A method and apparatus for evaluating calendar life of a battery

Abstract

One or more embodiments of the specification provide a battery calendar life evaluation method and device, electronic equipment and machine readable storage medium, the method comprising: obtaining a battery cell type of a battery to be evaluated, and determining a first evaluation model corresponding to the battery cell type; wherein the first evaluation model comprises a semi-empirical model obtained by data fitting of capacity attenuation test data based on calendar aging test, and a model obtained by fusing user usage data for the battery to be evaluated; the user usage data for the battery to be evaluated embodies the actual usage habit of the user for the battery to be evaluated; obtaining evaluation parameters of the battery to be evaluated; inputting the obtained evaluation parameters into the first evaluation model to evaluate the calendar life of the battery to be evaluated.

Description

TECHNICAL FIELD

[0001] One or more embodiments of the present specification relate to the technical field of power battery, and in particular to a battery calendar life evaluation method and device, electronic equipment and machine readable storage medium. BACKGROUND

[0002] The life problem of power battery is a key problem of new energy vehicles. The internal characteristics of single battery, the grouping mode of power source, and the performance demand of vehicle are related to the life state of battery. At present, the research on life problem has problems such as difficulty in collecting life data, difficulty in implementing life prediction and state estimation.

[0003] The calendar life of battery refers to the period from the production date of battery to the end of life. The end of life does not mean that the battery cannot be discharged, but that the current capacity of the battery decays to a set life termination threshold (the life termination threshold is usually a certain percentage of the initial capacity of the battery). During the use of the battery, the working conditions, storage, charging and discharging experienced by the battery will accompany the aging of the calendar life, and the use time, temperature, state of charge (SOC) of the battery and other factors will affect the life of the battery. The state of charge (SOC) of the battery refers to the ratio of the remaining capacity of the battery to the capacity of the battery, usually expressed as a percentage, which is a physical quantity used to reflect the remaining capacity of the battery.

[0004] Therefore, how to accurately evaluate the capacity degradation of the battery and further judge the calendar life of the battery is of great significance to the health management of the battery pack. SUMMARY

[0005] The present application provides a battery calendar life evaluation method, which comprises:

[0006] Obtaining the cell type of the battery to be evaluated, and determining the first evaluation model corresponding to the cell type; wherein the first evaluation model comprises a semi-empirical model obtained by data fitting based on capacity attenuation test data obtained by calendar aging test, and a model obtained by fusing user usage data of the battery to be evaluated; the user usage data of the battery to be evaluated reflects the actual usage habit of the user for the battery to be evaluated; obtaining the evaluation parameters of the battery to be evaluated; inputting the obtained evaluation parameters into the first evaluation model to evaluate the calendar life of the battery to be evaluated.

[0007] Optionally, the use data of the user for the battery to be evaluated includes: SOC data of the battery to be evaluated generated in the process that the user uses the battery to be evaluated; and the fusing of the semi-empirical model and the use data of the user for the battery to be evaluated to obtain the first evaluation model includes: fusing the semi-empirical model and the SOC data of the battery to be evaluated generated in the process that the user uses the battery to be evaluated to obtain the first evaluation model.

[0008] Optionally, the SOC data includes discharge starting SOC data; and the discharge starting SOC data includes SOC data displayed by a dashboard of an electric vehicle carrying the battery to be evaluated from static to before driving.

[0009] Optionally, the discharge starting SOC data includes a first frame of SOC data displayed by the dashboard of the electric vehicle from static to before driving.

[0010] Optionally, the fusing of the semi-empirical model and the SOC data of the battery to be evaluated generated in the process that the user uses the battery to be evaluated to obtain the first evaluation model includes: obtaining the SOC data of the battery to be evaluated generated in the process that the user uses the battery to be evaluated and statistically analyzing the obtained SOC data to obtain a SOC data distribution of the battery to be evaluated; and the SOC data distribution includes a plurality of SOC value intervals and a user proportion corresponding to each SOC value interval; and the fusing of the semi-empirical model and the SOC data distribution to obtain the first evaluation model.

[0011] Optionally, the SOC data distribution includes a plurality of preset SOC reference value intervals and a user proportion corresponding to the SOC reference value intervals; and the fusing of the semi-empirical model and the SOC data distribution to obtain the first evaluation model includes: obtaining, based on the semi-empirical model, a first capacity attenuation factor corresponding to a plurality of preset SOC reference values of the battery to be evaluated varying with a preset variable; and the preset variable includes time and temperature; and fusing the user proportion corresponding to the SOC reference value intervals in the SOC data distribution and the first capacity attenuation factor corresponding to the preset SOC reference values to obtain the first evaluation model.

[0012] Optionally, the SOC data distribution corresponding to the SOC reference value interval is fused with the first capacity attenuation factor corresponding to the preset SOC reference value to obtain the first evaluation model, including: weighting and summing the user proportion corresponding to each SOC reference value interval in the SOC data distribution and a plurality of first capacity attenuation factors corresponding to each SOC reference value to obtain the first evaluation model.

[0013] The application also provides a calendar life evaluation device of a battery, the device comprising:

[0014] The first evaluation model determination module is configured to obtain the type of the battery cell to be evaluated and determine a first evaluation model corresponding to the type of the battery cell; wherein the first evaluation model comprises a semi-empirical model obtained by data fitting of capacity attenuation test data obtained based on calendar aging test, and a model obtained by fusing user usage data of the battery to be evaluated; the user usage data of the battery to be evaluated reflects the actual usage habit of the user for the battery to be evaluated.

[0015] The evaluation parameter acquisition module is configured to obtain evaluation parameters of the battery to be evaluated.

[0016] The calendar life evaluation module is configured to input the obtained evaluation parameters into the first evaluation model to evaluate the calendar life of the battery to be evaluated.

[0017] Optionally, the user usage data of the battery to be evaluated comprises SOC data of the battery to be evaluated generated during the use of the battery to be evaluated by the user; and the first evaluation model determination module is configured to fuse the semi-empirical model with the SOC data of the battery to be evaluated generated during the use of the battery to be evaluated by the user to obtain the first evaluation model.

[0018] Optionally, the SOC data comprises discharge start SOC data; wherein the discharge start SOC data comprises SOC data displayed on the electric vehicle dashboard from static to driving of the electric vehicle equipped with the battery to be evaluated.

[0019] Optionally, the discharge start SOC data comprises the first frame SOC data displayed on the electric vehicle dashboard from static to driving of the electric vehicle.

[0020] Optionally, the first evaluation model determining module is configured to acquire SOC data of the battery to be evaluated generated in a process in which the user uses the battery to be evaluated, and statistically analyze the acquired SOC data to obtain an SOC data distribution of the battery to be evaluated; wherein the SOC data distribution comprises a plurality of SOC value intervals and a user proportion corresponding to each SOC value interval; and the semi-empirical model is fused with the SOC data distribution to obtain the first evaluation model.

[0021] Optionally, the SOC data distribution comprises a plurality of preset SOC reference value intervals and a user proportion corresponding to each SOC reference value interval; and the first evaluation model determining module is configured to obtain, based on the semi-empirical model, a first capacity attenuation factor corresponding to each preset SOC reference value varying with a preset variable for the battery to be evaluated; wherein the preset variable comprises time and temperature; and the user proportion corresponding to each SOC reference value interval in the SOC data distribution is fused with the first capacity attenuation factor corresponding to each preset SOC reference value to obtain the first evaluation model.

[0022] Optionally, the first evaluation model determining module is configured to perform weighted summation on the user proportion corresponding to each SOC reference value interval in the SOC data distribution and the plurality of first capacity attenuation factors corresponding to each preset SOC reference value to obtain the first evaluation model.

[0023] The application further provides an electronic device comprising a communication interface, a processor, a memory and a bus, wherein the communication interface, the processor and the memory are connected to each other through the bus.

[0024] The memory stores machine readable instructions, and the processor executes the above method by invoking the machine readable instructions.

[0025] The application further provides a machine readable storage medium storing machine readable instructions, wherein the machine readable instructions are invoked and executed by a processor to implement the above method.

[0026] Through the above embodiments, the first evaluation model is obtained by fusing actual use data of the battery to be evaluated with a semi-empirical model obtained by data fitting on capacity attenuation test data obtained based on a calendar aging test, so that the calendar life of the battery to be evaluated can be evaluated by using the first evaluation model.

[0027] The application is based on the calendar aging capacity attenuation of the battery cell to be evaluated in an ideal experimental environment, and the actual capacity attenuation of the battery caused by the user's usage habits is different from the capacity attenuation obtained by the experiment. Therefore, the application obtains the experimental data and fuses the actual usage data of the user to obtain the first evaluation model, so that the first evaluation model can be more accurate for the evaluation data of the calendar life of the battery to be evaluated. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 FIG. 1 is a flowchart of a battery calendar life evaluation method according to an example embodiment;

[0029] Figure 2 FIG. 2 is a flowchart of another battery calendar life evaluation method according to an example embodiment;

[0030] Figure 3 FIG. 3 is a schematic diagram of a SOC distribution of a battery calendar aging according to an example embodiment;

[0031] Figure 4 FIG. 4 is a schematic diagram of an electronic device structure of a battery calendar life evaluation device according to an example embodiment;

[0032] Figure 5 FIG. 5 is a block diagram of a battery calendar life evaluation device according to an example embodiment. DETAILED DESCRIPTION

[0033] The example embodiments will be described in detail herein with reference to the accompanying drawings. In the following description, the same numbers refer to the same or similar elements unless otherwise represented. The embodiments described in the following example embodiments do not represent all embodiments consistent with one or more embodiments of the present specification. Instead, they are merely examples of devices and methods consistent with some aspects of one or more embodiments of the present specification as detailed in the appended claims.

[0034] It should be noted that the steps of the corresponding method are not necessarily performed in the order shown and described in the present specification in other embodiments. In some other embodiments, the steps included in the method can be more or less than described in the present specification. In addition, a single step described in the present specification can be divided into multiple steps for description in other embodiments, and multiple steps described in the present specification can be combined into a single step for description in other embodiments.

[0035] In order for those skilled in the art to better understand the technical solutions in the embodiments of the present specification, the related technologies of the calendar storage capacity fading test experiment involved in the embodiments of the present specification will be briefly described first.

[0036] Please refer to Figure 1 , Figure 1 is an exemplary embodiment showing a flowchart of a calendar life evaluation method of a battery, the above method performs the following steps:

[0037] S102, obtain a battery with the same battery cell and the same batch as the battery to be evaluated as a test battery.

[0038] In actual application, after obtaining the above test battery, the obtained test battery needs to be formation treated before the experiment. Among them, the formation treatment refers to the activation treatment of a brand new battery to ensure that the test battery can normally perform capacity fading in the experimental environment, so as to ensure the normal progress of the experiment.

[0039] S104, performing 3 standard cycles on the test battery, and recording the capacity of the last standard cycle as the initial capacity of the test battery.

[0040] The experimental purpose of this experiment is to test the battery capacity fading of the test battery under the combined influence of the preset temperature, the preset time and the preset state of charge (SOC, hereinafter referred to as SOC), so the temperature, time and SOC of the test battery in the test environment are set as the capacity fading factors of this experiment, and the test battery is divided into 9 test groups for this experiment. The corresponding temperature and SOC of each test group are shown in Table 1 as follows:

[0041]

[0042]

[0043] Table 1

[0044] As shown in Table 1, the test battery is divided into test group 1 to test group 9; wherein the preset test temperature is 5℃, 25℃, 40℃, the preset SOC is the highest SOC used, 0.75 SOC of the use interval, and 0.2 SOC of the use interval.

[0045] S106, respectively put the test battery into the experimental environment corresponding to its group, and perform 30-day cumulative test.

[0046] In the experiment, the temperature in the above experimental environment can be set by human, in order to maintain the corresponding environment temperature, the experiment can be carried out in a thermostat; in order to ensure that the SOC of the above test battery meets the SOC standard corresponding to its group, the SOC of the above test battery can be adjusted to the target SOC by charging and discharging the above test battery.

[0047] In practical application, the above test battery is respectively put into the experimental environment corresponding to its group, and the cumulative test of storage for 30 days is carried out, and the battery capacity after the last cycle is taken as the residual capacity Ci of the above test battery for 30 days. Among them, the battery capacity is counted once every 30 days, and the above preset time can be 30i days.

[0048] S108, respectively, the capacity of the above test battery in the above 9 groups of experiments at the above preset time is obtained.

[0049] S110, the test data of the above test battery is obtained, and the data fitting is carried out to obtain the semi-empirical model for calendar life evaluation of the above test battery.

[0050] Among them, data fitting refers to the experimental data obtained by mathematical method, that is, the number formula for statistical law of the experimental data can be determined by the obtained experimental data.

[0051] The semi-empirical model of calendar life evaluation refers to the model for evaluating calendar life based on experimental data.

[0052] In practical application, the above experimental data is obtained, and the semi-empirical model for calendar life evaluation of the above test battery is obtained by data fitting.

[0053] In practical application, in the above experiment, the capacity decay of the battery is strongly affected by temperature and time, when the temperature and time are used as the capacity decay factor, the capacity decay of the battery follows the Arrhenius kinetic expression, that is:

[0054]

[0055] Among them, Q LOSS is the calendar capacity loss, B is the pre-exponential factor, E a is the activation energy, R is the molar gas constant, T is the absolute temperature, t is the calendar storage time, and z is a dimensionless number; and when the above experimental data is fitted, the above B, E a , R and z can be treated as constants.

[0056] For example, taking the experimental data of test groups 1-3 as an example, since the SOC of the above-mentioned test battery of test groups 1-3 is a consistent value, and the preset variable factor is time and temperature, the experimental data of test groups 1-3 can be fitted to obtain a semi-empirical evaluation model of the capacity attenuation of the above-mentioned test battery as the highest SOC of use changes with temperature and time:

[0057] Wherein, the experimental data of the capacity attenuation of the above-mentioned test battery as the highest SOC of use changes with temperature and time can be referred to Table 2, as shown in the following table:

[0058]

[0059] Table 2

[0060] Table 2 is a statistical table of the capacity degradation of the above-mentioned test battery at each time / temperature when the SOC of the above-mentioned test battery is the highest SOC of use; there is also a statistical table of the capacity degradation of the above-mentioned test battery at each time / temperature when the SOC is 0.75 SOC of the use interval, and a statistical table of the capacity degradation of the above-mentioned test battery at each time / temperature when the SOC is 0.2 SOC of the use interval, which is not listed in this specification.

[0061] In practical applications, the calendar life of the battery to be evaluated can usually be evaluated by the above-mentioned semi-empirical model for calendar life evaluation, but since the semi-empirical model for calendar life evaluation is obtained by fitting ideal data generated in an absolutely ideal and stable experimental environment according to experimental data, the calendar life of the battery to be evaluated obtained by using the semi-empirical model for calendar life evaluation is obviously too ideal; and since the user's usage habits also affect the calendar life of the battery, based on this, the evaluation data obtained by using the semi-empirical model for calendar life evaluation to evaluate the calendar life of the battery to be evaluated has poor accuracy.

[0062] Therefore, the present application aims to provide a battery calendar life evaluation method, which uses a model that fuses the actual usage data of the battery to be evaluated to evaluate the calendar life of the battery to be evaluated.

[0063] In implementation, since the semi-empirical model for calendar life evaluation does not consider the influence of user's usage habits on the calendar life of the battery to be evaluated when evaluating the calendar life of the battery, the calendar life of the battery to be evaluated can be evaluated by the first evaluation model fused with the actual usage data of the user for the battery to be evaluated to accurately evaluate the calendar life of the battery to be evaluated used by the user.

[0064] In the above technical solution, on the basis of the calendar aging capacity attenuation of the battery to be evaluated in the non-stop environment obtained through experiments, the actual capacity attenuation of the battery caused by the user's usage habits is combined with the error between the capacity attenuation obtained through experiments, so that the first evaluation model is obtained by fusing the actual usage data of the user on the basis of the obtained experimental data; and the evaluation data of the calendar life of the battery to be evaluated can be more accurate through the first evaluation model.

[0065] The application will be described below through specific embodiments and in combination with specific application scenarios.

[0066] Please refer to Figure 2 , Figure 2 is a flowchart of another battery calendar life evaluation method according to an example embodiment. The method comprises the following steps:

[0067] S202, obtaining the cell type of the battery to be evaluated, and determining a first evaluation model corresponding to the cell type; wherein the first evaluation model comprises a semi-empirical model obtained by data fitting based on capacity attenuation test data obtained through calendar aging test, and a model obtained by fusing usage data of the user for the battery to be evaluated; the usage data of the user for the battery to be evaluated reflects the actual usage habits of the user for the battery to be evaluated.

[0068] Wherein, the data fitting and the semi-empirical model for calendar life evaluation have been described in detail in the foregoing, and will not be described again here.

[0069] The cell type can include the battery model of the battery to be evaluated, and the characteristics and features of the battery to be evaluated can be queried and obtained based on the battery model of the battery to be evaluated; wherein the characteristics and features of the battery to be evaluated can include the cell material, battery specification, battery capacity and other parameters of the battery of the battery to be evaluated, which will not be limited further in this specification.

[0070] The semi-empirical model for calendar life evaluation is fitted by capacity decay test of calendar aging test of the battery to be tested. The semi-empirical model for calendar life evaluation is used to evaluate the calendar life of the battery, which is mainly used to evaluate the capacity decay of the battery under different temperatures, use time and SOC of the battery, and is mainly used to evaluate the capacity decay characteristics of the battery.

[0071] The first evaluation model is an evaluation model fused by the actual use data of the user for the battery to be evaluated on the basis of the semi-empirical model for calendar life evaluation. The actual use data of the user can reflect the use habit of the user for the battery to be evaluated. Therefore, by fusing the actual use data of the user, the use habit of the user for the battery to be evaluated is also taken as an influencing factor for evaluating the calendar life of the battery to be evaluated. Obviously, compared with the evaluation model of the battery calendar life only considering the characteristics of the battery, the evaluation value of the first evaluation model is more accurate.

[0072] In the present specification, the type of the battery to be evaluated can be obtained, so that it can be determined that the model for calendar life evaluation is the first evaluation model.

[0073] In an embodiment shown, the use data of the user for the battery to be evaluated can include SOC data of the battery to be evaluated during use of the battery to be evaluated by the user; and fusing the semi-empirical model with the use data of the user for the battery to be evaluated to obtain the first evaluation model can include fusing the semi-empirical model with the SOC data of the battery to be evaluated generated during use of the battery to be evaluated by the user to obtain the first evaluation model.

[0074] It should be noted that the use data of the user for the battery to be evaluated can refer to the SOC data of the battery to be evaluated generated by the battery to be evaluated during use of the battery to be evaluated by the user. The SOC data of the battery to be evaluated can reflect the use habit of the user for the battery to be evaluated.

[0075] For example, the SOC data can be used to count the frequency of the SOC of the battery to be evaluated at a certain value used by the user, so as to infer the power consumption habits of the user (when the SOC of the battery is in a certain range, charging is needed, the SOC value at the end of charging, the SOC of the battery to be tested before use) and the like, and then infer that the SOC value corresponding to the capacity decline occurs most frequently in the capacity decline process of the battery to be evaluated, so as to correct the semi-empirical model according to the power consumption habits of the user to obtain the first evaluation model.

[0076] In an embodiment shown, the SOC data of the battery to be evaluated can be discharge starting SOC data, wherein the discharge starting SOC refers to the SOC value displayed on the electric vehicle dashboard from the static state to the driving state of the electric vehicle equipped with the battery to be evaluated.

[0077] In another embodiment shown, the discharge starting SOC data can be the SOC value displayed on the first frame of the electric vehicle dashboard.

[0078] In the present application, since the first evaluation model is based on the semi-empirical model and combines the use data that can reflect the user's use habits for the battery to be evaluated, compared with the semi-empirical model (only considering the battery capacity attenuation of the battery in different environments), the actual use data of the user is referenced, the error between the actual capacity decline of the battery to be evaluated due to different user actual use habits and the capacity decline inferred by the experiment is reduced, and thus the accuracy of evaluating the battery to be evaluated can be improved.

[0079] In an embodiment shown, the fusion of the semi-empirical model and the SOC data of the battery to be evaluated generated in the process of the user using the battery to be evaluated to obtain the first evaluation model can include:

[0080] The SOC data of the battery to be evaluated in the process of the user using the battery to be evaluated can be obtained, and the obtained SOC data can be statistically analyzed to further obtain the SOC data distribution of the battery to be evaluated; wherein the SOC data distribution can include a plurality of SOC value intervals and a user proportion corresponding to each SOC value interval;

[0081] Then, the semi-empirical model can be fused with the SOC data distribution to obtain the first evaluation model.

[0082] For example, please refer to Figure 3 , Figure 3is a schematic diagram of a SOC distribution in which a calendar aging of a battery is shown according to an example embodiment. As shown in Figure 3 Figure 3 The horizontal axis of the SOC data distribution is a range of values of the SOC, which includes a plurality of value intervals of the SOC. Figure 3 The vertical axis of the SOC data distribution represents a user proportion corresponding to each of the plurality of value intervals of the SOC.

[0083] In an example embodiment, the SOC data distribution further includes a plurality of SOC reference value intervals and a user proportion corresponding to each of the plurality of SOC reference value intervals. The semi-empirical model can be fused with the SOC data distribution including the user proportion corresponding to each of the plurality of SOC reference value intervals to obtain the first evaluation model.

[0084] For example, please continue to refer to Figure 3 As shown in Figure 3 , the SOC values on the horizontal axis can represent the preset SOC reference values, which can be 0, 10, 15, 20, etc. The value intervals of the SOC corresponding to the SOC reference values can be SOC reference value intervals formed by two adjacent SOC reference values in value size, which can be 0-10, 10-15, 15-20, etc. Details are not described here, and please refer to Figure 3 .

[0085] The distribution diagram shown in Figure 3 can clearly and intuitively observe the distribution between the SOC of the battery to be evaluated and the use habits of the user. It can be clearly known that the user proportion corresponding to each of the SOC reference value intervals of the battery to be evaluated when the user actually uses the battery to be evaluated. Then, the semi-empirical model can be corrected based on the SOC data distribution to obtain the first evaluation model.

[0086] In an example embodiment, the fusion of the semi-empirical model and the SOC data distribution can obtain the first evaluation model, which can include: based on the semi-empirical model, a first capacity attenuation factor corresponding to each of a plurality of preset SOC reference values of the battery to be evaluated can be obtained, which changes with a preset variable. The preset variable can include time and temperature.

[0087] The user proportion corresponding to each of the SOC reference value intervals in the SOC data distribution can be fused with the first capacity attenuation factor corresponding to each of the preset SOC reference values to obtain the first evaluation model.

[0088] ​In the present application, according to the foregoing embodiment one, each SOC value corresponds to a semi-empirical model, and the expression of the semi-empirical model is: wherein the capacity degradation factor in the expression is temperature and time.

[0089] The above-mentioned preset variable can correspond to the above-mentioned capacity degradation factor, and specifically can include time and temperature.

[0090] In the above-mentioned embodiment, the user proportion corresponding to the above-mentioned SOC reference value interval in the above-mentioned SOC data distribution can be fused with the first capacity attenuation factor corresponding to each SOC reference value interval to obtain the above-mentioned first evaluation model.

[0091] In the illustrated embodiment, the user proportion corresponding to the above-mentioned SOC reference value interval in the above-mentioned SOC data distribution can be fused with the first capacity attenuation factor corresponding to the SOC reference value to obtain the first evaluation model, which can include: the user proportion corresponding to the above-mentioned SOC reference value interval in the above-mentioned SOC data distribution can be weighted and summed with the first capacity attenuation factor corresponding to each SOC reference value, so that the first evaluation model can be obtained.

[0092] For example, the first evaluation model can be specifically represented as:

[0093] C loss(T,t,SOC) =∑Q loss(T,t)SOC *W SOC

[0094] wherein C loss(T,t,SOC) represents the capacity attenuation amount of the battery to be evaluated, Q loss(T,t) represents the battery capacity attenuation amount corresponding to the change of the battery to be evaluated with time and temperature in the semi-empirical model, and W SOC represents the user proportion corresponding to each SOC reference value interval in the SOC data distribution.

[0095] S204, obtaining the evaluation parameter of the battery to be evaluated.

[0096] S206, inputting the obtained evaluation parameter into the first evaluation model to perform calendar life evaluation on the battery to be evaluated.

[0097] The above-mentioned evaluation parameter includes the evaluation parameter for performing calendar life evaluation on the battery to be evaluated using the first evaluation model; the evaluation parameter can include: the time parameter, the SOC parameter, and the temperature parameter in the first evaluation parameter.

[0098] The evaluation parameters are obtained, and the obtained evaluation parameters are input into the first evaluation model, so that the battery capacity degradation under the condition corresponding to the parameters can be calculated according to the first evaluation model, and then the calendar life of the battery to be evaluated can be further evaluated according to the calculated battery capacity degradation.

[0099] Corresponding to the embodiment of the battery calendar life evaluation method, the specification also provides an embodiment of a battery calendar life evaluation device.

[0100] Please refer to Figure 4 , Figure 4 is an exemplary embodiment of a battery calendar life evaluation device in an electronic device. At the hardware level, the device includes a processor 402, an internal bus 404, a network interface 406, a memory 408, and a non-volatile memory 410, and of course can also include other hardware required by the business. One or more embodiments of the specification can be implemented in software, such as reading the corresponding computer program from the non-volatile memory 410 into the memory 408 by the processor 402 and then running. Of course, in addition to the software implementation, one or more embodiments of the specification do not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc., that is, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic device.

[0101] Please refer to Figure 5 , Figure 5 is an exemplary embodiment of a battery calendar life evaluation device. The battery calendar life evaluation device can be applied to an electronic device as shown in Figure 4 , so as to realize the technical solutions of the specification. Wherein, the battery calendar life evaluation device can include:

[0102] The first evaluation model determination module 502 is configured to obtain the battery cell type of the battery to be evaluated, and determine the first evaluation model corresponding to the battery cell type; wherein, the first evaluation model includes a semi-empirical model obtained by data fitting based on the capacity attenuation test data obtained by the calendar aging test, and a model obtained by fusing the user's use data for the battery to be evaluated; the user's use data for the battery to be evaluated reflects the user's actual use habit for the battery to be evaluated;

[0103] The calendar life evaluation module 504 is configured to obtain the evaluation parameters of the battery to be evaluated, and input the obtained evaluation parameters into the first evaluation model, so as to evaluate the calendar life of the battery to be evaluated.

[0104] The implementation process of the functions and roles of each unit in the above apparatus is specifically described in the implementation process of the corresponding steps in the above method, which will not be repeated here.

[0105] For the device embodiment, since it basically corresponds to the method embodiment, the relevant part can be referred to the part of the method embodiment. The device embodiments described above are only illustrative, wherein the units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, that is, they can be located in one place or distributed on multiple network units. According to the actual needs, some or all of the modules can be selected to achieve the purpose of the scheme of the present specification. Those skilled in the art can understand and implement it without creative labor.

[0106] The system, device, module or unit illustrated in the above embodiments can be specifically implemented by a computer chip or entity, or by a product with certain functions. A typical implementation device is a computer, and the specific form of the computer can be a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email transceiver device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0107] In a typical configuration, the computer includes one or more processors (CPU), input / output interface, network interface and memory.

[0108] The memory can include non-persistent memory in computer readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of computer readable media.

[0109] Computer-readable media includes permanent and non-permanent, moveable and non- moveable media that can be implemented by any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disks (DVDs) or other optical storage, magnetic cassettes, magnetic disks storage, quantum memory, graphene-based storage media, or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definitions herein, computer-readable media does not include transitory media, such as modulated data signals and carrier waves.

[0110] It is also to be noted that the terms "comprising", "including", and any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises a... " does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0111] The above description of certain embodiments of the disclosure has been presented for the purposes of illustration and description. Other embodiments are within the scope and range of equivalents of the claims. In some cases, acts or steps can be performed in an order different from that of the embodiments, and still accomplish the desired results. Additionally, the processes depicted in the accompanying figures can not necessarily require the particular order shown, or sequential order to achieve the desired results. In certain implementations, multitasking and parallel processing can be advantageous.

[0112] The terminology used in this description of one or more embodiments of the disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of one or more embodiments of the disclosure. As used in this description of one or more embodiments of the disclosure and the accompanying claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term "and / or," as used herein, refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0113] It should be understood that, although the terms first, second, third, etc. can be used herein to describe various information, the information should not be limited to these terms. These terms are only used to differentiate one piece of information from another. For example, a first information can also be termed a second information, and, similarly, a second information can also be termed a first information, without departing from the scope of the one or more embodiments. The word "if' can be interpreted to mean "upon" or "when" or "in response to the determination" depending on the context.

[0114] The foregoing description of the one or more embodiments of the present disclosure is by way of example only, and other embodiments of the present disclosure are within the scope of the present disclosure as delineated by the claims, with equivalents of the claims to be included therein.

Claims

1. A method for assessing the calendar life of a battery, the method comprising: The cell type of the battery to be evaluated is obtained, and a first evaluation model corresponding to the cell type is determined. The first evaluation model includes a semi-empirical model for evaluating the calendar life of the battery to be evaluated by fitting data from capacity decay test data obtained from calendar aging tests, and a model obtained by fusing the SOC data of the battery to be evaluated generated during user use of the battery. The SOC data of the battery to be evaluated reflects the user's usage habits for the battery to be evaluated. Obtain the evaluation parameters of the battery to be evaluated; The obtained evaluation parameters are input into the first evaluation model to evaluate the calendar life of the battery to be evaluated. The first evaluation model is obtained by fusing the semi-empirical model with the SOC data of the battery under evaluation generated during the user's use of the battery under evaluation, including: The SOC data of the battery to be evaluated generated during the user's use of the battery to be evaluated is obtained, and the obtained SOC data is statistically analyzed to obtain the SOC data distribution of the battery to be evaluated; wherein, the SOC data distribution includes several SOC value intervals and the user proportion corresponding to each SOC value interval; The semi-empirical model is fused with the SOC data distribution to obtain the first evaluation model.

2. The method according to claim 1, wherein the SOC data includes discharge initiation SOC data; wherein, The discharge start-of-charge (SOC) data includes the SOC data displayed on the dashboard of the electric vehicle equipped with the battery to be evaluated, from when it was at rest until it was driven.

3. The method according to claim 2, wherein the discharge start-up SOC data includes the SOC value displayed on the electric vehicle's dashboard in the first frame from when the electric vehicle is stationary to when it is driven.

4. The method according to claim 1, wherein the SOC data distribution includes several preset SOC benchmark value intervals and the user percentage corresponding to the SOC benchmark value intervals; The step of fusing the semi-empirical model with the SOC data distribution to obtain the first evaluation model includes: Based on the semi-empirical model, a first capacity decay factor is obtained corresponding to several preset SOC benchmark values ​​for the battery to be evaluated, which vary with preset variables; wherein, the preset variables include time and temperature; The user percentage corresponding to the SOC benchmark value range included in the SOC data distribution is fused with the first capacity decay factor corresponding to the preset SOC benchmark value to obtain the first evaluation model.

5. The method according to claim 4, wherein fusing the user proportion corresponding to the SOC benchmark value interval included in the SOC data distribution with a preset first capacity decay factor corresponding to the SOC benchmark value to obtain the first evaluation model includes: The user percentage corresponding to each SOC benchmark value interval included in the SOC data distribution is weighted and summed with several first capacity attenuation factors corresponding to each preset SOC benchmark value to obtain the first evaluation model.

6. A battery calendar life assessment device, the device comprising: The first evaluation model determination module is used to obtain the cell type of the battery to be evaluated and determine the first evaluation model corresponding to the cell type. The first evaluation model includes a semi-empirical model for evaluating the calendar life of the battery to be evaluated by fitting data from capacity decay test data obtained from calendar aging tests, and a model obtained by fusing the SOC data of the battery to be evaluated generated during user use. The SOC data of the battery to be evaluated reflects the user's actual usage habits for the battery to be evaluated. An evaluation parameter acquisition module is used to acquire the evaluation parameters of the battery to be evaluated. The calendar life assessment module is used to input the acquired assessment parameters into the first assessment model to assess the calendar life of the battery to be assessed. The first evaluation model is obtained by fusing the semi-empirical model with the SOC data of the battery under evaluation generated during the user's use of the battery under evaluation, including: The SOC data of the battery to be evaluated generated during the user's use of the battery to be evaluated is obtained, and the obtained SOC data is statistically analyzed to obtain the SOC data distribution of the battery to be evaluated; wherein, the SOC data distribution includes several SOC value intervals and the user proportion corresponding to each SOC value interval; The semi-empirical model is fused with the SOC data distribution to obtain the first evaluation model.

7. An electronic device, comprising a communication interface, a processor, a memory, and a bus, wherein the communication interface, the processor, and the memory are interconnected via the bus; The memory stores machine-readable instructions, and the processor executes the method according to any one of claims 1 to 5 by invoking the machine-readable instructions.

8. A machine-readable storage medium storing machine-readable instructions that, when invoked and executed by a processor, implement the method of any one of claims 1 to 5.

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