A method and device for predicting calendar life of a battery, an electronic device and a medium

Abstract

This invention provides a method, apparatus, electronic device, and medium for predicting battery calendar life. The prediction method includes the following steps: constructing a prediction model between combined health factors and a storage independent variable; and numerically fitting the prediction model using experimental data from the storage independent variable and the combined health factors during the storage test, solving for the model parameters in the prediction model, and obtaining a prediction model for battery calendar life. The combined health factors include indirect health factors and direct health factors. This invention selects a combination of direct and indirect health factors for numerical fitting to examine the contribution ratio of each electrochemical parameter in the later capacity loss. The fitting effect is excellent, and the battery life evaluation cycle is greatly shortened, with high prediction accuracy.

Description

Technical Field

[0001] This invention relates to the field of battery life prediction technology, and in particular to a method, apparatus, electronic device, and medium for predicting battery calendar life. Background Technology

[0002] Calendar life refers to the time required for a battery to reach the end of its lifespan under certain environmental conditions. Energy storage and power devices increasingly demand higher performance stability from batteries under storage conditions, making calendar life a crucial aspect of lifespan assessment. In today's rapidly evolving product market, experimentally examining the performance stability of batteries after long-term storage is extremely time-consuming and costly. Therefore, using early calendar life data to build models and predict long-term calendar life degradation is of great significance.

[0003] Moreover, increasingly demanding customer needs are driving the need to shorten product development cycles, making the shortening of life evaluation cycles an urgent issue to be addressed. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a method, device, electronic device and medium for predicting battery calendar life. It can predict the change trend of battery calendar life in the later period using only short-term test data, and has good application prospects in the prediction of sodium-ion battery life.

[0005] To achieve this objective, the present invention adopts the following technical solution:

[0006] In a first aspect, the present invention provides a method for predicting battery calendar life, the method comprising the following steps:

[0007] A predictive model is constructed between the combined health factors and the shelved independent variables; the predictive model is numerically fitted using experimental data of the shelved independent variables and the combined health factors during the shelving test, and the model parameters in the predictive model are solved to obtain the predictive model of battery calendar life; wherein, the combined health factors include indirect health factors and direct health factors.

[0008] The method for predicting the calendar life of batteries described in this invention is preferably designed for predicting the calendar life of sodium-ion batteries.

[0009] Unlike existing technologies, this invention introduces indirect health factors, which, compared to using only direct health factors, facilitates the determination of the contribution ratio of recovery capacity loss rate, thereby helping to improve the accuracy of predicting later calendar life. Moreover, by finding a suitable prediction model, this invention can achieve accurate prediction of battery calendar life solely through numerical fitting, without the need for big data computing models, resulting in low computational load, short prediction time, and broad application prospects.

[0010] Preferably, the direct health factor includes the recovery volume loss rate Q. loss .

[0011] Preferably, the indirect health factor includes polarization loss Q. P Irreversible loss Q irr DCR instantaneous impedance R ohm+ct and DCR diffusion impedance R d .

[0012] Indirect health factors include polarization loss Q P Irreversible loss Q irr DCR instantaneous impedance R ohm+ct DCR diffusion impedance R d EIS Ohmic Impedance R ohm EIS interface impedance R ct EIS diffusion impedance R d This invention, through research, has discovered that by using only the polarization loss Q... P Irreversible loss Q irr DCR instantaneous impedance R ohm+ct and DCR diffusion impedance R d These four indirect health factors can be evaluated to obtain a prediction model with high accuracy. Currently, the accuracy of EIS (Electrochemical Impedance Spectroscopy) related detection data is not high, and the accuracy of the original data will also affect the solution of prediction model parameters and the construction process of prediction model. Therefore, by selecting the above-mentioned indirect health factors, the prediction accuracy of the prediction model is significantly improved.

[0013] The aforementioned EIS ohmic impedance R ohm This refers to the total resistance of the battery obtained from electrochemical impedance spectroscopy (EIS). EIS interfacial impedance R ct This refers to the charge transfer impedance at the interface between the battery electrode and the electrolyte, obtained from electrochemical impedance spectroscopy (EIS). EIS diffusion impedance R d It refers to the diffusion impedance of ions in the electrolyte within the battery, obtained from electrochemical impedance spectroscopy analysis.

[0014] Those skilled in the art will understand that the recovery capacity loss rate Q in this invention loss It refers to the percentage loss of a battery's capacity relative to its initial capacity after a period of storage, usually expressed as a percentage.

[0015] In this invention, the polarization loss Q PPolarization loss refers to the phenomenon in DC resistance testing where the battery voltage deviates from the theoretical voltage value during charging and discharging due to the limitation of the internal electrochemical reaction rate. Specifically, polarization loss consists of two main parts: ohmic polarization (electrolyte conductivity) and electrochemical polarization (determined by battery reaction kinetics).

[0016] Irreversible loss Q in this invention irr This refers to the energy loss caused by irreversible electrochemical reactions during the charging, discharging, or storage of a battery, which cannot be recovered through simple charging or discharging processes.

[0017] In this invention, the instantaneous impedance R of the DCR ohm+ct DC resistance refers to the total internal resistance of a battery measured at a specific moment during a DC resistance test, encompassing all resistive components within the battery. Typically, the instantaneous impedance of a DC resistance test consists of two parts: ohmic internal resistance (R...). ohm Charge transfer resistance (R): The direct resistance of the battery's internal materials (such as electrolyte and electrodes) to the electric current. ct DCR (Discharge Resistance) refers to the resistance during charge transfer on the battery electrode surface, and is usually related to the battery's chemical reaction rate. The magnitude of DCR instantaneous impedance is an important indicator for evaluating a battery's internal resistance, thermal effects, and power output capability. The unit of DCR instantaneous impedance is Ω.

[0018] In this invention, the DCR diffusion impedance R d This refers to the impedance caused by the diffusion process of ions within the battery in the electrolyte during DC resistance testing. DCR diffusion impedance R d The unit is Ω.

[0019] Preferably, the shelving independent variable includes the shelving period t. i The storage temperature T or the storage state of charge (SOC) of the battery, or any one or at least a combination of two of these, preferably a combination of all three.

[0020] In this invention, the shelving period t i The term "storage temperature" (T) refers to the time from the initial state of the battery under test to the commencement of the DCR (Direct Current Resistance Test), expressed in 100 days. The storage temperature (T) refers to the temperature at which the battery under test is stored, expressed in °C. The State of Charge (SOC) of a stored battery is an important parameter indicating its current charge level, typically used to describe the relative percentage of energy stored in the battery. SOC represents the ratio between the battery's current charge level and its maximum charge level, usually expressed as a percentage (%).

[0021] Preferably, the predictive model between the combined health factors and the shelved independent variables is shown in equation (1):

[0022]

[0023] In equation (1), A represents the model parameter to be solved.

[0024] Through various attempts, this invention has found that the prediction model between combined health factors and shelved independent variables conforms to the above-mentioned pattern and has a high degree of numerical fit.

[0025] Preferably, the prediction method further includes: constructing a coupled model of indirect health factors to solve for the recovery capacity loss rate Q. loss .

[0026] Preferably, the coupling model of the indirect health factor is shown in equation (2):

[0027] Q P *Q irr *R ohm+ct *R d =B*t i 2.16 Equation (2).

[0028] Where B represents the model parameters to be solved.

[0029] In this invention, in order to solve the problem of Q after it has been set aside loss The solution requires the values ​​of electrochemical parameters (i.e., indirect health factors). In this model, the product of the electrochemical parameters, Q, is used. P *Q irr *R ohm+ct *R d An indirect health factor coupling model is constructed as a whole. By constructing the coupling model according to equation (2), the values ​​of the above-mentioned indirect health factors can be calculated relatively well.

[0030] Preferably, A in equation (1) is a function of the storage temperature and the state of charge of the stored battery.

[0031] Preferably, A is exponentially related to the storage temperature and the state of charge of the stored battery.

[0032] Preferably, the calculation model formula for A in equation (1) is shown in equation (3):

[0033]

[0034] In equation (3), a1, b1, c1, and d1 are the model parameters of the calculation model for A; T0 is the reference ambient temperature in °C; and SOC0 is the reference state of charge of the battery in % (%).

[0035] In this invention, 25°C is used as the base ambient temperature and 50% SOC is used as the base state of charge for the battery.

[0036] Preferably, B in equation (2) is a function of the storage temperature and the state of charge of the storage battery.

[0037] Preferably, A is exponentially related to the storage temperature and the state of charge of the stored battery.

[0038] Preferably, the calculation model for B in equation (2) is shown in equation (4):

[0039]

[0040] In equation (4), a2, b2, c2, c0, d2, and d0 are model parameters in the calculation model of B.

[0041] Preferably, the acquisition of experimental data on the independent variables and combined health factors during the suspension test includes: obtaining experimental data on the initial state of the battery and the independent variables and combined health factors during the suspension test using the DCR test.

[0042] Preferably, the acquisition of experimental data on the independent variables and combined health factors during the postponement of the test includes:

[0043] S211. Perform DCR testing on the initial state of the battery to be evaluated and calculate the indirect health factor.

[0044] S212. Conduct a storage test on the battery to be tested. During the storage test period, perform a DCR test on the battery to be tested and record the storage independent variable and the indirect health factor corresponding to the storage independent variable.

[0045] Specifically, in step S211, the battery under test is charged and discharged at 0.5C and 0.05C at room temperature (25°C) to obtain the initial capacity data for the battery's Beginning of Life (BOL). Additionally, a DCR test is performed at 50% SOC and 25°C to calculate the polarization loss Q under the BOL state. P Irreversible loss Q irr DCR instantaneous impedance R ohm+ct and DCR diffusion impedance R d .

[0046] In step S212, multiple sets of batteries are set up as parallel samples and placed in constant temperature chambers at different temperatures for storage testing.

[0047] The DCR test includes: charging and discharging the battery under test at 0.5C and 0.05C at 25°C, and performing the DCR test at 50% SOC and 25°C.

[0048] Preferably, the constant temperature during the placement test is 25 to 60°C, for example, it can be 25°C, 29°C, 33°C, 37°C, 41°C, 45°C, 49°C, 53°C, 57°C or 60°C, but is not limited to the listed values. Other unlisted values ​​within this range are also applicable.

[0049] Preferably, the state of charge (SOC) of the battery under test in the storage test is 50% to 100%, such as 50%, 60%, 70%, 80%, 90%, or 100%, but not limited to the listed values. Other unlisted values ​​within this range are also applicable, with 50% or 100% SOC being the preferred values.

[0050] In this invention, multiple sets of stored battery state of charge (SOC) and multiple sets of storage temperatures can be combined for testing. Multiple sets of data can be obtained from a single storage test, saving time in acquiring experimental data.

[0051] Preferably, the resting test period is 100 to 250 days, for example, it can be 100 days, 117 days, 134 days, 150 days, 167 days, 184 days, 200 days, 217 days, 234 days or 250 days.

[0052] Preferably, at least 6 sets of tests are set during the set-off test period, and the interval between each set of tests is the same. For example, it can be 6, 7, 8, 9, 10, 11 or 12 sets, but it is not limited to the listed values. Other unlisted values ​​within this range are also applicable.

[0053] In a second aspect, the present invention provides an apparatus utilizing the battery calendar life prediction method described in the first aspect, the apparatus comprising:

[0054] The model building module is used to build predictive models between combined health factors and set-off independent variables.

[0055] The data acquisition module is used to collect indirect health factors, shelved independent variables, and indirect health factors corresponding to the shelved independent variables in the initial state.

[0056] The calculation module is used to numerically fit the prediction model using experimental data of the independent variables and combined health factors that were shelved during the shelving test, and to solve for the model parameters in the prediction model.

[0057] The prediction module is used to predict the battery calendar life of the battery under test based on the prediction model.

[0058] Thirdly, the present invention provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the battery calendar life prediction method described in the first aspect.

[0059] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a processor to execute the battery calendar life prediction method described in the first aspect.

[0060] Compared with the prior art, the present invention has at least the following beneficial effects:

[0061] (1) The calendar life prediction method for batteries provided by this invention provides a relatively simple and reliable calendar life prediction model. It does not require high-end testing equipment and complex theoretical calculations. It only needs to fit short-term calendar life data to obtain the parameters of the long-life sodium-ion battery calendar life prediction model. Substituting Q loss Once the value is obtained, the lifetime t can be calculated, and the calendar lifetime can be predicted. This can greatly shorten the lifetime prediction cycle, save time and cost in sodium-ion battery evaluation, avoid waste of testing resources, and reduce testing costs.

[0062] (2) The battery calendar life prediction method provided by the present invention is more reliable in prediction accuracy than theoretical model because the model parameters are obtained by fitting the previous experimental data. The relative error of the prediction is only within 0.55%, and it is simple and has stronger universal applicability. Attached Figure Description

[0063] Figure 1 This is the case of fitting the Y value when the device is placed at 50% SOC and 25°C in Example 1 of the present invention;

[0064] Figure 2 This is the case of fitting the Y value when the device is placed at 50% SOC and 45°C in Example 1 of the present invention;

[0065] Figure 3 This is the case of fitting the Y value when the device is placed at 50% SOC and 60℃ in Example 1 of the present invention;

[0066] Figure 4 This is the case of fitting the Y value when the device is placed at 100% SOC and 25°C in Example 1 of the present invention;

[0067] Figure 5 This is the case of fitting the Y value when the device is placed at 100% SOC and 45°C in Example 1 of the present invention;

[0068] Figure 6 This is the case of fitting the Y value when the device is placed at 100% SOC and 60℃ in Example 1 of the present invention;

[0069] Figure 7 This is the fitting result of the y-value when the device is placed at 50% SOC and 25°C in Example 1 of the present invention;

[0070] Figure 8 This is the case of fitting the y-value when the device is placed at 50% SOC and 45°C in Example 1 of the present invention;

[0071] Figure 9 This is the case of fitting the y-value when the device is placed at 50% SOC and 60℃ in Example 1 of the present invention;

[0072] Figure 10 This is the fitting result of the y-value when the device is placed at 100% SOC and 25°C in Example 1 of the present invention;

[0073] Figure 11 This is the case of fitting the y-value when the device is placed at 100% SOC and 45°C in Example 1 of the present invention;

[0074] Figure 12 This is the case of fitting the y-value when the device is placed at 100% SOC and 60℃ in Example 1 of the present invention;

[0075] Figure 13 This refers to the fitting of the remaining lifespan (SOH) according to the calendar lifespan model when the temperature is set at 50% SOC and 25℃, 50% SOC and 45℃, 50% SOC and 60℃, 100% SOC and 25℃, 100% SOC and 45℃, and 100% SOC and 60℃ in Example 1 of the present invention.

[0076] Figure 14 This is the case where the remaining lifespan (SOH) at 75% SOC and 35°C during storage in Example 1 of the present invention is fitted according to the calendar lifespan model.

[0077] Figure 15 This is a schematic diagram of the electronic device provided in Example 2.

[0078] Figure 15 In the diagram, 10-electronic device, 11-processor, 12-ROM, 13-RAM, 14-bus, 15-I / O interface, 16-input unit, 17-output unit, 18-storage memory, 19-communication unit. Detailed Implementation

[0079] To facilitate understanding of the present invention, the following embodiments are provided. Those skilled in the art should understand that these embodiments are merely illustrative and should not be construed as limiting the scope of the invention.

[0080] It should be noted that the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or device.

[0081] Example 1

[0082] This embodiment provides a method for predicting battery calendar life, the prediction method including the following steps:

[0083] S1. Construct a predictive model between combined health factors and shelved independent variables. Specifically, this includes:

[0084] S11, Select polarization loss Q P Irreversible loss Q irr DCR instantaneous impedance R ohm+ct and DCR diffusion impedance R d As an indirect health factor, the recovery volume loss rate Q loss As a direct health factor; the independent variable is the shelving period t. i , storage temperature T or storage battery state of charge (SOC).

[0085] The predictive model between the combined health factors and the shelved independent variables is shown in Equation (1):

[0086]

[0087] In equation (1), A is the model parameter to be solved, and Y is a substitute symbol for easy calculation, representing the electrochemical data quotient.

[0088] S12. Construct a coupled model of indirect health factors to solve for the recovery volume loss rate Q. loss ;

[0089] The coupling model of the indirect health factors is shown in equation (2):

[0090] y = Q P *Q irr *R ohm+ct *R d =B*t i 2.16 Equation (2);

[0091] Where B represents the model parameters to be solved, and y is a substitute symbol for easy calculation, representing the product of electrochemical data.

[0092] S13. Construct computational models for A and B respectively.

[0093] The calculation model formula for A in equation (1) is shown in equation (3):

[0094]

[0095] In equation (3), a1, b1, c1 and d1 are the model parameters of the calculation model of A; T0 is the reference ambient temperature and SOC0 is the reference state of charge of the battery.

[0096] The calculation model for B in equation (2) is shown in equation (4):

[0097]

[0098] In equation (4), a2, b2, c2, c0, d2, and d0 are model parameters in the calculation model of B.

[0099] The baseline ambient temperature is 25℃, and the baseline SOC0 is 50% for the battery's state of charge.

[0100] S2. Obtain experimental data of the independent variables and combined health factors during the postponement testing period, and numerically fit the prediction model to solve for the model parameters in the prediction model. Specifically, this includes:

[0101] S21. Experimental data on the initial state of the battery, the independent variables during the storage test, and the combined health factors were obtained using the DCR test. Specifically, this includes:

[0102] S211. Perform DCR testing on the battery under test in its initial state and calculate the indirect health factor. Specifically, the battery under test is charged and discharged at 0.5C and 0.05C at room temperature (25℃) to obtain the initial capacity data for the battery's Beginning of Life (BOL). DCR testing is then performed at 50% SOC and 25℃ to calculate the polarization loss Q under the BOL state. P Irreversible loss Q irr DCR instantaneous impedance R ohm+ct and DCR diffusion impedance R d .

[0103] S212. Adjust the state of charge (SOC) of the stored battery to be tested to obtain parallel groups of batteries to be tested with different SOCs (selecting SOCs of 50% and 100% for the stored batteries).

[0104] Three groups of test batteries with two different states of charge (SOC) were placed in three constant temperature chambers (25℃, 45℃, and 60℃) for storage testing. During the storage test period (tested every 30 days for a total of 180 days), the test batteries were charged and discharged at 0.5C and 0.05C at 25℃, and DCR was tested at 50% SOC and 25℃. The storage independent variable and the indirect health factor corresponding to the storage independent variable were recorded.

[0105] S22. The prediction model is numerically fitted using experimental data of the independent variables and combined health factors that were left unused during the test period. The model parameters in the prediction model are solved to obtain the prediction model for battery calendar life.

[0106] The data is fitted according to equation (1), and the fitting graphs under different states are shown below. Figures 1-6 As shown in Table 1, based on the fitting results, the solution for the model parameter A in this embodiment is as follows.

[0107] Table 1

[0108]

[0109] Based on the values ​​of model parameter A in Table 1 and equation (3), the values ​​of the model parameters of the calculation model of A are obtained by data fitting: a1 = 0.7309; b1 = 77.07; c1 = 0.6996; d1 = 1.1703.

[0110] Based on equation (3), the A value was predicted for different storage temperatures and storage battery charge states. The comparison between the results and experimental values ​​is shown in Table 2.

[0111] Table 2

[0112]

[0113] The data is fitted according to equation (2), and the fitting graphs under different states are shown below. Figures 7-12 As shown in Table 3, based on the fitting results, the solution of the model parameter B value in this embodiment is as follows.

[0114] Table 3

[0115]

[0116] Based on the values ​​of model parameter A in Table 3 and equation (4), the values ​​of the model parameters of the calculation model of A are obtained by data fitting: a2 = -0.00205; b2 = 1243.35; c2 = 18.456; d2 = 3.5543; c0 = 1.9997; d0 = 0.9266.

[0117] Based on equation (4), the B value was predicted for different storage temperatures and storage battery charge states. The comparison between the results and experimental values ​​is shown in Table 4.

[0118] Table 4

[0119]

[0120] Based on the A and B values ​​obtained from Tables 2 and 4, lifetime prediction curves were constructed under different temperatures and SOC conditions. The calendar lifetime prediction curves under the measured conditions are shown below. Figure 13 As shown.

[0121] The calendar lifetime was predicted under the test conditions of 75% SOC and 35℃ storage. See the detailed calendar lifetime curves below. Figure 14 . Figures 13-14 SOH refers to the ratio of the battery's current effective capacity to its design capacity, and is calculated using the formula: SOH = 1 - Q. loss .

[0122] To verify the accuracy of the prediction model in this embodiment, a verification test was conducted. Experiments were performed under conditions of 75% SOC and 35°C, and the SOH obtained at the corresponding resting time was compared with... Figure 14 Table 5 shows a comparison of the SOH calculated by the prediction model.

[0123] Table 5

[0124] Shelving time Experimental data SOH Forecast data relative error 30 days 97.82％ 97.97％ 0.15％ 60 days 97.51％ 97.40％ 0.11％ 90 days 96.98％ 96.99％ 0.00％ 180 days 95.61％ 96.14％ 0.55％ 360 days 95.12％ 95.04％ 0.08％

[0125] As can be seen from Table 5, the battery life prediction method provided by this invention has high prediction accuracy, with a relative error of only within 0.55%, and the calculation scheme is simple, easy to operate, and saves time in life prediction.

[0126] Comparative Example 1

[0127] This comparative example provides a method for predicting battery calendar life. This method directly constructs the relationship between direct health factors and the set-off independent variables without using indirect health factors. The specific fitting formula is as follows: and

[0128] Data fitting revealed that this model was difficult to fit well, resulting in low prediction accuracy.

[0129] Example 2

[0130] This embodiment provides an electronic device, such as... Figure 15The diagram shown is a structural schematic of an electronic device 10 used to implement an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0131] like Figure 15 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from storage unit 18. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14; an I / O interface 15 is also connected to the bus 14.

[0132] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0133] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as methods for predicting battery calendar life.

[0134] In some embodiments, the battery calendar life prediction method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the battery calendar life prediction method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the battery calendar life prediction method by any other suitable means (e.g., by means of firmware).

[0135] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0136] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0137] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0138] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0139] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0140] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0141] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0142] The present invention has been illustrated with the above embodiments to illustrate its detailed features, but the present invention is not limited to the above detailed features, that is, it does not mean that the present invention must rely on the above detailed features to be implemented. Those skilled in the art should understand that any improvements to the present invention, equivalent substitutions for the selected technical features, additions of auxiliary technical features, and selection of specific methods, etc., all fall within the protection scope and disclosure scope of the present invention.

Claims

1. A method of predicting calendar life of a battery, characterized by, The prediction method includes the following steps: A predictive model is constructed between the combined health factors and the shelved independent variables; the predictive model is numerically fitted using experimental data of the shelved independent variables and the combined health factors during the shelving test, and the model parameters in the predictive model are solved to obtain the predictive model of battery calendar life. The combined health factors include indirect health factors and direct health factors.

2. The prediction method of claim 1, wherein, The direct health factor includes a rate of recovery of lost capacity Q loss ; And / or, the indirect health factor comprises a polarization loss Q P , an irreversible loss Q irr , a DCR instantaneous impedance R ohm+ct , and a DCR diffusion impedance R d ; And / or, the shelfing independent variable comprises any one or a combination of at least two of a shelfing period t i , a shelfing temperature T, or a shelfing battery state of charge SOC.

3. The prediction method of claim 2, wherein, The predictive model between the combined health factors and the shelved independent variables is shown in Equation (1): In equation (1), A represents the model parameter to be solved.

4. The prediction method of claim 3, wherein, The prediction method further includes: constructing a coupled model of indirect health factors to solve for the recovery capacity loss rate Q. loss ; The coupling model of the indirect health factors is shown in equation (2): Q P *Q irr *R ohm+ct *R d = B * t i 2.16 Equation (2); Where B represents the model parameters to be solved.

5. The prediction method according to claim 3 or 4, characterized in that, In equation (1), A is a function of the storage temperature and the state of charge of the stored battery; Furthermore, A is exponentially related to the storage temperature and the state of charge of the stored battery.

6. The prediction method of claim 4, wherein, In equation (2), B is a function of the storage temperature and the state of charge of the stored battery; Furthermore, B is exponentially related to the storage temperature and the state of charge of the stored battery.

7. The prediction method according to any one of claims 1 to 6, characterized in that, The acquisition of experimental data for the deferred independent variables and combined health factors during the deferred testing period includes: S211. Perform DCR testing on the initial state of the battery to be evaluated and calculate the indirect health factor; S212. Conduct a storage test on the battery to be tested. During the storage test period, conduct a DCR test on the battery to be tested and record the storage independent variable and the indirect health factor corresponding to the storage independent variable. The settling test period is 100 to 250 days.

8. An apparatus for predicting the calendar life of a battery using the method of any one of claims 1 to 7, characterized in that The device includes: The model building module is used to build predictive models between combined health factors and set-off independent variables. The data acquisition module is used to collect indirect health factors, set aside independent variables, and indirect health factors corresponding to the set aside independent variables in the initial state. The calculation module is used to numerically fit the prediction model using experimental data of the independent variables and combined health factors that were shelved during the shelving test, and to solve for the model parameters in the prediction model. The prediction module is used to predict the battery calendar life of the battery under test based on the prediction model.

9. An electronic device, comprising: The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the battery calendar life prediction method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the battery calendar life prediction method according to any one of claims 1 to 7.

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