtain training sample data.

[0103] S4, inputting the training sample data into a preset model to train and obtain a battery remaining life prediction model.

[0104] The battery remaining life prediction model is used to predict the actual remaining life corresponding to the target battery capacity feature data of the input actual battery.

[0105] The battery remaining life is usually characterized by the remaining charge-discharge cycle number of the battery, and the sample charge-discharge data generally corresponds to the charge-discharge curve data, including the charge-discharge cycle number of the sample battery from the beginning of the life to the end of the life, and the charge-discharge data (such as voltage, current, time, battery capacity, etc.) corresponding to each cycle number.

[0106] The target cycle number interval corresponds to the interval between two different charge cycle numbers or the interval between two different discharge cycle numbers; and the training sample data is constructed according to the charge data of the two charge cycles or the discharge data of the two discharge cycles corresponding to the target cycle number interval.

[0107] Specifically, the process of the sample battery from the beginning of the life to the end of the life can correspond to the attenuation of the battery capacity from the rated capacity to 80% of the rated capacity.

[0108] The existing RUL prediction based on the network model needs a large amount of battery aging data to construct sample data, and the sample data usually needs to cover the data from the beginning of the life to the end of the life of the battery, which has a large amount of data and a complex model training process.

[0109] The construction method of the battery remaining life prediction model of the embodiment only needs to determine the target cycle number interval, and construct the training sample data according to the charge-discharge data corresponding to the target cycle number interval, which greatly simplifies the sample data, makes the whole model training more simple, and can accurately predict the actual remaining life of the battery.

[0110] In an optional embodiment, as shown in Figure 2 The above step S2 includes:

[0111] S21, based on the sample charge-discharge data corresponding to the sample battery, obtaining first battery capacity data corresponding to a first preset battery cycle number of the sample battery, and second battery capacity data corresponding to a second preset battery cycle number of the sample battery.

[0112] The second preset battery cycle number is greater than the first preset battery cycle number.

[0113] S22, obtaining the first target sample battery remaining life corresponding to the second battery capacity data of the sample battery.

[0114] S23, calculating the target cycle number interval based on the first battery capacity data, the second battery capacity data and the first target sample battery remaining life.

[0115] Each battery cycle number has corresponding sample charge-discharge data, and the sample charge-discharge data includes the battery capacity. The battery has a corresponding battery remaining life (i.e. RUL) at a certain battery capacity.

[0116] For example, the first preset battery cycle number of a sample battery is i, the first battery capacity data is Qi, the battery remaining life corresponding to the first battery capacity data Qi is Ri', the second preset battery cycle number is i+n, where n is the interval between the second preset battery cycle number and the first preset battery cycle number, n is a positive integer, the second battery capacity data is Qi+n, and the battery remaining life corresponding to the second battery capacity data Qi+n is Ri.

[0117] The selection of i is flexible, and i can be selected according to the actual use condition of the sample battery. The value of i should be within the charge-discharge cycle number during the period from the beginning of the life to the end of the life of the sample battery. The selection of n is also flexible, but should ensure that i+n is within the charge-discharge cycle number during the period from the beginning of the life to the end of the life of the sample battery.

[0118] The first target sample battery remaining life of the sample battery is Ri. The target cycle number interval N can be calculated according to the first battery capacity data Qi, the second battery capacity data Qi+n, and the first target sample battery remaining life Ri.

[0119] In an optional embodiment, as shown in Figure 3 The step S23 includes:

[0120] S231, calculating the capacity difference between the second battery capacity data and the first battery capacity data.

[0121] S232, taking the logarithm of the variance of the capacity difference to obtain sample voltage data.

[0122] S233, using the Pearson correlation coefficient method, based on the sample voltage data and the first target sample battery remaining life, to obtain a Pearson correlation coefficient;

[0123] When the absolute value of the Pearson correlation coefficient is greater than a preset correlation value, the current cycle number interval is determined as the target cycle number interval.

[0124] Battery capacity attenuation is a slow process. The target cycle number interval, i.e., the optimal cycle interval, needs to ensure that there is enough cycle data to effectively predict the RUL, and this cycle number interval needs to be shortened as much as possible.

[0125] For example, the first preset battery cycle number of a sample battery is i, the first battery capacity data is Qi, and the second battery capacity data is Qi+n. The sample voltage data Li=log(Var(Q i+n -Q i) ; the first target sample battery remaining life is Ri; the sample voltage data and the first target sample battery remaining life are taken as variables to calculate a Pearson correlation coefficient r, since i and n can be flexibly selected, for a given i, as n continuously increases, the correlation r also continuously increases, when |r| > 0.7, n obtained is considered as an optimal cycle interval point, that is, a target cycle interval N corresponding to the entire sample battery is obtained, and the correlation formula is as follows:

[0126]

[0127] wherein, the mean of the sample voltage data corresponding to the entire sample battery is represented by R, and the mean of the sample battery remaining life corresponding to the entire sample battery is represented by R.

[0128] For example, if the number of charge and discharge cycles of the sample battery during the period from the beginning of the life to the end of the life is 1-2000 cycles, the value of i can be 1-10, the value of n can be 60-100, and the target cycle interval N finally calculated can be 90.

[0129] In a specific example, i is 10, r calculated is -0.879, 0.7 < |r| ≤ 0.9 represents strong correlation, and N is 90, that is, the first preset battery cycle number is the 10th cycle, the second preset battery cycle number is the 100th cycle, and it is indicated that the charge or discharge data in the interval of the 10th and the 100th cycle is taken.

[0130] Figure 4 A corresponding relationship diagram of the sample voltage data and the first target sample battery remaining life is shown in FIG. 2, wherein the abscissa represents the sample voltage data, the ordinate represents the first target sample battery remaining life, different sample voltage data has corresponding first target sample battery remaining life, and the Pearson correlation coefficient r is calculated by taking the sample voltage data and the first target sample battery remaining life as variables. Figure 4 In the Pearson correlation coefficient method, the Pearson correlation coefficient r is -0.877, indicating that the target cycle interval and the battery remaining life have strong correlation, which reflects the rationality of the target cycle interval, and the battery remaining life can be accurately predicted subsequently.

[0131] The construction method of the battery remaining life prediction model of the embodiment can accurately calculate the target cycle interval by the Pearson correlation coefficient method, and then construct the training sample data, greatly simplify the sample data, make the whole model training more simple, and accurately predict the actual battery remaining life.

[0132] In an optional embodiment, as shown in FIG. 3, the step S3 includes: Figure 5

[0133] ​S31, obtain a preset voltage interval corresponding to the sample battery, a third preset battery cycle number and a fourth preset battery cycle number.

[0134] The fourth preset battery cycle number is obtained based on a sum of the third preset battery cycle number and a target cycle number interval.

[0135] S32, divide the preset voltage interval based on a preset voltage interval, and obtain third battery capacity data and fourth battery capacity data corresponding to the divided preset voltage interval.

[0136] The third battery capacity data is capacity data of the sample battery corresponding to the third preset battery cycle number, and the fourth battery capacity data is capacity data of the sample battery corresponding to the fourth preset battery cycle number.

[0137] S33, obtain sample battery capacity feature data of a plurality of preset dimensions of the sample battery based on the third battery capacity data and the fourth battery capacity data.

[0138] S34, obtain training sample data based on the sample battery capacity feature data and a second target sample battery remaining life corresponding to the fourth battery capacity data of the sample battery.

[0139] In a specific example, the preset voltage interval is 2.5V-3.3V (volt), and the preset voltage interval is 1mv. According to 1mv, 2.5V-3.3V is divided into 800 voltage intervals, i.e. the preset dimension is 800; if the target cycle number interval N is 90, the third preset battery cycle number is the 10th cycle, and the fourth preset battery cycle number is 10+90=100, i.e. the 100th cycle. For the bth sample battery, the third battery capacity data is Q10, and the fourth battery capacity data is Q100. According to the 800 voltage intervals, 800 Q 100 -Q 10 , 800 Q 100 -Q 10 are obtained, and vectorization processing is performed to obtain sample battery capacity feature data x b with 800 dimensions, the battery remaining life corresponding to the fourth battery capacity data Q100 of the sample battery is the second target sample battery remaining life, which is marked as y b , the bth training sample (x b , y b ) is constructed, where b=1, 2, …, p, p is the total number of sample batteries, and a training sample data set consisting of p sample batteries is obtained, i.e. training sample data.

[0140] If the third preset battery cycle number is the first cycle, the fourth preset battery cycle number is 1+90=91.

[0141] The third preset battery cycle number can be flexibly selected, and the value thereof should be in the number of charge-discharge cycles during the period from the beginning of the service life to the end of the service life of the sample battery. The fourth preset battery cycle number is obtained based on the sum of the third preset battery cycle number and the target cycle number interval, that is, the cycle interval between the fourth preset battery cycle number and the third preset battery cycle number is ensured to be the target cycle number interval.

[0142] The preset dimension is determined based on the preset voltage interval and the preset voltage interval.

[0143] The sample battery can be a battery of the same specification parameter, and the battery of the same specification parameter corresponds to the same charge-discharge voltage interval and the same target cycle number interval N.

[0144] The target cycle number interval N corresponding to the battery of different specification parameters will be different due to the influence of manufacturing process and specification parameters, and the corresponding charge-discharge voltage interval should be within the preset voltage interval; but the target cycle number interval N can be calculated by the above method, and the target cycle number interval N at this time can be the average of a plurality of target cycle number intervals N.

[0145] The above data is only exemplary and does not limit the protection scope of the present disclosure, and a person skilled in the art can flexibly select according to actual needs.

[0146] The construction method of the battery remaining life prediction model of the embodiment can accurately calculate the target cycle number interval by the Pearson correlation coefficient method, process the preset voltage interval by the preset voltage interval, the preset voltage interval and the target cycle number interval, obtain a plurality of sample battery capacity feature data corresponding to the preset dimension, and then construct a training sample data set based on the sample battery capacity feature data and the second target sample battery remaining life. On the basis of simplifying the sample data, the multi-dimensional information of the sample data is ensured, which greatly simplifies the sample data, makes the whole model training simpler, and at the same time can accurately predict the actual remaining life of the battery.

[0147] In an optional embodiment, the preset model includes but is not limited to a GBDT model.

[0148] The construction method of the battery remaining life prediction model of the present disclosure will be discussed in detail below taking the preset model as the GBDT model as an example.

[0149] The bth training sample is (x b , y b), where b = 1, 2, ... p, p is the total number of sample batteries, and the training sample data consisting of p sample batteries is

[0150] Step 1: Initialize the GBDT model by minimizing the loss function:

[0151]

[0152] Among them, h 0 is the predicted value of the GBDT model. When the loss function is the mean square error, the mean of RUL of all training samples is taken. 0 represents the initialization of the GBDT model, ρ represents the weight of this initialization GBDT model, K(y b ,h 0 ) represents the distance between the predicted value and the true value of the GBDT initial model, and x represents the input of the GBDT model, i.e., the aforementioned sample battery capacity characteristic data.

[0153] Step 2: Assume that the training is for M rounds. For the mth round, m=1,2,...,M, for each training sample (x b ,y b ) Calculate the negative gradient r bm , where b = 1, 2, ... p, where:

[0154]

[0155] where r bm It is the gradient of the real value and the predicted value in the previous round. When the loss function is the mean square error, r bm That is the residual, E(y b ,f m-1 (x b )) represents the GBDT model prediction value f learned in the m-1th round m-1 (x b ) and the true value y b The distance is expressed in the same way as the distance K(y b ,h 0 ) indicates agreement, and the recommendations are both K, f m-1 (x b ) represents the GBDT model learned in the previous round (m-1) of the mth round.

[0156] Step 3: Use the residual obtained above as the label for the next round of training, and get a new training sample (r bm ,y b ), b=1,2,...,p; fit a new regression tree h with this sample m (x), where h m(x) is the prediction value of the mth round of GBDT model, and the weight p of the mth round of GBDT model is calculated m wherein:

[0157]

[0158] The model is updated to obtain:

[0159] f m (x) = f m-1 (x) + v p m h m (x) ;

[0160] wherein, v is a learning rate (also known as a step size), which is a positive number less than 1, used to control the update amplitude of each step to prevent overfitting of the model.

[0161] The training steps 2-3 are repeated to obtain a final battery remaining life prediction model;

[0162]

[0163] wherein, F(x) is the actual remaining life predicted by the battery remaining life prediction model, and x is the target battery capacity feature data corresponding to the actual battery. The repeated stopping condition can be a fixed M rounds, or when the loss function is less than a certain value, or when the loss function does not decrease for several consecutive rounds, the training is stopped.

[0164] Figure 6 The actual remaining life predicted by the battery remaining life prediction model of the present disclosure is shown in comparison with the actual remaining life, wherein the horizontal coordinate is the battery number in the test set, and the vertical coordinate is the actual remaining life of the battery. The circle point (predict) represents the actual remaining life predicted by the battery remaining life prediction model, and x (true) represents the actual remaining life. The actual remaining life RUL of the battery can be predicted by the battery remaining life prediction model. As can be seen from the comparison, the actual remaining life of most of the batteries in the test set differs little from the actual remaining life predicted by the battery remaining life prediction model of the present disclosure, and some overlap occurs, which reflects the accuracy of the battery remaining life prediction model of the present disclosure.

[0165] The battery remaining life prediction model of the present disclosure can output the actual remaining life of the actual battery, for example, the predicted actual remaining life of a certain battery is 1500 cycles.

[0166] Figure 7 The error of the predicted actual remaining life of the battery remaining life prediction model of the present disclosure is shown in comparison with the existing ElasticNET (ElasticNET) model. Figure 7In the figure, the abscissa is the battery number in the test set, the ordinate is the actual remaining life of the battery, GBDT_Error represents the single error of the actual remaining life predicted by the battery remaining life prediction model of the present disclosure, GBDT_Mean_Error represents the average error of the actual remaining life predicted by the battery remaining life prediction model of the present disclosure; ElasticNET_Error represents the single error of the actual remaining life predicted by the ElasticNET model, and ElasticNET_Mean_Error represents the average error of the actual remaining life predicted by the ElasticNET model. It can be seen from the comparison that the prediction effect of the battery remaining life prediction model of the present disclosure is better than that of the ElasticNET model, and the average error of the RUL is reduced to below 100 cycles, and the average error ratio is about 11%, which is 34.2% higher than the accuracy of the ElasticNET model.

[0167] The preset model of the present disclosure can also be other models as long as numerical prediction can be realized.

[0168] In an optional embodiment, the battery aging data set includes the remaining life of the sample battery under the constant current charging and variable frequency discharging working condition.

[0169] The battery aging data set can be constructed according to the real use scene. In the field of electric vehicles, the working condition is stable when the electric vehicle is charging, and the working condition is complex during discharging, that is, the working condition of the lithium battery is mainly constant current charging and variable frequency discharging. For example, when constructing the battery aging data set, the charging process uses constant current 0.5C charging, and the discharging can use different rates and different steps to facilitate accurate prediction of the remaining life of the lithium battery of the electric vehicle.

[0170] In an optional embodiment, the preset voltage interval is within the full charging and discharging voltage interval of the sample battery.

[0171] If the sample battery is a battery with the same specification parameters, it corresponds to the same full charging and discharging voltage. At this time, it should be ensured that the preset voltage interval is within the full charging and discharging voltage interval of the sample battery. If the sample battery is a battery with different specification parameters, it corresponds to different full charging and discharging voltages. At this time, it should also be ensured that the preset voltage interval is within the full charging and discharging voltage interval of the sample battery. Since the full charging and discharging voltage interval is particularly important for battery remaining life prediction, when determining the preset voltage interval, it is necessary to ensure that the preset voltage interval is within the full charging and discharging voltage interval.

[0172] In an optional embodiment, the target cycle number interval includes a target charging cycle number interval or a target discharging cycle number interval.

[0173] The battery aging dataset can correspond to the charging conditions of the sample battery. In this case, the target cycle interval corresponds to the target charging cycle interval. Training sample data is constructed based on the data of the sample battery during the charging period, thereby predicting the actual remaining life of the actual battery.

[0174] The battery aging dataset can correspond to the discharge conditions of the sample battery. In this case, the target cycle interval corresponds to the target discharge cycle interval. Training sample data is constructed based on the data of the sample battery during the discharge period, thereby predicting the actual remaining life of the actual battery.

[0175] Example 2

[0176] Based on the same inventive concept, this application also provides a method for predicting the remaining life of a battery, such as Figure 8 As shown, the method includes:

[0177] S5. Obtain initial charge and discharge data corresponding to the actual battery.

[0178] S6. Preprocess the initial charge and discharge data to obtain target battery capacity characteristic data.

[0179] S7. Input the target battery capacity characteristic data into the battery remaining life prediction model to output the actual remaining life corresponding to the actual battery.

[0180] The remaining battery life prediction model is constructed based on the method for constructing the remaining battery life prediction model in Example 1.

[0181] Specifically, the same preprocessing method as that used for the battery aging data set in the model building process in Example 1 is adopted to preprocess the initial charge and discharge data corresponding to the actual battery to obtain target battery capacity characteristic data of several preset dimensions.

[0182] For example, if the sample battery capacity characteristic data of the preset dimensions in the model building process corresponds to 800 dimensions, then the target battery capacity characteristic data of the preset dimensions in this embodiment also corresponds to 800 dimensions, ensuring that the input data in the model building process and the model use process correspond to each other.

[0183] The battery remaining life prediction method of this embodiment predicts the battery remaining life based on the model constructed by the battery remaining life prediction model construction method in Example 1, and can accurately predict the actual remaining life of the battery.

[0184] Example 3

[0185] Based on the same inventive concept, the present application also provides a system for constructing a battery remaining life prediction model, such as Figure 9 As shown, the build system includes:

[0186] a data set obtaining module 1 configured to obtain battery aging data sets corresponding to a plurality of sample batteries; wherein the battery aging data sets comprise sample remaining life and sample charge-discharge data corresponding to the plurality of sample batteries from the beginning of the life span to the end of the life span;

[0187] a cycle interval obtaining module 2 configured to obtain a target cycle interval based on the battery aging data sets; the target cycle interval is an interval between two different charge-discharge cycle numbers;

[0188] a sample data obtaining module 3 configured to preprocess the battery aging data sets based on the target cycle interval to obtain training sample data;

[0189] a model training module 4 configured to input the training sample data into a preset model to train a battery remaining life prediction model.

[0190] The battery remaining life prediction model is configured to predict an actual remaining life corresponding to target battery capacity feature data of an actual battery inputted.

[0191] In an optional embodiment, the cycle interval obtaining module 2 comprises:

[0192] a first capacity data obtaining unit 21 configured to obtain first battery capacity data corresponding to a first preset battery cycle number and second battery capacity data corresponding to a second preset battery cycle number based on sample charge-discharge data corresponding to the sample batteries;

[0193] The second preset battery cycle number is greater than the first battery cycle number.

[0194] a remaining life obtaining unit 22 configured to obtain a first target sample battery remaining life corresponding to the second battery capacity data of the sample batteries;

[0195] a cycle interval obtaining unit 23 configured to calculate the target cycle interval based on the first battery capacity data, the second battery capacity data and the first target sample battery remaining life.

[0196] In an optional embodiment, the cycle interval obtaining unit 23 is configured to calculate a capacity difference value of the second battery capacity data and the first battery capacity data; take a logarithm of a variance of the capacity difference value to obtain sample voltage data; and calculate the target cycle interval based on the sample voltage data and the first target sample battery remaining life by using a Pearson correlation coefficient method.

[0197] In an optional embodiment, the sample data obtaining module 3 comprises:

[0198] The preset voltage interval acquisition unit 31 is configured to acquire a preset voltage interval corresponding to the sample battery, a third preset battery cycle number and a fourth preset battery cycle number.

[0199] The fourth preset battery cycle number is obtained based on a sum of the third preset battery cycle number and a target cycle number interval.

[0200] The second capacity data acquisition unit 32 is configured to divide the preset voltage interval based on a preset voltage interval, and acquire third battery capacity data and fourth battery capacity data corresponding to the divided preset voltage interval.

[0201] The third battery capacity data is capacity data of the sample battery corresponding to the third preset battery cycle number, and the fourth battery capacity data is capacity data of the sample battery corresponding to the fourth preset battery cycle number.

[0202] The feature data acquisition unit 33 is configured to obtain sample battery capacity feature data of a plurality of preset dimensions of the sample battery based on the third battery capacity data and the fourth battery capacity data.

[0203] The training sample data acquisition unit 34 is configured to obtain training sample data based on the sample battery capacity feature data and second target sample battery remaining life corresponding to the fourth battery capacity data of the sample battery.

[0204] In an optional implementation, the preset model comprises a GBDT model.

[0205] In an optional implementation, the process of the plurality of sample batteries from the beginning of the life to the end of the life corresponds to the attenuation of the battery capacity from the rated capacity to 80% of the rated capacity.

[0206] In an optional implementation, the battery aging data set comprises the remaining life of the sample battery under the constant current charging and variable frequency discharging working condition.

[0207] In an optional implementation, the preset voltage interval is within the full charging and full discharging voltage interval of the sample battery.

[0208] In an optional implementation, the target cycle number interval comprises a target charging cycle number interval or a target discharging cycle number interval.

[0209] The construction system of the battery remaining life prediction model of the embodiment corresponds to the construction method of the battery remaining life prediction model of the embodiment 1, the working principle of the construction system is the same as the working principle of the construction method, and the specific content will not be described here.

[0210] The construction system of the battery residual life prediction model of the embodiment obtains a target cycle number interval through a battery aging data set, pre-processes the battery aging data set based on the target cycle number interval, obtains training sample data, inputs the training sample data into a preset model, trains to obtain a battery residual life prediction model, and predicts an actual residual life corresponding to an actual battery through the battery residual life prediction model. Only the target cycle number interval needs to be determined, and the training sample data is constructed according to the charge-discharge data corresponding to the target cycle number interval, which greatly simplifies the sample data, makes the whole model training simpler, and can accurately predict the actual residual life of the battery.

[0211] Embodiment 4

[0212] Based on the same inventive concept, the application also provides a battery residual life prediction system, as shown in the structural schematic diagram of the battery residual life prediction system. Figure 10 The system comprises:

[0213] An initial data acquisition module 5 is configured to acquire initial charge-discharge data corresponding to an actual battery.

[0214] A target data acquisition module 6 is configured to pre-process the initial charge-discharge data to obtain target battery capacity feature data.

[0215] A prediction module 7 is configured to input the target battery capacity feature data into a battery residual life prediction model to output an actual residual life corresponding to the actual battery.

[0216] The battery residual life prediction model is constructed based on the construction system of the battery residual life prediction model of embodiment 3.

[0217] The battery residual life prediction system of the embodiment corresponds to the battery residual life prediction method of embodiment 2, and the working principle of the battery residual life prediction system is the same as that of the battery residual life prediction method, which will not be described here.

[0218] The battery residual life prediction system of the embodiment is constructed based on the model constructed by the construction system of the battery residual life prediction model in embodiment 3, and can accurately predict the actual residual life of the battery.

[0219] Embodiment 5

[0220] Based on the same inventive concept, the application also provides an electronic device, Figure 11 A structural schematic diagram of an electronic device is provided for the embodiment. The electronic device comprises a memory, a processor, and a computer program stored in the memory and used for executing on the processor. When the processor executes the program, the construction method of the battery residual life prediction model in embodiment 1 or the battery residual life prediction method in embodiment 2 is implemented. Figure 11 The electronic device 80 shown is merely one example and should not be taken as limiting the scope of functionality or use of embodiments of the disclosure.

[0221] As shown in Figure 11 FIG. 8, the electronic device 80 can be in the form of a general computing device, such as a server device. Components of the electronic device 80 can include, but are not limited to, the at least one processor 81 described above, the at least one memory 82 described above, and a bus 83 that connects the different system components, including the memory 82 and the processor 81.

[0222] The bus 83 includes a data bus, an address bus, and a control bus.

[0223] The memory 82 can include volatile memory, such as random access memory (RAM) 821 and / or cache memory 822, and can further include non-volatile memory, such as read-only memory (ROM) 823.

[0224] The memory 82 can also include a program / utility 825 having a set of program modules 824, including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which can include an implementation of a network environment, or a combination thereof.

[0225] The processor 81 can execute the computer program modules stored in the memory 82 to perform various functions and data processing, such as the method for constructing a battery remaining life prediction model in embodiment 1 or the method for predicting battery remaining life in embodiment 2.

[0226] The electronic device 80 can also communicate with one or more external devices 84 (such as a keyboard or a pointing device, among others) via an input / output (I / O) interface 85. Further, the model generating device 80 can communicate with one or more networks, such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet, via a network adapter 86. As Figure 11 shown, the network adapter 86 communicates with the other modules of the model generating device 80 via the bus 83. It should be appreciated that although not shown, other hardware and / or software modules could be used in conjunction with the model generating device 80, including but not limited to, microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc.

[0227] It should be noted that although several units / modules or sub-units / modules of the electronic device are mentioned in the above detailed description, such division is merely exemplary and not mandatory. In fact, according to embodiments of the present disclosure, features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, features and functions of one unit / module described above can be further divided into units / modules embodied by multiple units / modules.

[0228] Embodiment 6

[0229] Based on the same inventive concept, the present application also provides a computer readable storage medium, which stores a computer program, the program being executed by a processor to implement the steps in the method for constructing a battery remaining life prediction model in embodiment 1 or the steps in the battery remaining life prediction method in embodiment 2.

[0230] More specifically, the readable storage medium can include, but is not limited to, a portable disc, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0231] In possible embodiments, the present disclosure can also be implemented in the form of a program product, which includes program code for causing a terminal device to execute the steps in the method for constructing a battery remaining life prediction model in embodiment 1 or the steps in the battery remaining life prediction method in embodiment 2 when the program product is executed on the terminal device.

[0232] The program code for executing the present disclosure can be written in any combination of one or more programming languages, and can be executed entirely on the user device, partially on the user device and partially on a remote device, as a stand-alone software package, partially on the user device and partially on a remote device, or entirely on a remote device.

[0233] In the specification, the same or similar parts between various embodiments are referred to each other, and each embodiment focuses on the difference from other embodiments. In particular, for the embodiments described later, the description is relatively simple, and the relevant parts refer to the part of the description of the foregoing embodiments.

[0234] The above only describes specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for constructing a battery remaining life prediction model, characterized in that: The construction method comprises: Obtaining a battery aging data set corresponding to a number of sample batteries; wherein the battery aging data set includes: corresponding sample remaining life and sample charge and discharge data of the sample batteries from the beginning to the end of their life; Obtaining a target cycle interval based on the battery aging data set; the target cycle interval is the interval between two different charge and discharge cycle times; Preprocessing the battery aging dataset based on the target cycle interval to obtain training sample data; Inputting the training sample data into a preset model to train and obtain the battery remaining life prediction model; The battery remaining life prediction model is used to predict the actual remaining life corresponding to the input target battery capacity characteristic data of the actual battery.

2. The construction method according to claim 1, characterized in that The step of obtaining a target cycle interval based on the battery aging data set includes: Based on the sample charge and discharge data corresponding to the sample battery, obtaining first battery capacity data corresponding to a first preset number of battery cycles and second battery capacity data corresponding to a second preset number of battery cycles of the sample battery; Wherein, the second preset number of battery cycles is greater than the first preset number of battery cycles; Obtaining a first target sample battery remaining life corresponding to the second battery capacity data of the sample battery; The target cycle interval is calculated based on the first battery capacity data, the second battery capacity data, and the first target sample battery remaining life.

3. The construction method according to claim 2, characterized in that The step of calculating the target cycle interval based on the first battery capacity data, the second battery capacity data and the first target sample battery remaining life includes: Calculating a capacity difference between the second battery capacity data and the first battery capacity data; Taking the logarithm of the variance of the capacity difference to obtain sample voltage data; Using a Pearson correlation coefficient method, based on the sample voltage data and the remaining life of the first target sample battery, obtain a Pearson correlation coefficient; When the absolute value of the Pearson correlation coefficient is greater than a preset correlation value, the current cycle number interval is determined to be the target cycle number interval.

4. The construction method according to claim 2, characterized in that The step of preprocessing the battery aging data set based on the target cycle interval to obtain training sample data includes: Obtaining a preset voltage range, a third preset battery cycle number, and a fourth preset battery cycle number corresponding to the sample battery; The fourth preset battery cycle number is obtained based on the sum of the third preset battery cycle number and the target cycle number interval; Dividing the preset voltage interval based on a preset voltage interval, and acquiring third battery capacity data and fourth battery capacity data corresponding to the divided preset voltage interval; The third battery capacity data is the capacity data of the sample battery corresponding to the third preset battery cycle number, and the fourth battery capacity data is the capacity data of the sample battery corresponding to the fourth preset battery cycle number; Based on the third battery capacity data and the fourth battery capacity data, obtaining sample battery capacity characteristic data of several preset dimensions of the sample battery; The training sample data is obtained based on the sample battery capacity characteristic data and the second target sample battery remaining life corresponding to the fourth battery capacity data of the sample battery.

5. The construction method according to claim 1, characterized in that The preset model includes a gradient boosting decision tree model; And / or, the process from the beginning to the end of the life of the several sample batteries corresponds to the battery capacity decaying from the rated capacity to 80% of the rated capacity; And / or, the battery aging data set includes the remaining life of the sample battery under constant current charging and variable frequency discharging conditions.

6. The construction method according to claim 4, characterized in that The preset voltage range is within the full charge and discharge voltage range of the sample battery; And / or, the target cycle number interval includes a target charge cycle number interval or a target discharge cycle number interval.

7. A method for predicting remaining battery life, characterized in that: The method comprises: Obtain the initial charge and discharge data corresponding to the actual battery; Preprocessing the initial charge and discharge data to obtain target battery capacity characteristic data; Inputting the target battery capacity characteristic data into a battery remaining life prediction model to output an actual remaining life corresponding to the actual battery; The battery remaining life prediction model is constructed based on the battery remaining life prediction model construction method according to any one of claims 1 to 6.

8. A system for constructing a battery remaining life prediction model, characterized in that: The build system includes: A data set acquisition module is used to acquire battery aging data sets corresponding to a number of sample batteries; wherein the battery aging data sets include: the remaining service life and sample charge and discharge data of the corresponding sample batteries from the beginning to the end of their life; A cycle interval acquisition module, configured to acquire a target cycle number interval based on the battery aging data set; the target cycle number interval is the interval between two different charge and discharge cycle numbers; a sample data acquisition module, which pre-processes the battery aging data set based on the target cycle number interval to obtain training sample data; A model training module, configured to input the training sample data into a preset model to train and obtain the battery remaining life prediction model; The battery remaining life prediction model is used to predict the actual remaining life corresponding to the input target battery capacity characteristic data of the actual battery.

9. A battery remaining life prediction system, characterized in that: The system comprises: An initial data acquisition module is used to obtain initial charge and discharge data corresponding to the actual battery; a target data acquisition module, configured to pre-process the initial charge and discharge data to obtain target battery capacity characteristic data; A prediction module, configured to input the target battery capacity characteristic data into a battery remaining life prediction model to output an actual remaining life corresponding to the actual battery; The battery remaining life prediction model is constructed based on the battery remaining life prediction model construction system as described in claim 8.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and configured to run on the processor, wherein: When the processor executes the computer program, it implements the method for constructing a battery remaining life prediction model as described in any one of claims 1 to 6, or the battery remaining life prediction method as described in claim 7.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When executed by a processor, the computer program implements the method for constructing a battery remaining life prediction model as described in any one of claims 1 to 6, or the battery remaining life prediction method as described in claim 7.

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