Battery calendar life prediction method, device, equipment and medium

Abstract

The invention provides a battery calendar life prediction method and device, equipment and a medium, and relates to the technical field of batteries, and the method comprises the steps: obtaining a sample data set of a to-be-detected battery; acquiring a capacity loss model, and respectively inputting the sample data into the capacity loss model to obtain reaction rates of the to-be-detected battery under different storage conditions; obtaining a fitting model, determining a plurality of fitting parameters of the fitting model according to each reaction rate and the storage condition corresponding to each reaction rate, and obtaining a target fitting model; according to the target fitting model, determining a target reaction rate of the to-be-tested battery under a target storage condition; determining a target capacity loss model according to the target reaction rate and the capacity loss model; determining a capacity loss curve based on the target capacity loss model, and determining the calendar life according to the capacity loss curve. The prediction efficiency of the calendar life of the battery is improved.

Description

Technical Field

[0001] The present invention relates to the field of battery technology, and in particular to a battery calendar life prediction method, device, equipment and medium. Background Art

[0002] Battery life is mainly divided into cycle life and calendar life. Calendar life refers to the time it takes for the battery's capacity to naturally decay to a certain specified proportion of the initial capacity when it is in storage or idle state.

[0003] Existing methods for predicting battery calendar life typically involve first grouping batteries according to temperature conditions and initial capacity, then selecting battery samples from each group for capacity testing at fixed intervals. The battery's calendar life is determined by tracking the changing trend of its capacity loss rate. This method is costly and cannot account for calendar life under the full range of conditions. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to overcome the deficiencies in the prior art and provide a battery calendar life prediction method, device, equipment and medium. The present invention provides the following technical solutions: In a first aspect, the present invention provides a battery calendar life prediction method, the method comprising: Acquire a sample data set of the battery to be tested, the sample data set comprising: a plurality of sample data, each sample data comprising: a capacity loss value of the battery to be tested under different storage conditions and / or different storage times; Obtaining a capacity loss model, inputting each of the sample data into the capacity loss model, and obtaining a reaction rate of the battery under different storage conditions, wherein the different storage conditions include: different storage temperatures and / or different storage capacities; Obtaining a fitting model, and determining a plurality of fitting parameters of the fitting model according to the reaction rates and the storage conditions corresponding to the reaction rates, to obtain a target fitting model; determining a target reaction rate of the battery under target storage conditions according to the target fitting model; Determining a target capacity loss model of the battery to be tested under the target storage conditions according to the target reaction rate and the capacity loss model; A capacity loss curve is determined based on the target capacity loss model, and the calendar life of the battery to be tested under the target storage conditions is determined according to the capacity loss curve.

[0005] In an optional embodiment, obtaining the capacity loss model includes: Obtaining an initial capacity loss model, and determining a parameter value to be measured of the initial capacity loss model based on each of the sample data, wherein the parameter value to be measured includes: a power change value; Determine an average value of the power change values ​​as a target power change value; Substituting the target power change value into the initial capacity loss model, the capacity loss model is obtained.

[0006] In an optional embodiment, the fitting model includes: a first fitting model, the target fitting model includes: a first target fitting model, and the determining of a plurality of fitting parameters of the fitting model according to each of the reaction rates and the storage conditions corresponding to each of the reaction rates to obtain the target fitting model includes: Determine the reaction rates having the same storage capacity but different storage temperatures as first reaction rates; determining a plurality of first fitting parameters corresponding to the first fitting model according to each of the first reaction rates and the storage temperatures corresponding to each of the first reaction rates; Substitute each of the first fitting parameters into the first fitting model to obtain the first target fitting model. In an optional embodiment, the fitting model includes: a second fitting model, the target fitting model includes: a second target fitting model, and the target fitting model is obtained by determining a plurality of fitting parameters of the fitting model according to each of the reaction rates and the storage conditions corresponding to each of the reaction rates, including: Determine the reaction rates having the same storage temperature but different storage capacities as second reaction rates; determining a plurality of second fitting parameters corresponding to the second fitting model according to each of the second reaction rates and the storage temperatures corresponding to each of the second reaction rates; Substitute each of the second fitting parameters into the second fitting model to obtain the second target fitting model.

[0007] In an optional embodiment, the target storage conditions include: a target storage temperature and a target storage capacity, and determining the target reaction rate of the battery to be tested under the target storage conditions based on the target fitting model includes: Determining a first fitting curve of the battery to be tested at the target storage temperature based on the first target fitting model; Determining a second fitting curve of the battery to be tested at the target storage capacity based on a second target fitting model; The target reaction rate is determined according to the first fitting curve and the second fitting curve.

[0008] In an optional embodiment, determining the target reaction rate according to the first fitting curve and the second fitting curve includes: The target reaction rate is determined as a consistent value between a reaction rate value corresponding to the first fitting curve at the target storage capacity and a reaction rate value corresponding to the second fitting curve at the target storage temperature. In an optional embodiment, the capacity loss curve includes: a plurality of data points, each data point representing a capacity loss value under different calendar days, and determining the battery calendar life of the battery to be tested under target storage conditions according to the capacity loss curve includes: Determining a data point on the capacity loss curve where the capacity loss value exceeds a preset life end threshold as a termination data point; comparing the calendar days corresponding to the respective termination data points, and determining a target data point from the plurality of termination data points based on the comparison result; The calendar days corresponding to the target data point are determined as the battery calendar life of the battery to be tested.

[0009] In a second aspect, the present invention provides a battery calendar life prediction device, the device comprising: an acquisition module, configured to acquire a sample data set of the battery to be tested, the sample data set comprising: a plurality of sample data, each of the sample data comprising: a capacity loss value of the battery to be tested under different storage conditions and / or different storage times; a reaction rate determination module, configured to obtain a capacity loss model, input each sample data into the capacity loss model, and obtain a reaction rate of the battery under different storage conditions, wherein the different storage conditions include: different storage temperatures and / or different storage capacities; a model determination module, configured to obtain a fitting model, determine a plurality of fitting parameters of the fitting model according to each of the reaction rates and the storage conditions corresponding to each of the reaction rates, and obtain a target fitting model; a target reaction rate determination module, configured to determine a target reaction rate of the battery to be tested under target storage conditions according to the target fitting model; a target model determination module, configured to determine a target capacity loss model of the battery to be tested under the target storage conditions according to the target reaction rate; A calendar life determination module is used to determine a capacity loss curve based on the target capacity loss model, and determine the calendar life of the battery to be tested under target storage conditions according to the capacity loss curve.

[0010] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program runs on the processor, the battery calendar life prediction method described in any one of the aforementioned embodiments is executed. In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the battery calendar life prediction method described in any one of the aforementioned embodiments.

[0011] The present application provides a battery calendar life prediction method, apparatus, device, and medium, which obtain a sample data set of a battery to be tested, wherein the sample data set includes: multiple sample data, each of which includes: a capacity loss value of the battery to be tested under different storage conditions and / or different storage times; obtain a capacity loss model, input each of the sample data into the capacity loss model, and obtain the reaction rate of the battery to be tested under different storage conditions, wherein the different storage conditions include: different storage temperatures and / or different storage capacities; obtain a fitting model, and determine multiple fitting parameters of the fitting model based on each of the reaction rates and the storage conditions corresponding to each of the reaction rates. A target fitting model is obtained; based on the target fitting model, a target reaction rate of the battery to be tested under target storage conditions is determined; based on the target reaction rate and the capacity loss model, a target capacity loss model of the battery to be tested under the target storage conditions is determined; based on the target capacity loss model, a capacity loss curve is determined, and based on the capacity loss curve, the calendar life of the battery to be tested under the target storage conditions is determined. This realizes the promotion of calendar life prediction based on limited sample data, and does not require re-conducting calendar life detection experiments. The calendar life of the battery to be tested under any storage temperature and / or storage capacity can be promoted and predicted, thereby improving the prediction efficiency of the battery calendar life.

[0012] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without making any creative efforts.

[0014] Figure 1 A schematic diagram of a process flow of a battery calendar life prediction method provided by an embodiment of the present application is shown; Figure 2 Another flowchart of the battery calendar life prediction method provided by an embodiment of the present application is shown; Figure 3 Another flow chart of the battery calendar life prediction method provided by an embodiment of the present application is shown; Figure 4 Another flowchart of the battery calendar life prediction method provided by the embodiment of the present application is shown; Figure 5 Another flowchart of the battery calendar life prediction method provided by the embodiment of the present application is shown; Figure 6 A schematic diagram of a process flow of a battery calendar life prediction method provided by an embodiment of the present application is shown; Figure 7 A schematic diagram of the structure of a battery calendar life prediction device provided by an embodiment of the present application is shown; Figure 8 A structural schematic diagram of an electronic device provided in an embodiment of the present application is shown.

[0015] Description of main component symbols: 700 - battery calendar life prediction device; 710 - acquisition module; 720 - reaction rate determination module; 730 - model determination module; 740 - target reaction rate determination module; 750 - target model determination module; 760 - calendar life determination module; 800 - electronic device; 801 - transceiver; 802 - processor; 803 - memory. DETAILED DESCRIPTION

[0016] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0017] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature identified as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by one skilled in the art to which this application belongs. The terms used in the template description herein are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0019] Example 1 See Figure 1 , an embodiment of the present application provides a battery calendar life prediction method, including: steps S110~S160.

[0020] Step S110 , obtaining a sample data set of the battery to be tested, wherein the sample data set includes: a plurality of sample data, each of the sample data respectively including: a capacity loss value of the battery to be tested under different storage conditions and / or different storage time.

[0021] In this embodiment, different storage conditions include different storage temperatures and / or storage capacities. Specifically, batteries from the same batch are grouped, and each group is stored under a unique combination of storage conditions. Capacity changes are monitored at regular intervals, such as weekly or monthly, until the end of the calendar life. The capacity loss values ​​of the batteries under different storage temperatures, storage capacities, and / or storage times are recorded to generate a sample data set for the batteries under test. The storage conditions, storage times, and capacity loss values ​​in the sample data set form a correlation, providing fundamental data support for the subsequent development of a quantitative correlation model.

[0022] Step S120 , obtaining a capacity loss model, inputting each of the sample data into the capacity loss model, and obtaining a reaction rate of the battery under different storage conditions, wherein the different storage conditions include: different storage temperatures and / or different storage capacities.

[0023] In this embodiment, the capacity loss model is: ,in, Indicates the storage temperature and storage capacity The capacity loss value of the battery under test under the conditions of Indicates storage temperature and storage capacity The reaction rate of the battery under test under the conditions of Indicates the shelf time of the battery to be tested. It should be noted that the power change value is predetermined based on the sample data set.

[0024] It can be understood that the reaction rate of the battery to be tested is different at different storage temperatures and / or different storage capacities. In order to determine the reaction rate of the battery to be tested at different storage temperatures and / or storage capacities, the technical means adopted in the embodiment of the present application is: inputting sample data into the capacity loss model, wherein the sample data includes: the capacity loss value of the battery to be tested at different storage temperatures, different storage capacities and / or different storage times, and obtaining the reaction rate of the battery to be tested at different storage temperatures and / or different storage capacities.

[0025] In one embodiment, see Figure 2 , obtaining a capacity loss model, including: steps S121 to S123.

[0026] S121 , obtaining an initial capacity loss model, and determining a parameter value to be measured of the initial capacity loss model based on each of the sample data sets, wherein the parameter value to be measured includes a power change value.

[0027] In this embodiment, the initial capacity loss model is: In order to find the right The capacity loss values ​​at different storage temperatures and / or storage capacities are identified by parameter respectively to obtain the capacity loss values ​​at different storage temperatures and / or storage capacities. and .

[0028] S122: Determine the average value of each power change value as the target power change value.

[0029] Furthermore, the obtained series The average value is obtained by removing abnormal values, and the average value is the target power change value of the initial capacity loss model.

[0030] S123: Substitute the target power change value into the initial capacity loss model to obtain the capacity loss model.

[0031] Substituting the target power change value into the initial capacity loss model, the model is simplified into a "capacity loss model" that can calculate the capacity loss rate only through reaction rate and shelf time, laying the foundation for subsequent calendar life extension prediction.

[0032] Step S130 , obtaining a fitting model, determining a plurality of fitting parameters of the fitting model according to each of the reaction rates and the storage conditions corresponding to each of the reaction rates, and obtaining a target fitting model.

[0033] It can be understood that by establishing a fitting model for the reaction rate changing with storage conditions, the reaction rate under unknown storage conditions can be estimated based on the reaction rate of the battery to be tested under known storage conditions.

[0034] In this embodiment, the fitting model includes: a fitting model of the reaction rate of the battery to be tested at the same storage capacity but different storage temperatures: ,in, 、 and are fitting parameters to be determined. Indicates absolute temperature.

[0035] The fitting model also includes: the fitting model of the reaction rate of the battery to be tested at the same storage temperature but different storage capacities: ,in, 、 and are fitting parameters to be determined. is the initial capacity percentage of the battery to be tested.

[0036] In one embodiment, the fitting model includes: a first fitting model, and the target fitting model includes: a first target fitting model, see Figure 3 , determining multiple fitting parameters of the fitting model according to each of the reaction rates and the storage conditions corresponding to each of the reaction rates to obtain a target fitting model includes: steps S131~S133.

[0037] Step S131 : determining the reaction rates having the same storage capacity but different storage temperatures as first reaction rates.

[0038] From all acquired reaction rates (obtained in step S120), select the data with the same storage capacity but different storage temperatures and define it as the first reaction rate. For example, when the storage capacity is fixed at 25% SOC, the reaction rates measured at different storage temperatures, such as 25°C, 45°C, and 60°C, are all defined as the first reaction rate.

[0039] It can be understood that by fixing the storage capacity variable, targeted data can be screened out for the subsequent establishment of a relationship model between reaction rate and temperature under the same storage capacity, ensuring that the model only reflects the impact of temperature on reaction rate.

[0040] Step S132: determining a plurality of first fitting parameters corresponding to the first fitting model according to each of the first reaction rates and the storage temperatures corresponding to each of the first reaction rates.

[0041] In this embodiment, the first fitting model is the reaction rate fitting model for the same storage capacity but different storage temperatures as described above. Each first reaction rate and the storage temperature (converted to absolute temperature) corresponding to each first reaction rate are substituted into the first fitting model, and the first fitting parameter to be determined in the first fitting model is calculated using a parameter identification method, such as nonlinear least squares fitting. 、 and These parameters quantify how the reaction rate varies with storage temperature for a given storage capacity.

[0042] Step S133: Substitute each of the first fitting parameters into the first fitting model to obtain the first target fitting model. In this embodiment, the determined first fitting parameters are substituted into the first fitting model to obtain a first target fitting model that can be used directly. For example, when the first fitting parameter corresponding to 25% SOC is 、 and After the calculation is complete, it is substituted into the first fitting model to obtain a quantitative "reaction rate-temperature" model applicable to 25% SOC. The value of the first target fitting model lies in: entering any storage temperature, even those that have not been experimentally tested, can directly calculate the reaction rate corresponding to the input storage temperature at 25% SOC, thus generalizing the reaction rate across different storage temperatures at the same storage capacity.

[0043] In one embodiment, the fitting model includes: a second fitting model, and the target fitting model includes: a second target fitting model, see Figure 4 , determining multiple fitting parameters of the fitting model according to each of the reaction rates and the storage conditions corresponding to each of the reaction rates to obtain a target fitting model includes: steps S134~S136.

[0044] Step S134: determining the reaction rates having the same storage temperature but different storage capacities as second reaction rates.

[0045] In this embodiment, the reaction rates corresponding to conditions with the same storage temperature but different storage capacities are selected from the multiple existing reaction rates (specifically, the multiple reaction rates obtained in step S120) and are each determined as the second reaction rate. For example, when the storage temperature is fixed at 25°C, the reaction rates measured at different storage capacities, such as 25% SOC, 50% SOC, and 100% SOC, all belong to the second reaction rate corresponding to the storage temperature of 25°C. This step, by fixing the storage temperature as a variable, filters out targeted data for the subsequent establishment of a relationship model between reaction rate and storage capacity at the same storage temperature, ensuring that the relationship model only reflects the impact of storage capacity on reaction rate.

[0046] Step S135 : determining a plurality of second fitting parameters corresponding to the second fitting model according to each of the second reaction rates and the storage temperatures corresponding to each of the second reaction rates.

[0047] In this embodiment, the second fitting model is the reaction rate fitting model for the same storage temperature but different storage capacities as described above. Each second reaction rate and the storage capacity corresponding to each second reaction rate are substituted into the second fitting model, and the second fitting parameter to be determined in the second fitting model is calculated by a parameter identification method, such as nonlinear least squares fitting. 、 and These parameters quantify how the reaction rate varies with storage capacity at a given storage temperature.

[0048] Step S136: Substitute each of the second fitting parameters into the second fitting model to obtain the second target fitting model.

[0049] It can be understood that after substituting the determined second fitting parameters into the second fitting model, a second target fitting model that can be used directly is obtained. For example, when the second fitting parameters corresponding to 25°C are 、 and After the calculations were completed, they were substituted into the second fitting model to obtain a quantitative "reaction rate-storage capacity" model applicable to 25°C. The value of this model lies in its ability to directly calculate the reaction rate for any input storage capacity, even those that have not been experimentally tested, at a given storage temperature. This allows for the generalization of reaction rates for different storage capacities at the same storage temperature.

[0050] Step S140 , determining a target reaction rate of the battery to be tested under target storage conditions according to the target fitting model.

[0051] In this embodiment, the target fitting model includes: a first target fitting model and a second target fitting model, and the target storage condition refers to any combination of storage temperature and storage capacity that needs to be predicted.

[0052] In one embodiment, the target storage conditions include: target storage temperature and target storage capacity, see Figure 5 , step S140 includes: steps S141~S143.

[0053] Step S141 : determining a first fitting curve of the battery to be tested at the target storage temperature based on the first target fitting model. It can be understood that the first target fitting model quantifies how the reaction rate changes with storage temperature at a specified storage capacity. Once the target storage capacity is determined, the first target fitting model corresponding to the target storage capacity is invoked. Based on this first target fitting model, the reaction rates corresponding to different storage temperatures at the target storage capacity can be derived, forming a first fitting curve with storage temperature as the horizontal axis and reaction rate as the vertical axis.

[0054] Step S142: determining a second fitting curve of the battery to be tested under the target storage capacity based on a second target fitting model.

[0055] As can be understood, the second fitting model quantifies how the reaction rate changes with storage capacity at a specified temperature. Once the target storage temperature is determined, the corresponding second target fitting model is invoked. Based on this second target fitting model, the reaction rates corresponding to different storage capacities at the target storage temperature can be deduced, forming a second fitting curve with storage capacity on the horizontal axis and reaction rate on the vertical axis.

[0056] Step S143: determining the target reaction rate according to the first fitting curve and the second fitting curve.

[0057] In this embodiment, the first fitting curve shows the relationship between the storage capacity and the reaction rate at the target storage capacity, and the second fitting curve shows the relationship between the storage temperature and the reaction rate at the target storage temperature.

[0058] In one embodiment, determining the target reaction rate based on the first fitting curve and the second fitting curve includes: determining the target reaction rate as a consistent value of the reaction rate value corresponding to the first fitting curve at the target storage capacity and the reaction rate value corresponding to the second fitting curve at the target storage temperature.

[0059] In this embodiment, the first fitting curve has storage capacity on the horizontal axis and reaction rate on the vertical axis. This curve is generated under the premise of a fixed target storage temperature, for example, a curve showing the reaction rate changes corresponding to different storage capacities at 30°C. Therefore, when the target storage capacity is, for example, 50% SOC, the vertical axis value corresponding to this storage capacity can be found on the first fitting curve, i.e., the reaction rate at 30°C and 50% SOC.

[0060] The second fitting curve has storage temperature on the horizontal axis and reaction rate on the vertical axis. This curve is generated under the assumption of a fixed target storage capacity, for example, a curve showing the reaction rate at different storage temperatures at 50% SOC. Therefore, for a target storage temperature of 30°C, for example, the vertical axis value corresponding to the target temperature can be found on the second fitting curve, i.e., the reaction rate at 30°C and 50% SOC.

[0061] Since the vertical axes of the first and second fitting curves are both reaction rates and are both constrained by the target storage conditions of 30°C and 50% SOC, the vertical axis values ​​calculated from the two fitting curves must be equal (i.e., the reaction rate under the same conditions is unique).

[0062] Step S150 , determining a target capacity loss model of the battery to be tested under the target storage condition according to the target reaction rate and the capacity loss model.

[0063] Specifically, the calculated target reaction rate is substituted into the capacity loss model to obtain a target capacity loss model for the battery to be tested under target storage conditions.

[0064] Step S160 , determining a capacity loss curve based on the target capacity loss model, and determining the calendar life of the battery to be tested under the target storage condition according to the capacity loss curve.

[0065] Based on the target capacity loss model, a capacity loss curve is fitted. The horizontal axis of the capacity loss curve represents the calendar life of the battery under test, and the vertical axis represents the capacity loss value of the battery under test. Based on the preset end-of-life threshold, the time point corresponding to the preset end-of-life threshold is found on the capacity loss curve. This time point is the calendar life of the battery under the target storage conditions. The entire process does not require long-term field testing.

[0066] In one embodiment, see Figure 6 The capacity loss curve includes: multiple data points, each data point represents a capacity loss value under different calendar days, and step S160 includes: steps S161~S163.

[0067] Step S161 : determining a data point on the capacity loss curve where the capacity loss value exceeds a preset life end threshold as a termination data point.

[0068] Specifically, the capacity loss curve uses calendar life as the horizontal axis and capacity loss value as the vertical axis, reflecting the decay trend of battery capacity over time. The preset end-of-life threshold is the critical capacity loss value at which the battery is deemed unusable. This step compares the capacity loss value of each data point on the curve with the preset threshold and marks all data points with "capacity loss values ​​exceeding or equal to the threshold" as "termination data points." These points represent that the battery has reached the end of its life at the corresponding time point.

[0069] Step S162 , comparing the calendar days corresponding to the respective termination data points, and determining a target data point from the plurality of termination data points according to the comparison result.

[0070] Filter out the earliest end-of-life time from multiple termination data points. Since the capacity loss curve is usually a continuous attenuation curve, or is fitted by multiple discrete data points), there may be multiple termination data points, that is, the capacity loss values ​​at multiple time points exceed the threshold. By comparing the calendar days corresponding to these termination data points, find the smallest calendar day, that is, the time point that reaches the end-of-life threshold earliest, and determine it as the "target data point." The logic of this operation is: the calendar life of the battery is determined by the time when the end-of-life threshold is first reached, rather than any subsequent time that exceeds the threshold, to ensure the rigor of the life calculation.

[0071] Step S163: Determine the calendar days corresponding to the target data point as the battery calendar life of the battery to be tested.

[0072] The calendar days corresponding to the target data point are determined as the battery's calendar life. This refers to the time it takes for the battery to decay from the start of storage to the point where its capacity no longer meets usage requirements under the target storage conditions. For example, if the calendar days corresponding to the target data point are 1200 days, the battery's calendar life under the target storage conditions is 1200 days. This step converts the abstract curve data into a specific lifespan value, completing the final transition from curve analysis to lifespan determination.

[0073] The present application provides a battery calendar life prediction method, which obtains a sample data set of a battery to be tested, wherein the sample data set includes: multiple sample data, each of the sample data includes: a capacity loss value of the battery to be tested under different storage conditions and / or different storage times; obtains a capacity loss model, inputs each of the sample data into the capacity loss model, and obtains a reaction rate of the battery to be tested under different storage conditions, wherein the different storage conditions include: different storage temperatures and / or different storage capacities; obtains a fitting model, and determines multiple fitting parameters of the fitting model according to each of the reaction rates and the storage conditions corresponding to each of the reaction rates to obtain a target fitting model; determines a target reaction rate of the battery to be tested under the target storage conditions according to the target fitting model; determines a target capacity loss model of the battery to be tested under the target storage conditions according to the target reaction rate and the capacity loss model; determines a capacity loss curve based on the target capacity loss model, and determines the calendar life of the battery to be tested under the target storage conditions according to the capacity loss curve.

[0074] Example 2 Also, see Figure 7 The present application also provides a battery calendar life prediction device 700, including: An acquisition module 710 is configured to acquire a sample data set of a battery to be tested, wherein the sample data set includes: a plurality of sample data, each of which includes: a capacity loss value of the battery to be tested under different storage conditions and / or different storage time; a reaction rate determination module 720 for obtaining a capacity loss model, inputting each sample data into the capacity loss model, and obtaining a reaction rate of the battery under different storage conditions, wherein the different storage conditions include different storage temperatures and / or different storage capacities; The model determination module 730 is used to obtain a fitting model, determine a plurality of fitting parameters of the fitting model according to the reaction rates and the storage conditions corresponding to the reaction rates, and obtain a target fitting model; A target reaction rate determination module 740 is configured to determine a target reaction rate of the battery under target storage conditions according to the target fitting model; A target model determination module 750 is configured to determine a target capacity loss model of the battery under the target storage condition according to the target reaction rate; The calendar life determination module 760 is configured to determine a capacity loss curve based on the target capacity loss model, and determine the calendar life of the battery under test under target storage conditions according to the capacity loss curve.

[0075] The battery calendar life prediction device 700 provided in the embodiment of the present invention can execute the battery calendar life prediction method provided in the above-mentioned method embodiment 1, which will not be described again here to avoid repetition.

[0076] The present application provides a battery calendar life prediction device, which realizes the promotion of calendar life prediction based on limited sample data. It does not need to re-conduct calendar life detection experiments, and can promote the prediction of the calendar life of the battery to be tested at any storage temperature and / or storage capacity, thereby improving the prediction efficiency of the battery calendar life.

[0077] Example 3 In addition, an embodiment of the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program runs on the processor, the battery calendar life prediction method provided in Example 1 is executed.

[0078] For details, see Figure 8 The electronic device 800 includes: a transceiver 801, a bus interface and a processor 802, wherein the processor 802 is configured to obtain a sample data set of a battery to be tested, wherein the sample data set includes: multiple sample data, each sample data including: a capacity loss value of the battery to be tested under different storage conditions and / or different storage times; obtain a capacity loss model, input each sample data into the capacity loss model to obtain a reaction rate of the battery to be tested under different storage conditions, wherein the different storage conditions include: different storage temperatures and / or different storage capacities; obtain a fitting model, determine multiple fitting parameters of the fitting model according to each reaction rate and the storage conditions corresponding to each reaction rate, and obtain a target fitting model; determine a target reaction rate of the battery to be tested under the target storage condition according to the target fitting model; determine a target capacity loss model of the battery to be tested under the target storage condition according to the target reaction rate and the capacity loss model; determine a capacity loss curve based on the target capacity loss model, and determine the calendar life of the battery to be tested under the target storage condition according to the capacity loss curve.

[0079] In the embodiment of the present invention, the electronic device 800 further includes a memory 803. Figure 8In the embodiment, the bus architecture can include any number of interconnected buses and bridges, specifically linking together various circuits of one or more processors represented by processor 802 and memory represented by memory 803. The bus architecture can also link together various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are all well known in the art and therefore will not be further described herein. The bus interface provides an interface. The transceiver 801 can be multiple components, that is, including a transmitter and a receiver, providing a unit for communicating with various other devices on a transmission medium. The processor 802 is responsible for managing the bus architecture and general processing, and the memory 803 can store data used by the processor 802 when performing operations.

[0080] The electronic device 800 provided in the embodiment of the present invention can execute the battery calendar life prediction method provided in the above method embodiment 1, which will not be described again here to avoid repetition.

[0081] Example 4 In addition, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the battery calendar life prediction method provided in Example 1 is implemented.

[0082] In this embodiment, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0083] The computer-readable storage medium provided in this embodiment can implement the battery calendar life prediction method provided in Example 1, and will not be described again here to avoid repetition.

[0084] In all examples shown and described herein, any specific values ​​should be interpreted as merely exemplary and not limiting, and thus other examples of the exemplary embodiments may have different values.

[0085] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0086] The above-described embodiments merely illustrate several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that variations and modifications are possible without departing from the scope of the present invention, and such variations and modifications are fully within the scope of protection of the present invention.

Claims

1. A battery calendar life prediction method, characterized in that: The method comprises: Acquire a sample data set of the battery to be tested, the sample data set comprising: a plurality of sample data, each sample data comprising: a capacity loss value of the battery to be tested under different storage conditions and / or different storage times; Obtaining a capacity loss model, inputting each of the sample data into the capacity loss model, and obtaining a reaction rate of the battery under different storage conditions, wherein the different storage conditions include: different storage temperatures and / or different storage capacities; Obtaining a fitting model, and determining a plurality of fitting parameters of the fitting model according to the reaction rates and the storage conditions corresponding to the reaction rates, to obtain a target fitting model; determining a target reaction rate of the battery under target storage conditions according to the target fitting model; Determining a target capacity loss model of the battery to be tested under the target storage conditions according to the target reaction rate and the capacity loss model; A capacity loss curve is determined based on the target capacity loss model, and the calendar life of the battery to be tested under the target storage conditions is determined according to the capacity loss curve.

2. The battery calendar life prediction method according to claim 1, characterized in that: The obtaining of the capacity loss model includes: Obtaining an initial capacity loss model, and determining a parameter value to be measured of the initial capacity loss model based on each of the sample data, wherein the parameter value to be measured includes: a power change value; Determine an average value of the power change values ​​as a target power change value; Substituting the target power change value into the initial capacity loss model, the capacity loss model is obtained.

3. The battery calendar life prediction method according to claim 2, characterized in that: The fitting model includes: a first fitting model, and the target fitting model includes: a first target fitting model. The target fitting model is obtained by determining a plurality of fitting parameters of the fitting model according to each of the reaction rates and the storage conditions corresponding to each of the reaction rates, including: Determine the reaction rates having the same storage capacity but different storage temperatures as first reaction rates; determining a plurality of first fitting parameters corresponding to the first fitting model according to each of the first reaction rates and the storage temperatures corresponding to each of the first reaction rates; Substitute each of the first fitting parameters into the first fitting model to obtain the first target fitting model.

4. The battery calendar life prediction method according to claim 3, characterized in that: The fitting model includes: a second fitting model, the target fitting model includes: a second target fitting model, and the target fitting model is obtained by determining a plurality of fitting parameters of the fitting model according to each of the reaction rates and the storage conditions corresponding to each of the reaction rates, including: Determine the reaction rates having the same storage temperature but different storage capacities as second reaction rates; determining a plurality of second fitting parameters corresponding to the second fitting model according to each of the second reaction rates and the storage temperatures corresponding to each of the second reaction rates; Substitute each of the second fitting parameters into the second fitting model to obtain the second target fitting model.

5. The battery calendar life prediction method according to claim 4, characterized in that: The target storage conditions include: a target storage temperature and a target storage capacity. Determining the target reaction rate of the battery under the target storage conditions based on the target fitting model includes: Determining a first fitting curve of the battery to be tested at the target storage temperature based on the first target fitting model; Determining a second fitting curve of the battery to be tested at the target storage capacity based on a second target fitting model; The target reaction rate is determined according to the first fitting curve and the second fitting curve.

6. The battery calendar life prediction method according to claim 5, characterized in that: Determining the target reaction rate according to the first fitting curve and the second fitting curve includes: The target reaction rate is determined as a consistent value between a reaction rate value corresponding to the first fitting curve at the target storage capacity and a reaction rate value corresponding to the second fitting curve at the target storage temperature.

7. The battery calendar life prediction method according to claim 1, characterized in that: The capacity loss curve includes: a plurality of data points, each of which represents a capacity loss value under different calendar days. Determining the battery calendar life of the battery to be tested under target storage conditions according to the capacity loss curve includes: Determining a data point on the capacity loss curve where the capacity loss value exceeds a preset life end threshold as a termination data point; comparing the calendar days corresponding to the respective termination data points, and determining a target data point from the plurality of termination data points based on the comparison result; The calendar days corresponding to the target data point are determined as the battery calendar life of the battery to be tested.

8. A battery calendar life prediction device, characterized in that: The device comprises: an acquisition module, configured to acquire a sample data set of the battery to be tested, the sample data set comprising: a plurality of sample data, each of the sample data comprising: a capacity loss value of the battery to be tested under different storage conditions and / or different storage times; a reaction rate determination module, configured to obtain a capacity loss model, input each sample data into the capacity loss model, and obtain a reaction rate of the battery under different storage conditions, wherein the different storage conditions include: different storage temperatures and / or different storage capacities; a model determination module, configured to obtain a fitting model, determine a plurality of fitting parameters of the fitting model according to each of the reaction rates and the storage conditions corresponding to each of the reaction rates, and obtain a target fitting model; a target reaction rate determination module, configured to determine a target reaction rate of the battery to be tested under target storage conditions according to the target fitting model; a target model determination module, configured to determine a target capacity loss model of the battery to be tested under the target storage conditions according to the target reaction rate; A calendar life determination module is used to determine a capacity loss curve based on the target capacity loss model, and determine the calendar life of the battery to be tested under target storage conditions according to the capacity loss curve.

9. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is run on the processor, the method for predicting battery calendar life according to any one of claims 1 to 7 is executed.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the battery calendar life prediction method according to any one of claims 1 to 7 is implemented.

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