Method, device and equipment for predicting cycle life of lithium iron phosphate battery for energy storage

Abstract

The invention provides a method, device and equipment for predicting the cycle life of a lithium iron phosphate battery for energy storage, and belongs to the field of battery prediction.The method comprises the steps that on the basis of cycle test data of the lithium iron phosphate battery, the change condition of delta V and the change condition of delta LLI in the electrochemical cycle process are obtained, delta V is the voltage difference of a charging and discharging platform, and delta LLI is the voltage difference of the charging and discharging platform; delta LLI is the loss capacity of the active lithium; constructing an ideal cycle life prediction model based on the change condition of delta V and the change condition of delta LLI in the electrochemical cycle process; and determining the ideal cycle life of the to-be-tested battery based on the delta V limit value of the to-be-tested battery and the ideal cycle life prediction model. Through the method provided by the invention, the cycle life of the lithium iron phosphate battery for energy storage can be quickly predicted, and the prediction result is relatively accurate and reliable.

Description

Technical Field

[0001] The present invention relates to the field of battery prediction technology, and in particular to a method for predicting the cycle life of a lithium iron phosphate battery for energy storage, a device for predicting the cycle life of a lithium iron phosphate battery for energy storage, an electronic device and a computer-readable storage medium. Background Art

[0002] As a secondary battery that can be repeatedly charged and discharged, lithium-ion batteries are widely used in various fields of society, such as 3C field, electric vehicles, power storage, etc. Compared with lithium-ion batteries, lithium iron phosphate batteries have good thermal stability and chemical stability, and are safer. This type of battery can maintain normal working conditions even in a high temperature environment. Moreover, the components of lithium iron phosphate batteries are basically non-toxic and easier to handle, which reduces the cost of lithium iron phosphate batteries.

[0003] The cycle life of lithium iron phosphate power batteries can reach more than 2,000 times. How to predict the life of lithium iron phosphate power batteries has become a research hotspot for developers. Predicting battery life can save battery resources on the one hand, and shorten the development cycle and save time on the other. Most of the existing public technical solutions use some accelerated aging methods and mathematical models to predict the cycle aging life of lithium iron phosphate batteries, but the overall modeling process is relatively cumbersome. Summary of the invention

[0004] In view of the technical problem that the prior art lacks a simple, reliable and accurate life prediction model for lithium iron phosphate batteries, the present invention provides a method for predicting the cycle life of lithium iron phosphate batteries for energy storage. This method can quickly predict the cycle life of lithium iron phosphate batteries for energy storage, and the prediction result is relatively accurate and reliable.

[0005] To achieve the above-mentioned purpose, the first aspect of the present invention provides a method for predicting the cycle life of a lithium iron phosphate battery for energy storage, the prediction method comprising the following steps: based on the cycle test data of the lithium iron phosphate battery, obtaining the ΔV change and ΔLLI change of the electrochemical cycle process, wherein ΔV is the charge and discharge platform voltage difference, and ΔLLI is the active lithium loss capacity; based on the ΔV change and ΔLLI change of the electrochemical cycle process, constructing an ideal cycle life prediction model; based on the ΔV limit value of the battery to be tested and the ideal cycle life prediction model, determining the ideal cycle life of the battery to be tested.

[0006] In an exemplary embodiment of the present invention, the cycle test data based on the lithium iron phosphate battery, obtaining the ΔV change and ΔLLI change of the electrochemical cycle process, may include: based on the cycle test data of the lithium iron phosphate battery, respectively drawing the voltage change curve and the discharge state differential voltage change curve under different cycle periods; determining the difference between the charging platform and the discharging platform on the vertical axis in the voltage change curve under different cycle periods, and obtaining the ΔV change of the electrochemical cycle process; determining the difference between the valley bottom and the peak value of the second stage in the discharge state differential voltage change curve under different cycle periods on the horizontal axis, and obtaining the ΔLLI change of the electrochemical cycle process.

[0007] In an exemplary embodiment of the present invention, the construction of an ideal cycle life prediction model based on the ΔV change and ΔLLI change in the electrochemical cycle process may include: determining a first ideal curve relationship of ΔV-ΔLLI based on the ΔV change and ΔLLI change in different cycle periods; determining a second ideal curve relationship of N-ΔV-ΔLLI based on the ΔV change and ΔLLI change in different cycle periods, wherein N is the number of cycle periods.

[0008] In an exemplary embodiment of the present invention, the first ideal curve relationship of ΔV-ΔLLI may be: ΔV=a·ΔLLI 2 +b·ΔLLI+c; wherein ΔV is the charge and discharge platform voltage difference, ΔLLI is the active lithium loss capacity, a is the first fitting coefficient, b is the second fitting coefficient, and c is the third fitting coefficient.

[0009] In an exemplary embodiment of the present invention, the second ideal curve relationship of N-ΔV-ΔLLI may be: ΔV=d+e·N+f·ΔLLI+g·N 2 +h·N·ΔLLI+i·ΔLLI 2 ; Wherein, ΔV is the charge and discharge platform voltage difference, ΔLLI is the active lithium loss capacity, N is the number of cycles, d is the fourth fitting coefficient, e is the fifth fitting coefficient, f is the sixth fitting coefficient, g is the seventh fitting coefficient, h is the eighth fitting coefficient, and i is the ninth fitting coefficient.

[0010] In an exemplary embodiment of the present invention, determining the ideal cycle life of the battery to be tested based on the ΔV limit value of the battery to be tested and the ideal cycle life prediction model may include: determining the ΔLLI prediction value of the battery to be tested based on the ΔV limit value of the battery to be tested and the first ideal curve relationship; determining the N prediction value of the battery to be tested based on the ΔV limit value of the battery to be tested, the ΔLLI prediction value and the second ideal curve relationship; and determining the N prediction value of the battery to be tested as the ideal cycle life of the battery to be tested.

[0011] In an exemplary embodiment of the present invention, the battery to be tested may be a lithium iron phosphate battery or a graphite full battery with an aging temperature range of 20 to 60°C.

[0012] The second aspect of the present invention provides a cycle life prediction device for a lithium iron phosphate battery for energy storage, the prediction device comprising: an acquisition module, used to acquire the ΔV change and ΔLLI change of the electrochemical cycle process based on the cycle test data of the lithium iron phosphate battery, wherein ΔV is the charge and discharge platform voltage difference, and ΔLLI is the active lithium loss capacity; a model construction module, used to construct an ideal cycle life prediction model based on the ΔV change and ΔLLI change of the electrochemical cycle process; a life prediction module, used to determine the ideal cycle life of the battery to be tested based on the ΔV limit value of the battery to be tested and the ideal cycle life prediction model.

[0013] A third aspect of the present invention provides an electronic device, comprising a processor and a memory, wherein the memory stores at least one computer program, and the at least one computer program is loaded and executed by one or more processors so that the processor executes the method for predicting the cycle life of a lithium iron phosphate battery for energy storage as described above.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium, which stores at least one program code, and the program code is loaded and executed by a processor to enable a computer to execute the above-mentioned method for predicting the cycle life of a lithium iron phosphate battery for energy storage.

[0015] Through the technical solution provided by the present invention, the present invention has at least the following technical effects:

[0016] (1) The cycle life prediction of lithium iron phosphate batteries for energy storage provided by the present invention is the first to propose, based on the normal failure mechanism of lithium iron phosphate batteries, to establish an ideal cycle life prediction model using the cycle period N, ΔLLI change degree and ΔV change degree of lithium iron phosphate batteries. This model can obtain more reliable battery cycle aging life prediction results in a shorter time with fewer resources;

[0017] (2) The present invention is simple and reliable. It does not require disassembly of the battery. It only needs to test the electrochemical data of the battery. By inputting the electrochemical data obtained from the test into the prediction model, the cycle life of the battery can be obtained. This not only shortens the product verification cycle, but also the prediction results are proven to be reliable and the model is stable.

[0018] Other features and advantages of the present invention will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0020] Figure 1 A flow chart of a method for predicting the cycle life of a lithium iron phosphate battery for energy storage provided by an embodiment of the present invention;

[0021] Figure 2 Cycle capacity diagrams corresponding to different aging cycles provided by the embodiment of the present invention;

[0022] Figure 3 A charge and discharge platform voltage change curve diagram corresponding to different aging cycles provided by an embodiment of the present invention;

[0023] Figure 4 A discharge state differential voltage change curve diagram corresponding to different aging cycles provided by an embodiment of the present invention;

[0024] Figure 5 A first ideal fitting curve diagram of ΔV-ΔLLI provided by an embodiment of the present invention;

[0025] Figure 6 A second ideal fitting curve diagram of N-ΔV-ΔLLI provided in an embodiment of the present invention;

[0026] Figure 7 A schematic diagram of the structure of a device for predicting the cycle life of a lithium iron phosphate battery for energy storage provided by an embodiment of the present invention;

[0027] Figure 8 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention.

[0028] Description of Reference Numerals

[0029] 101 - acquisition module, 102 - model building module, 103 - life prediction module, 201 - memory, 202 - processor. DETAILED DESCRIPTION

[0030] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0031] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0032] In the present invention, “first”, “second”, etc. are merely used for convenience of description and distinction and cannot be understood as indicating or implying relative importance.

[0033] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "installation", "connection", etc. should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integrated connection; it can be a direct connection, or an indirect connection; it can be a wired connection, or a wireless connection. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances.

[0034] In the existing technology, most of the cycle aging life of lithium iron phosphate batteries is predicted by some accelerated aging methods and the establishment of mathematical models, but the overall modeling process is relatively cumbersome and cannot save battery resources and shorten the development time of researchers. Therefore, using fewer resources in a shorter time to obtain a more reliable battery cycle life aging model has become a research hotspot for lithium iron phosphate power batteries in recent years.

[0035] In view of the technical problem of the lack of a simple, reliable and accurate life prediction model for lithium iron phosphate batteries in the prior art, the present invention provides a method for predicting the cycle life of lithium iron phosphate batteries for energy storage. The method can extract the active lithium loss capacity ΔLLI and the charge and discharge platform voltage difference ΔV of the lithium iron phosphate battery through different battery cycle aging methods based on the aging failure mode of the lithium iron phosphate battery, and establish an ideal cycle life prediction model based on N-ΔV-ΔLLI through data fitting. As long as the electrochemical data of the test battery is input into the prediction model, the cycle life of the battery can be obtained. Compared with the battery cycle life prediction method in the prior art, the present invention can simply, accurately and reliably obtain the prediction results of the battery cycle life. In specific implementation, the above method can be executed by an electronic device, which can be a server, terminal or other device with processing function.

[0036] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0037] like Figure 1 As shown, an embodiment of the present invention provides a method for predicting the cycle life of a lithium iron phosphate battery for energy storage, the prediction method comprising the following steps:

[0038] Step S101, based on the cycle test data of the lithium iron phosphate battery, obtaining the ΔV change and the ΔLLI change in the electrochemical cycle process.

[0039] Among them, ΔV is the charge and discharge platform voltage difference, and ΔLLI is the active lithium loss capacity.

[0040] Step S102, constructing an ideal cycle life prediction model based on the changes in ΔV and ΔLLI during the electrochemical cycle process.

[0041] Step S103, determining the ideal cycle life of the battery to be tested based on the ΔV limit value of the battery to be tested and the ideal cycle life prediction model.

[0042] Further, in a possible implementation, in step S101, the process of obtaining the ΔV change and ΔLLI change during the electrochemical cycle process based on the cycle test data of the lithium iron phosphate battery may include but is not limited to the following sub-steps S1011 to S1013.

[0043] Sub-step S1011, based on the cycle test data of the lithium iron phosphate battery, respectively draw the voltage change curves under different cycle periods and the discharge state differential voltage change curves.

[0044] Sub-step S1012, determining the difference between the charging platform and the discharging platform on the vertical axis in the voltage change curve under different cycle periods, and obtaining the ΔV change of the electrochemical cycle process.

[0045] Sub-step S1013, determining the difference between the valley bottom and the peak value in the second stage of the discharge state differential voltage change curve under different cycle periods on the horizontal axis, and obtaining the ΔLLI change of the electrochemical cycle process.

[0046] Further, in a possible implementation, in step S102, the ideal cycle life prediction model is constructed based on the change of ΔV and ΔLLI during the electrochemical cycle process, which may include but is not limited to the following sub-steps S1021 to S1022.

[0047] Sub-step S1021, determining a first ideal curve relationship of ΔV-ΔLLI based on the change of ΔV and ΔLLI in different cycles.

[0048] Sub-step S1022, determining a second ideal curve relationship of N-ΔV-ΔLLI based on the change of ΔV and ΔLLI in different cycles.

[0049] Where N is the number of cycles.

[0050] Furthermore, in a possible implementation, a first ideal curve relationship of ΔV-ΔLLI and a second ideal curve relationship of N-ΔV-ΔLLI may be determined by data fitting according to changes in ΔV and ΔLLI in different cycles.

[0051] For example, the first ideal curve relationship of ΔV-ΔLLI determined by fitting can be shown as the following equation (1).

[0052] ΔV=a·ΔLLI 2 +b·ΔLLI+c (1)

[0053] In formula (1), ΔV is the charge and discharge platform voltage difference, ΔLLI is the active lithium loss capacity, a is the first fitting coefficient, b is the second fitting coefficient, and c is the third fitting coefficient.

[0054] The second ideal curve relationship of N-ΔV-ΔLLI determined by fitting can be shown as the following equation (2).

[0055] ΔV=d+e·N+f·ΔLLI+g·N 2 +h·N·ΔLLI+i·ΔLLI 2 (2)

[0056] In formula (2), ΔV is the charge and discharge platform voltage difference, ΔLLI is the active lithium loss capacity, N is the number of cycles, d is the fourth fitting coefficient, e is the fifth fitting coefficient, f is the sixth fitting coefficient, g is the seventh fitting coefficient, h is the eighth fitting coefficient, and i is the ninth fitting coefficient.

[0057] Of course, the present invention is not limited to this, and a suitable machine learning model such as linear regression, decision tree regression, random forest regression, etc. can also be selected to construct a ternary model of N-ΔV-ΔLLI to determine the ideal cycle life prediction model.

[0058] Further, in a possible implementation, in step S103, based on the ΔV limit value of the battery to be tested and the ideal cycle life prediction model, the process of determining the ideal cycle life of the battery to be tested may include but is not limited to the following sub-steps S1031 to S1033.

[0059] Sub-step S1031 , determining a ΔLLI prediction value of the battery to be tested based on the ΔV limit value of the battery to be tested and a first ideal curve relationship of ΔV-ΔLLI.

[0060] Sub-step S1032, determining the predicted value N of the battery to be tested based on the ΔV limit value of the battery to be tested, the predicted value ΔLLI and a second ideal curve relationship of N-ΔV-ΔLLI.

[0061] Sub-step S1033, determining the predicted value N of the battery to be tested as the ideal cycle life of the battery to be tested.

[0062] In this embodiment, it should be noted that the battery to be tested should be a lithium iron phosphate battery or a graphite full battery with an aging temperature range of 20 to 60°C. In other words, the first ideal curve relationship of ΔV-ΔLLI and the second ideal curve relationship of N-ΔV-ΔLLI are only applicable to lithium iron phosphate / graphite full batteries with a predicted aging temperature range of 20 to 60°C.

[0063] In addition, the first ideal curve relationship of ΔV-ΔLLI and the second ideal curve relationship of N-ΔV-ΔLLI are only applicable to predicting the ideal cycle life within a theoretical capacity loss of 20%.

[0064] In order to verify the effectiveness and practicality of the method for predicting the cycle life of a lithium iron phosphate battery for energy storage of the present invention, taking a lithium iron phosphate battery as an example, the method for predicting the cycle life of a lithium iron phosphate battery for energy storage of the present invention is implemented for the lithium iron phosphate battery to obtain a prediction result of the battery cycle life. The specific process is shown below.

[0065] Step 1: Perform a cycle test on the lithium iron phosphate battery.

[0066] like Figure 2 As shown, the cycle aging test of lithium iron phosphate battery can be carried out at 25°C at a 1C rate.

[0067] Step 2: Draw the charge and discharge platform voltage change curves corresponding to different aging cycles, and calculate the ΔV change under different aging cycles.

[0068] For example, Figure 3 As shown, Figure 3 The horizontal coordinate of the charge and discharge platform voltage change curve is the specific capacity. Figure 3 The vertical coordinate of the charge and discharge platform voltage change curve is voltage. In this figure, multiple curves are drawn, which respectively represent the change of the voltage of the charge and discharge platforms with the specific capacity after different aging cycles. It can be seen that the voltage of the charge platform increases with the increase of the specific capacity after each aging cycle, while the voltage of the discharge platform decreases with the increase of the specific capacity after each aging cycle. The voltage difference ΔV of the charge and discharge platforms after each aging cycle can be obtained by subtracting the voltage of the charge platform from the voltage of the discharge platform after each aging cycle.

[0069] Step 3: Draw the discharge state differential voltage change curves corresponding to different aging cycles, and calculate the ΔLLI changes under different aging cycles.

[0070] For example, Figure 4 As shown, Figure 4 The discharge state differential voltage change curve (abbreviated as dV / dQ curve) has the capacity as the abscissa. Figure 4The vertical coordinate of the discharge state differential voltage change curve is dV / dQ. The figure shows the change of dV / dQ with capacity after different aging cycles. It can be seen that the dV / dQ curve after each aging cycle can be roughly divided into two stages. The first stage is the loss of active materials (ΔLAM) and the second stage is the loss of active lithium (ΔLLI). From the dV / dQ curve, the second stage appears in the area from the first trough to the first peak on the right side of the curve. At this time, the horizontal coordinates corresponding to the first peak and the first trough of the second stage in the dV / dQ curve after each aging cycle are subtracted to obtain the active lithium loss ΔLLI after each aging cycle.

[0071] Step 4: fitting a first ideal curve relationship P(ΔV, ΔLLI) of ΔV-ΔLLI according to the changes of ΔV and ΔLLI under different aging cycles.

[0072] For example, Figure 5 As shown in the figure, the charge-discharge platform voltage difference ΔV corresponding to different cycle numbers and the active lithium loss capacity ΔLLI value are recorded as N groups of data points, that is, (ΔV k , ΔLLI k ), 1≤k≤N, and a fitting ideal curve corresponding to ΔV-ΔLLI is drawn based on N groups of data points, thereby fitting a first ideal curve relationship P(ΔV, ΔLLI) corresponding to ΔV-ΔLLI.

[0073] That is, ΔV = 21366.69 ΔLLI 2 -1546.92ΔLLI+28.19, where ΔLLI is the active lithium loss capacity in the dV / dQ curve, and ΔV is the voltage difference between the charge and discharge platform curves.

[0074] Step 5: According to the changes of ΔV and ΔLLI under different aging cycles, a second ideal curve relationship Q(N, ΔLLI, ΔV) of N-ΔV-ΔLLI is fitted.

[0075] For example, Figure 6 As shown, the charge-discharge platform voltage difference ΔV and the active lithium loss capacity ΔLLI corresponding to different cycle numbers are recorded as N groups of data points, namely (N, ΔLLI k , ΔV k ), 1≤k≤N, and a fitting ideal curve corresponding to N-ΔV-ΔLLI is drawn based on N groups of data points, and a second ideal curve relationship Q(N, ΔLLI, ΔV) of the fitted N-ΔV-ΔLLI is obtained.

[0076] That is, ΔV = 46.3432-0.0024N-2526.3818ΔLLI+0.0000N 2+0.0646N·ΔLLI+34580.2524ΔLLI 2 =46.3432-0.0024N-2526.3818ΔLLI+0.0646N·ΔLLI+34580.2524ΔLLI 2 , where N is the number of cycles, ΔLLI is the active lithium loss capacity in the dV / dQ curve, and ΔV is the voltage difference between the charge and discharge platform curves.

[0077] Step 6, determining the charge and discharge platform voltage difference ΔV of the lithium iron phosphate battery to be tested, and inputting the first ideal curve relationship P(ΔV, ΔLLI) for calculation to obtain the predicted ΔLLI of the lithium iron phosphate battery.

[0078] For example, the change of ΔV during the electrochemical cycle of the battery SOC in the range of 40-60% can be observed and recorded. When ΔV>0.3V, it is considered to exceed the polarization boundary, that is, the cycle aging life of the battery has been reached. At this time, ΔV=0.3 can be determined as the ΔV limit value of the lithium iron phosphate battery to be tested, and ΔV=0.3 can be input into the first ideal curve relationship P(ΔV, ΔLLI) for calculation, and the predicted ΔLLI of the lithium iron phosphate battery is obtained as 0.035.

[0079] Step 7, bringing the charge and discharge platform voltage difference ΔV and the predicted ΔLLI of the lithium iron phosphate battery to be tested into the second ideal curve relationship Q(N, ΔLLI, ΔV) for calculation to obtain the predicted ideal cycle life of the lithium iron phosphate battery.

[0080] For example, ΔV=0.3 and ΔLLI=0.035 are recorded as the end point of life, which are input into the second ideal curve relationship Q(N, ΔLLI, ΔV) to predict the ideal cycle life of the lithium iron phosphate battery to be tested, and N=2400.65195768 weeks is obtained.

[0081] In summary, according to the prediction results, the rational cycle life of this lithium iron phosphate battery can reach 2400 weeks.

[0082] In addition, the implementation environment of this embodiment includes at least one terminal and a server, and the method is executed on the terminal or the server respectively. The terminal and the server can be connected in communication to realize the interactive transmission of information.

[0083] Among them, the terminal can be any electronic product that can interact with the user through one or more methods such as keyboard, touchpad, touch screen, voice interaction, etc., such as PC (Personal Computer), PPC (Pocket Personal Computer), tablet computer, etc.

[0084] The server can be a single server or a server cluster composed of multiple servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), as well as big data and artificial intelligence platforms.

[0085] like Figure 7 As shown, an embodiment of the present invention further provides a cycle life prediction device for a lithium iron phosphate battery for energy storage, the prediction device comprising: an acquisition module 101 , a model building module 102 and a life prediction module 103 .

[0086] The acquisition module 101 is used to acquire the ΔV change and ΔLLI change during the electrochemical cycle process based on the cycle test data of the lithium iron phosphate battery, wherein ΔV is the voltage difference between the charge and discharge platforms, and ΔLLI is the active lithium loss capacity.

[0087] The model building module 102 is connected to the acquisition module 101 and is used to build an ideal cycle life prediction model based on the ΔV change and the ΔLLI change during the electrochemical cycle process.

[0088] The life prediction module 103 is connected to the model building module 102 and is used to determine the ideal cycle life of the battery to be tested based on the ΔV limit value of the battery to be tested and the ideal cycle life prediction model.

[0089] It should be noted that the above-mentioned device only uses the division of the above-mentioned functional modules as an example to illustrate when implementing its functions. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device provided in the above embodiment and the method provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0090] like Figure 8 As shown, an embodiment of the present invention further provides an electronic device, which includes a processor 201 and a memory 202, wherein at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by one or more of the above-mentioned processors to enable the processor to implement the cycle life prediction method of the lithium iron phosphate battery for energy storage in the above-mentioned embodiment.

[0091] Of course, the electronic device may also have components such as a wired or wireless network interface, a keyboard, and an input / output interface for input and output. The electronic device may also include other components for realizing various functions of the device, which will not be described in detail here.

[0092] An embodiment of the present invention further provides a computer-readable storage medium, in which at least one program code is stored. The program code is loaded and executed by a processor so that a computer implements the cycle life prediction method of a lithium iron phosphate battery for energy storage in the above embodiment.

[0093] Optionally, the computer readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, and an optical disc data storage device. Those skilled in the art may understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, and the program is stored in a storage medium, including several instructions to enable a single-chip microcomputer, a chip or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disc.

[0094] The preferred embodiments of the present invention are described in detail above in conjunction with the accompanying drawings. However, the present invention is not limited to the specific details in the above embodiments. Within the technical concept of the present invention, a variety of simple modifications can be made to the technical solution of the present invention, and these simple modifications all belong to the protection scope of the present invention.

[0095] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not further describe various possible combinations.

[0096] In addition, various embodiments of the present invention may be arbitrarily combined, and as long as they do not violate the concept of the present invention, they should also be regarded as the contents disclosed by the present invention.

Claims

1. A method for predicting the cycle life of a lithium iron phosphate battery for energy storage, characterized in that: The prediction method comprises: Based on the cycle test data of lithium iron phosphate batteries, the changes in ΔV and ΔLLI during the electrochemical cycle process are obtained, where ΔV is the voltage difference between the charge and discharge platforms, and ΔLLI is the active lithium loss capacity; Based on the changes in ΔV and ΔLLI during the electrochemical cycle process, an ideal cycle life prediction model is constructed; Based on the ΔV limit value of the battery to be tested and the ideal cycle life prediction model, the ideal cycle life of the battery to be tested is determined.

2. The method for predicting the cycle life of a lithium iron phosphate battery for energy storage according to claim 1, characterized in that: The cycle test data based on the lithium iron phosphate battery is used to obtain the ΔV change and ΔLLI change during the electrochemical cycle process, including: Based on the cycle test data of lithium iron phosphate batteries, the voltage change curves under different cycle periods and the discharge state differential voltage change curves are drawn respectively; Determine the difference between the charging platform and the discharging platform on the ordinate in the voltage change curve under different cycle periods to obtain the ΔV change of the electrochemical cycle process; The difference between the valley bottom and the peak value in the second stage of the discharge state differential voltage change curve under different cycle periods is determined on the horizontal axis to obtain the ΔLLI change of the electrochemical cycle process.

3. The method for predicting the cycle life of a lithium iron phosphate battery for energy storage according to claim 1, characterized in that: The ideal cycle life prediction model is constructed based on the ΔV change and ΔLLI change during the electrochemical cycle process, including: Based on the changes of ΔV and ΔLLI under different cycles, a first ideal curve relationship of ΔV-ΔLLI is determined; Based on the changes in ΔV and ΔLLI under different cycle periods, a second ideal curve relationship of N-ΔV-ΔLLI is determined, where N is the number of cycle periods.

4. The method for predicting the cycle life of a lithium iron phosphate battery for energy storage according to claim 3, characterized in that: The first ideal curve relationship of ΔV-ΔLLI is: ΔV=a·ΔLLI 2 +b·ΔLLI+c; Among them, ΔV is the charge and discharge platform voltage difference, ΔLLI is the active lithium loss capacity, a is the first fitting coefficient, b is the second fitting coefficient, and c is the third fitting coefficient.

5. The method for predicting the cycle life of a lithium iron phosphate battery for energy storage according to claim 3, characterized in that: The second ideal curve relationship of N-ΔV-ΔLLI is: ΔV=d+e·N+f·ΔLLI+g·N 2 +h·N·ΔLLI+i·ΔLLI 2 ; Among them, ΔV is the charge and discharge platform voltage difference, ΔLLI is the active lithium loss capacity, N is the number of cycles, d is the fourth fitting coefficient, e is the fifth fitting coefficient, f is the sixth fitting coefficient, g is the seventh fitting coefficient, h is the eighth fitting coefficient, and i is the ninth fitting coefficient.

6. The method for predicting the cycle life of a lithium iron phosphate battery for energy storage according to claim 3, characterized in that: The method of determining the ideal cycle life of the battery to be tested based on the ΔV limit value of the battery to be tested and the ideal cycle life prediction model includes: Determine a ΔLLI prediction value of the battery to be tested based on the ΔV limit value of the battery to be tested and the first ideal curve relationship; Determine a predicted value of N of the battery to be tested based on a ΔV limit value of the battery to be tested, a predicted value of ΔLLI and the second ideal curve relationship; The predicted value of N of the battery to be tested is determined as the ideal cycle life of the battery to be tested.

7. The method for predicting the cycle life of a lithium iron phosphate battery for energy storage according to claim 1, characterized in that: The battery to be tested is a lithium iron phosphate battery or a graphite full battery with an aging temperature range of 20 to 60°C.

8. A cycle life prediction device for lithium iron phosphate batteries for energy storage, characterized in that: The prediction device comprises: An acquisition module is used to obtain the ΔV change and ΔLLI change during the electrochemical cycle process based on the cycle test data of the lithium iron phosphate battery, wherein ΔV is the voltage difference between the charge and discharge platforms, and ΔLLI is the active lithium loss capacity; Model building module, used to build an ideal cycle life prediction model based on the changes in ΔV and ΔLLI during the electrochemical cycle process; The life prediction module is used to determine the ideal cycle life of the battery to be tested based on the ΔV limit value of the battery to be tested and the ideal cycle life prediction model.

9. An electronic device, characterized in that: The electronic device includes a processor and a memory, wherein at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by one or more processors so that the processor executes the cycle life prediction method of a lithium iron phosphate battery for energy storage according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one program code, and the program code is loaded and executed by a processor so that a computer executes the cycle life prediction method of a lithium iron phosphate battery for energy storage according to any one of claims 1 to 7.

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