Method and system for predicting aging trend of battery capacity

Abstract

The embodiment of the invention provides a method and system for predicting the aging trend of battery capacity, and belongs to the field of battery detection. The method comprises the following steps: constructing a positive open-circuit voltage model and a negative open-circuit voltage model based on a Nernst equation, a plurality of correction factors, a plurality of types of lattice site numbers of a positive electrode material of a sample total battery and a plurality of types of lattice site numbers of a negative electrode material of the sample total battery; according to the positive electrode open-circuit voltage model and the negative electrode open-circuit voltage model, an open-circuit voltage model of a to-be-detected total battery is constructed, and the open-circuit voltage model of the to-be-detected total battery is a corresponding relation between the ion fraction of a positive electrode material of the to-be-detected total battery and the ion fraction of a negative electrode material of the to-be-detected total battery; and predicting the capacity aging trend of the to-be-measured total battery according to the open-circuit voltage model of the to-be-measured total battery and the plurality of open-circuit voltages of the to-be-measured total battery. According to the invention, the aging trend of the battery capacity can be predicted without damaging the battery.

Description

Technical Field

[0001] The present invention relates to the technical field of battery detection, and in particular to a method and system for predicting the aging trend of battery capacity. Background Art

[0002] With the rapid development of new energy vehicles, lithium-ion batteries have become the mainstream power battery type for new energy vehicles due to their many advantages such as high energy density, high voltage platform, low self-discharge rate and long cycle life. As one of the core key components of new energy vehicles, the performance of the on-board power battery system has a direct impact on the performance of the entire vehicle. At present, power battery technology is also an important technical bottleneck restricting the rapid development of new energy vehicles.

[0003] As the battery is used in cycles of charge and discharge, or even as it is stored, it will gradually age, its capacity will gradually decay, and its internal resistance will gradually increase. The battery decay mechanism and decay model are the basis for studying the battery SOH (state of health) estimation algorithm. Understanding the battery decay mechanism is conducive to developing a reasonable battery SOH estimation algorithm, judging the actual battery decay, and improving the battery usage strategy.

[0004] In recent years, with the widespread application of secondary batteries (i.e., storage batteries) in various fields, how to quickly evaluate the battery life, thereby judging the battery reliability and effectively controlling the battery use process has become a research hotspot.

[0005] To study the mechanism of battery capacity attenuation, it is often necessary to disassemble the battery and use methods such as XRD (X-Ray Diffraction) and SEM (Scanning Electron Microscope) to analyze the changes in the positive and negative electrodes of the battery, so as to determine the battery attenuation mechanism. However, for actual commercial batteries, such a method of damaging the battery is completely infeasible. Therefore, how to accurately and quantitatively predict the attenuation process inside the battery without damaging the battery and realize the evaluation of the battery life is still an urgent problem to be solved. Summary of the invention

[0006] The purpose of the embodiments of the present invention is to provide a method and system for predicting the aging trend of battery capacity, which can partially or completely solve the problem that the prior art cannot predict the aging trend of battery capacity while ensuring the integrity of the battery.

[0007] In order to achieve the above-mentioned purpose, an embodiment of the present invention provides a method and system for predicting the aging trend of battery capacity, the method comprising: constructing a positive electrode open circuit voltage model and a negative electrode open circuit voltage model based on the Nernst equation, multiple correction factors, multiple types of lattice site numbers of the positive electrode material of the sample full battery, and multiple types of lattice site numbers of the negative electrode material of the sample full battery; constructing an open circuit voltage model of the full battery to be tested according to the positive electrode open circuit voltage model and the negative electrode open circuit voltage model, wherein the open circuit voltage model of the full battery to be tested is the correspondence between the ion fraction of the positive electrode material of the full battery to be tested and the ion fraction of the negative electrode material of the full battery to be tested; and predicting the capacity aging trend of the full battery to be tested according to the open circuit voltage model of the full battery to be tested and the multiple open circuit voltages of the full battery to be tested.

[0008] Optionally, the plurality of correction factors include a first correction factor and a second correction factor, and based on the Nernst equation, the plurality of correction factors, the number of multiple types of lattice sites of the positive electrode material of the sample full battery, and the number of multiple types of lattice sites of the negative electrode material of the sample full battery, constructing the positive electrode open circuit voltage model and the negative electrode open circuit voltage model includes: according to the Nernst equation, the first correction factor γ i , the number of various types of lattice sites N of the positive electrode materials of the sample full battery and the following formula are used to construct the positive electrode open circuit voltage model:

[0009] And according to the Nernst equation, the second correction factor γ j , the number of various types of lattice sites M of the negative electrode material of the sample full battery and the following formula are used to construct the negative electrode open circuit voltage model:

[0010]

[0011] Where Δx i represents the site occupancy fraction of the positive electrode, Δx j Indicates the fraction of negative electrode position occupied, E 0，i represents the positive electrode redox potential, E 0，j represents the negative electrode redox potential, It represents the ratio of the number of inserted lattice sites to the number of available lattice sites in the cathode material. represents the ratio of the number of inserted lattice sites to the number of available lattice sites in the negative electrode material, represents the open circuit voltage of the positive electrode, represents the open circuit voltage of the negative electrode, e represents the elementary unit of charge, k represents the Boltzmann constant, and T represents the Kelvin temperature.

[0012] Optionally, constructing the open circuit voltage model of the full battery to be tested according to the positive electrode open circuit voltage model and the negative electrode open circuit voltage model comprises: Negative open circuit voltage model And the following formula, construct the open circuit voltage model of the full battery to be tested Among them, x PE represents the ion fraction of the positive electrode material of the full battery to be tested, x NE Represents the ion fraction of the negative electrode material of the full battery to be tested.

[0013] Optionally, the positive electrode material ion fraction x of the full battery to be tested PE And the negative electrode material ion fraction x of the full battery to be tested NE Determined by the following formula:

[0014] Among them, y PE0 is the initial ion fraction of the positive electrode of the full battery to be tested, y NE0 is the initial ion fraction of the negative electrode of the full battery to be tested, i is the discharge current, t is the discharge time, C PE is the content of positive electrode active material in the full battery to be tested, C NE is the content of negative electrode active material in the full battery to be tested.

[0015] Optionally, predicting the capacity aging trend of the full battery to be tested based on the open circuit voltage model of the full battery to be tested and the multiple open circuit voltages of the full battery to be tested includes: determining the contents of multiple positive electrode active substances of the full battery to be tested and the contents of multiple negative electrode active substances of the full battery to be tested based on the open circuit voltage model of the full battery to be tested and the multiple open circuit voltages of the full battery to be tested; and predicting the capacity aging trend of the full battery to be tested based on the contents of multiple positive electrode active substances of the full battery to be tested and the contents of multiple negative electrode active substances of the full battery to be tested.

[0016] On the other hand, the present invention also provides a system for predicting the aging trend of battery capacity, the system comprising: a first construction module, for constructing a positive electrode open circuit voltage model and a negative electrode open circuit voltage model based on the Nernst equation, multiple correction factors, the number of multiple types of lattice sites of the positive electrode material of the sample full battery, and the number of multiple types of lattice sites of the negative electrode material of the sample full battery; a second construction module, for constructing an open circuit voltage model of the full battery to be tested according to the positive electrode open circuit voltage model and the negative electrode open circuit voltage model, wherein the open circuit voltage model of the full battery to be tested is the correspondence between the ion fraction of the positive electrode material of the full battery to be tested and the ion fraction of the negative electrode material of the full battery to be tested; and a prediction module, for predicting the capacity aging trend of the full battery to be tested according to the open circuit voltage model of the full battery to be tested and the multiple open circuit voltages of the full battery to be tested.

[0017] Optionally, the plurality of correction factors include a first correction factor and a second correction factor, and the first construction module is used to construct a positive electrode open circuit voltage model and a negative electrode open circuit voltage model based on the Nernst equation, the plurality of correction factors, the number of multiple types of lattice sites of the positive electrode material of the sample full battery, and the number of multiple types of lattice sites of the negative electrode material of the sample full battery, including: according to the Nernst equation, the first correction factor γ i , the number of various types of lattice sites N of the positive electrode materials of the sample full battery and the following formula are used to construct the positive electrode open circuit voltage model: And according to the Nernst equation, the second correction factor γ j , the number of various types of lattice sites M of the negative electrode material of the sample full battery and the following formula are used to construct the negative electrode open circuit voltage model:

[0018]

[0019] Where Δx i represents the site occupancy fraction of the positive electrode, Δx j Indicates the fraction of negative electrode position occupied, E 0，i represents the positive electrode redox potential, E 0，j represents the negative electrode redox potential, It represents the ratio of the number of inserted lattice sites to the number of available lattice sites in the cathode material. represents the ratio of the number of inserted lattice sites to the number of available lattice sites in the negative electrode material, represents the open circuit voltage of the positive electrode, represents the open circuit voltage of the negative electrode, e represents the elementary unit of charge, k represents the Boltzmann constant, and T represents the Kelvin temperature.

[0020] Optionally, the second construction module, for constructing an open circuit voltage model of the full battery to be tested according to the positive electrode open circuit voltage model and the negative electrode open circuit voltage model, comprises:

[0021] According to the positive open circuit voltage model Negative open circuit voltage model And the following formula, construct the open circuit voltage model of the full battery to be tested Among them, x PE represents the ion fraction of the positive electrode material of the full battery to be tested, x NE Represents the ion fraction of the negative electrode material of the full battery to be tested.

[0022] Optionally, the positive electrode material ion fraction x of the full battery to be tested PE And the negative electrode material ion fraction x of the full battery to be tested NE Determined by the following formula: Among them, y PE0 is the initial ion fraction of the positive electrode of the full battery to be tested, y NE0is the initial ion fraction of the negative electrode of the full battery to be tested, i is the discharge current, t is the discharge time, C PE is the content of positive electrode active material in the full battery to be tested, C NE is the content of negative electrode active material in the full battery to be tested.

[0023] Optionally, the prediction module is used to predict the capacity aging trend of the full battery to be tested based on the open circuit voltage model of the full battery to be tested and the multiple open circuit voltages of the full battery to be tested, including: determining the content of multiple positive electrode active substances of the full battery to be tested and the content of multiple negative electrode active substances of the full battery to be tested based on the open circuit voltage model of the full battery to be tested and the multiple open circuit voltages of the full battery to be tested; and predicting the capacity aging trend of the full battery to be tested based on the content of multiple positive electrode active substances of the full battery to be tested and the content of multiple negative electrode active substances of the full battery to be tested.

[0024] Through the above technical scheme, based on the Nernst equation, multiple correction factors, the number of various types of lattice sites of the positive electrode material of the sample full battery, and the number of various types of lattice sites of the negative electrode material of the sample full battery, a positive electrode open circuit voltage model and a negative electrode open circuit voltage model are constructed; according to the positive electrode open circuit voltage model and the negative electrode open circuit voltage model, an open circuit voltage model of the full battery to be tested is constructed, wherein the open circuit voltage model of the full battery to be tested is the correspondence between the ion fraction of the positive electrode material of the full battery to be tested and, optionally, the ion fraction of the negative electrode material; and according to the open circuit voltage model of the full battery to be tested and the multiple open circuit voltages of the full battery to be tested, the capacity aging trend of the full battery to be tested is predicted. The present invention is based on the Nernst equation and introduces multiple correction factors. It uses the number of multiple types of lattice sites of the positive electrode material of the sample full battery to establish the positive electrode open circuit voltage model and the negative electrode open circuit voltage model of the battery, so that the present invention is suitable for predicting the capacity of multi-phase intercalation material batteries; based on the above parameters, the open circuit voltage model of the full battery to be tested is further constructed. Without damaging the battery, the capacity aging trend of the full battery to be tested is predicted based on the open circuit voltage model of the full battery to be tested and the multiple open circuit voltages of the full battery to be tested, and then the remaining available cycle life of the battery to be tested can be predicted.

[0025] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] 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:

[0027] Figure 1is a flow chart of a method for predicting battery capacity aging trend provided by an embodiment of the present invention;

[0028] Figure 2 It is a rendering of the open circuit voltage model of the full battery in the embodiment of the present invention and the actual observed data;

[0029] Figure 3 It is a graph of the contents of multiple positive electrode active materials during a cycle charge and discharge test of a full battery to be tested;

[0030] Figure 4 It is a graph of the contents of multiple negative electrode active materials during a cycle charge and discharge test of a full battery to be tested;

[0031] Figure 5 is a graph for predicting the aging trend of the capacity of a full battery to be tested provided by an embodiment of the present invention;

[0032] Figure 6 is another capacity aging trend diagram of a full battery to be tested provided by an embodiment of the present invention;

[0033] Figure 7 is a conceptual diagram of a method for predicting battery capacity aging trend provided by an embodiment of the present invention;

[0034] Figure 8 The differential capacity curves of the soft-pack lithium battery under different cycles provided by the embodiment of the present invention;

[0035] Fig. 9 It is a structural diagram of a system for predicting battery capacity aging trend provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0036] 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.

[0037] Figure 1 The flowchart of the method for predicting the aging trend of battery capacity provided by the embodiment of the present invention includes the following steps S10-S12.

[0038] S10, constructing a positive electrode open circuit voltage model and a negative electrode open circuit voltage model based on the Nernst equation, multiple correction factors, the number of various types of lattice sites of the positive electrode material of the sample full battery, and the number of various types of lattice sites of the negative electrode material of the sample full battery. Step S10 constructs the positive electrode open circuit voltage model and the negative electrode open circuit voltage model with specific reference to steps S101-S102.

[0039] Furthermore, the plurality of correction factors include a first correction factor and a second correction factor.

[0040] S101, according to the Nernst equation, the first correction factor γ i , the number of various types of lattice sites N of the positive electrode materials of the sample full battery and the following formula are used to construct the positive electrode open circuit voltage model:

[0041]

[0042] Specifically, the open circuit voltage expression is calculated based on the Nernst equation: At the same time, the first correction factor γ is introduced i , where the first correction factor γ i is a dimensionless correction factor, so the open circuit voltage expression can be further simplified to The expression is made applicable to multiphase intercalated materials by introducing a first correction factor.

[0043] According to the open circuit voltage expression As well as the number of different types of lattice sites N of the positive electrode material of the sample full battery, the positive electrode open circuit voltage model is constructed: In this expression, Δx i 、E 0，i and γ i The three are unknown parameters of the expression, so a total of 3N parameters are introduced into the open circuit voltage model under N types of lattice sites.

[0044] Furthermore, by measuring the number of type lattice sites N of the positive electrode of the sample full battery and multiple positive electrode open circuit voltages of the sample full battery, and N and multiple positive electrode open circuit voltages Substituting into the above positive electrode open circuit voltage model, 3N Δx can be obtained. i 、E 0，i and γ i The numerical value of the parameter can be expressed by the following matrix: Among them, α 1-1 That is Δx1, α 2-1 That is E 0，1 , α 3-1 That is γ1, and so on, the 3N parameters in the above positive electrode open circuit voltage model can be calculated, so an open circuit voltage model suitable for any N types of lattice sites can be fitted.

[0045] The embodiment of the present invention introduces a first correction factor γ i And multiple types of lattice site numbers are used to construct a positive electrode open circuit voltage model, thereby making it suitable for the open circuit voltage model when the positive electrode of the battery is a multi-phase intercalation material.

[0046] S102, according to the Nernst equation, the second correction factor γ j, the number of various types of lattice sites M of the negative electrode material of the sample full battery and the following formula are used to construct the negative electrode open circuit voltage model:

[0047]

[0048] Where Δx i represents the site occupancy fraction of the positive electrode, Δx j Indicates the fraction of negative electrode position occupied, E 0，i represents the positive electrode redox potential, E 0，j represents the negative electrode redox potential, It represents the ratio of the number of inserted lattice sites to the number of available lattice sites in the positive electrode material (normalized capacity of the battery), represents the ratio of the number of inserted lattice sites to the number of available lattice sites in the negative electrode material, represents the open circuit voltage of the positive electrode, represents the open circuit voltage of the negative electrode, e represents the elementary unit of charge, k represents the Boltzmann constant, and T represents the Kelvin temperature.

[0049] Specifically, due to the differences in the types of positive and negative electrode materials of the battery, the number of lattice sites M and the second correction factor γ of various types of negative materials are calculated. j Construct the negative electrode open circuit voltage model. The specific construction process details are similar to those of constructing the positive electrode open circuit voltage model, and will not be repeated here.

[0050] S11. Constructing an open circuit voltage model of the full battery to be tested according to the positive electrode open circuit voltage model and the negative electrode open circuit voltage model.

[0051] Among them, the open circuit voltage model of the full battery to be tested is the corresponding relationship between the ion fraction of the positive electrode material of the full battery to be tested and the ion fraction of the negative electrode material of the full battery to be tested.

[0052] Further, according to the positive open circuit voltage model Negative open circuit voltage model And the following formula, construct the open circuit voltage model of the full battery to be tested Among them, x PE represents the ion fraction of the positive electrode material of the full battery to be tested, x NE Represents the ion fraction of the negative electrode material of the full battery to be tested.

[0053] Specifically, the positive electrode open circuit voltage model is constructed according to the above steps S101-S102 And the negative open circuit voltage model Further construct the open circuit voltage model of the full battery to be tested. The open circuit voltage model of the full battery to be tested can be composed of the positive electrode open circuit voltage model Negative open circuit voltage model The difference calculation is: It should be noted that the positive and negative electrode open circuit voltage models are based on capturing the possible phase change processes observable in the active materials, but they do not necessarily fully conform to the theoretical phase change processes of the materials themselves.

[0054] Furthermore, the positive electrode material ion fraction x of the full battery to be tested PE And the negative electrode material ion fraction x of the full battery to be tested NE Determined by the following formula:

[0055] Among them, y PE0 is the initial ion fraction of the positive electrode of the full battery to be tested, y NE0 is the initial ion fraction of the negative electrode of the full battery to be tested, i is the discharge current, t is the discharge time, C PE is the content of positive electrode active material in the full battery to be tested, C NE is the content of negative electrode active material in the full battery to be tested.

[0056] Specifically, the SOC (remaining charge) of the positive and negative electrodes is determined by the upper and lower voltage limits of the positive and negative electrode materials. Assuming that the material properties of the battery electrodes do not change throughout the cycle life, the OCV of the half-cell only needs to be parameterized once. The positive electrode of the full battery and the negative electrode of the full battery range are determined by x PE，1 , x PE，n , x NE，1 , x NE，n The decision can be expressed by matrix parameters: θ xcell = x can be set according to actual needs PE，1 , x PE，n , x NE，1 , x NE，n The embodiment of the present invention does not impose any specific restrictions on the value of x. PE，1 ~x PE，n The expression for the positive electrode material ion fraction of the full battery is expressed as: PE =y PE0 -i*t / C PE , correspondingly, x NE，1 ~x NE，n The expression for the negative electrode material ion fraction of the full battery between NE =y NE0 +i*t / C NE Among them, y PE0 , C PE ,y NE0 and C NE are all unknown parameters in the expression, t is x NE The independent variable is , and i is the discharge current of the whole battery.

[0057] like Figure 2 The figure shows the effect diagram of the open circuit voltage model of the full battery fitted by the embodiment of the present invention and the actual observed data. The capacity of the battery in this experiment is 1500mAh, the battery is a lithium battery LiCoC2 / Graphite soft pack battery, and the SOC is 0-60% charge and discharge cycle. The open circuit voltage model of the full battery provided by the embodiment of the present invention has a high degree of match with the actual observed open circuit voltage data.

[0058] S12. Predicting the capacity aging trend of the full battery to be tested based on the open circuit voltage model of the full battery to be tested and multiple open circuit voltages of the full battery to be tested.

[0059] Further, the contents of multiple positive active materials of the full battery to be tested and, optionally, the contents of multiple negative active materials of the full battery to be tested are determined according to the open circuit voltage model of the full battery to be tested and the multiple open circuit voltages of the full battery to be tested. And according to the contents of multiple positive active materials of the full battery to be tested and the contents of multiple negative active materials of the full battery to be tested, the capacity aging trend of the full battery to be tested is predicted.

[0060] Specifically, after determining the open circuit voltage model of the full battery and constructing the open circuit voltage model of the full battery to be tested according to the above step S11, At the same time, combined with the positive electrode material ion fraction x of the full battery to be tested PE And the negative electrode material ion fraction x of the full battery to be tested NE The expression is: The battery under test is subjected to multiple charge and discharge tests. For example, the battery under test is subjected to 700 charge and discharge tests to obtain the open circuit voltage of 700 full batteries under test. And the corresponding discharge current i and discharge time t, and from the 700 Select a number of data in the i and t parameters (for example, select 22 data), and you can get 22 y values ​​accordingly. PE0 ,y NEO , C PE and C NE .like Figure 3 The contents C of 22 positive electrode active materials in a full battery to be tested obtained after 700 cycles of charge and discharge test provided in an embodiment of the present invention PE ,like Figure 4 To obtain the content C of 22 negative electrode active materials after 700 cycles of charge and discharge test NE .

[0061] from Figure 3 and Figure 4 It can be seen that the content C of 22 positive electrode active materials PEAnd the content of 22 negative electrode active materials C NE The changes are very small. Figure 5 The capacity aging trend diagram of the full battery to be tested is predicted based on the content of multiple positive electrode active materials in the full battery and the content of multiple negative electrode active materials in the full battery. Therefore, it can be concluded that the full battery capacity of the full battery to be tested is still in a healthy state (the main reason for the battery to be tested to be in a healthy state is that the content of the positive electrode active material C PE , and the content of negative electrode active material C NE No attenuation occurred).

[0062] refer to Figure 6 is another capacity aging trend diagram of a full battery to be tested. The main reason for the aging of the battery to be tested is the content C of the negative electrode active material of the full battery to be tested. NE According to the embodiment of the present invention, the capacity aging trend and aging cause of the full battery to be tested can be predicted. Figure 7 A conceptual diagram of a method for predicting battery capacity aging trend provided by an embodiment of the present invention. It should be noted that the present invention is particularly suitable for predicting lithium battery capacity aging trend.

[0063] Figure 8 The present invention provides a lithium battery LiCoC2 / Graphite with a capacity of 4.5Ah under multiple cycles of charge and discharge. The present invention proposes a method for battery fault diagnosis and capacity aging prediction applicable to multi-phase intercalation materials. The key to this method is to establish a parameterized model of electrode open circuit voltage applicable to multi-phase intercalation materials. The internal parameter information of battery aging is extracted by measuring the open circuit voltage (OCV), and the battery is cycled in the test phase to quickly obtain different aging states of the battery; the internal state of the battery at different aging stages is identified by using a parameterized OCV model in the modeling phase to obtain the law of change of the internal parameters of the battery with the cycle; and then the evolution law of the obtained internal parameters of the battery is used in the prediction phase, and brought into the parameterized open circuit voltage model to obtain the evolution of the battery capacity. When the set lower capacity limit is reached, the number of cycles corresponding to the capacity is the battery life. The present invention does not require any physical parameters related to the characteristics of the battery material to be known in advance, and is applicable to any commercial battery, which improves the safe operation of commercial secondary batteries and the prediction of the remaining available cycle life.

[0064] The present invention is based on the Nernst equation and introduces multiple correction factors. It uses the number of multiple types of lattice sites of the positive electrode material of the sample full battery to establish the positive electrode open circuit voltage model and the negative electrode open circuit voltage model of the battery, so that the present invention is suitable for predicting the capacity of multi-phase intercalation material batteries; based on the above parameters, the open circuit voltage model of the full battery is further constructed. Without damaging the battery, the capacity aging trend of the full battery to be tested is predicted based on the open circuit voltage model of the full battery and multiple open circuit voltages of the full battery to be tested, and then the remaining available cycle life of the battery to be tested can be predicted.

[0065] On the other hand, Fig. 9 The present invention also provides a structural schematic diagram of a system for predicting the aging trend of battery capacity. The system for predicting the aging trend of battery capacity includes: a first construction module 1, which is used to construct a positive electrode open circuit voltage model and a negative electrode open circuit voltage model based on the Nernst equation, multiple correction factors, the number of multiple types of lattice sites of the positive electrode material of the sample full battery, and the number of multiple types of lattice sites of the negative electrode material of the sample full battery; a second construction module 2, which is used to construct an open circuit voltage model of the full battery to be tested based on the positive electrode open circuit voltage model and the negative electrode open circuit voltage model, wherein the open circuit voltage model of the full battery to be tested is the correspondence between the ion fraction of the positive electrode material of the full battery to be tested and the ion fraction of the negative electrode material of the full battery to be tested; and a prediction module 3, which predicts the capacity aging trend of the full battery to be tested based on the open circuit voltage model of the full battery to be tested and the multiple open circuit voltages of the full battery to be tested.

[0066] The method and effect of the system for predicting the battery capacity aging trend are the same as those in the above embodiment and will not be described in detail here.

[0067] Furthermore, the plurality of correction factors include a first correction factor and a second correction factor, and the first construction module is used to construct a positive electrode open circuit voltage model and a negative electrode open circuit voltage model based on the Nernst equation, the plurality of correction factors, the number of multiple types of lattice sites of the positive electrode material of the sample full battery, and the number of multiple types of lattice sites of the negative electrode material of the sample full battery, including: according to the Nernst equation, the first correction factor γ i , the number of various types of lattice sites N of the positive electrode materials of the sample full battery and the following formula are used to construct the positive electrode open circuit voltage model: And according to the Nernst equation, the second correction factor γ j , the number of various types of lattice sites M of the negative electrode material of the sample full battery and the following formula are used to construct the negative electrode open circuit voltage model: Where Δx i represents the site occupancy fraction of the positive electrode, Δx j Indicates the fraction of negative electrode position occupied, E 0，i represents the positive electrode redox potential, E 0，j represents the negative electrode redox potential, It represents the ratio of the number of inserted lattice sites to the number of available lattice sites in the cathode material. represents the ratio of the number of inserted lattice sites to the number of available lattice sites in the negative electrode material, represents the open circuit voltage of the positive electrode, represents the open circuit voltage of the negative electrode, e represents the elementary unit of charge, k represents the Boltzmann constant, and T represents the Kelvin temperature.

[0068] Furthermore, the second construction module is used to construct the open circuit voltage model of the full battery to be tested according to the positive electrode open circuit voltage model and the negative electrode open circuit voltage model, including: Negative open circuit voltage model And the following formula to construct the open circuit voltage model of the full battery to be tested Among them, x PE represents the ion fraction of the positive electrode material of the full battery to be tested, x NE Represents the ion fraction of the negative electrode material of the full battery to be tested.

[0069] Furthermore, the positive electrode material ion fraction x PE And the negative electrode material ion fraction x NE Determined by the following formula: Among them, y PE0 is the initial ion fraction of the positive electrode of the full battery to be tested, y NE0 is the initial ion fraction of the negative electrode of the full battery to be tested, i is the discharge current, t is the discharge time, C PE is the content of positive electrode active material in the full battery to be tested, C NE is the content of negative electrode active material in the full battery to be tested.

[0070] Furthermore, the prediction module is used to predict the capacity aging trend of the full battery to be tested based on the open circuit voltage model of the full battery to be tested and the multiple open circuit voltages of the full battery to be tested, including: determining the contents of multiple positive electrode active substances of the full battery to be tested and the contents of multiple negative electrode active substances of the full battery to be tested based on the open circuit voltage model of the full battery to be tested and the multiple open circuit voltages of the full battery to be tested; and predicting the capacity aging trend of the full battery to be tested based on the contents of multiple positive electrode active substances of the full battery to be tested and the contents of multiple negative electrode active substances of the full battery to be tested.

[0071] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods or systems. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware.

[0072] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0073] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for predicting battery capacity aging trend, characterized in that: The method comprises: Based on the Nernst equation, multiple correction factors, the number of various types of lattice sites of the positive electrode material of the sample full battery, and the number of various types of lattice sites of the negative electrode material of the sample full battery, a positive electrode open circuit voltage model and a negative electrode open circuit voltage model are constructed; According to the positive electrode open circuit voltage model and the negative electrode open circuit voltage model, constructing an open circuit voltage model of the full battery to be tested, wherein the open circuit voltage model of the full battery to be tested is a correspondence between the positive electrode material ion fraction of the full battery to be tested and the negative electrode material ion fraction of the full battery to be tested; and The capacity aging trend of the full battery to be tested is predicted based on the open circuit voltage model of the full battery to be tested and the multiple open circuit voltages of the full battery to be tested.

2. The method according to claim 1, characterized in that The plurality of correction factors include a first correction factor and a second correction factor, Based on the Nernst equation, multiple correction factors, the number of various types of lattice sites of the positive electrode material of the sample full battery, and the number of various types of lattice sites of the negative electrode material of the sample full battery, the positive electrode open circuit voltage model and the negative electrode open circuit voltage model are constructed, including: According to the Nernst equation, the first correction factor γ i , the number of various types of lattice sites N of the positive electrode materials of the sample full battery and the following formula are used to construct the positive electrode open circuit voltage model: According to the Nernst equation, the second correction factor γ j , the number of various types of lattice sites M of the negative electrode material of the sample full battery and the following formula are used to construct the negative electrode open circuit voltage model: Where Δx i represents the site occupancy fraction of the positive electrode, Δx j Indicates the fraction of negative electrode position occupied, E 0,i represents the positive electrode redox potential, E 0,j represents the negative electrode redox potential, It represents the ratio of the number of inserted lattice sites to the number of available lattice sites in the cathode material. represents the ratio of the number of inserted lattice sites to the number of available lattice sites in the negative electrode material, represents the open circuit voltage of the positive electrode, represents the open circuit voltage of the negative electrode, e represents the elementary unit of charge, k represents the Boltzmann constant, and T represents the Kelvin temperature.

3. The method according to claim 2, characterized in that The step of constructing an open circuit voltage model of the full battery to be tested according to the positive electrode open circuit voltage model and the negative electrode open circuit voltage model comprises: According to the positive open circuit voltage model Negative open circuit voltage model And the following formula, construct the open circuit voltage model of the full battery to be tested Among them, x PE represents the ion fraction of the positive electrode material of the full battery to be tested, x NE Represents the ion fraction of the negative electrode material of the full battery to be tested.

4. The method according to claim 3, characterized in that The positive electrode material ion fraction x of the full battery to be tested PE And the negative electrode material ion fraction x of the full battery to be tested NE Determined by the following formula: Among them, y PE0 is the initial ion fraction of the positive electrode of the full battery to be tested, y NEo is the initial ion fraction of the negative electrode of the full battery to be tested, i is the discharge current, t is the discharge time, C PE is the content of positive electrode active material in the full battery to be tested, C NE is the content of negative electrode active material in the full battery to be tested.

5. The method according to claim 4, characterized in that Predicting the capacity aging trend of the full battery to be tested according to the open circuit voltage model of the full battery to be tested and the multiple open circuit voltages of the full battery to be tested includes: Determining the contents of multiple positive electrode active materials of the full battery to be tested and the contents of multiple negative electrode active materials of the full battery to be tested according to the open circuit voltage model of the full battery to be tested and the multiple open circuit voltages of the full battery to be tested; and The capacity aging trend of the full battery to be tested is predicted based on the contents of multiple positive electrode active materials of the full battery to be tested and the contents of multiple negative electrode active materials of the full battery to be tested.

6. A system for predicting battery capacity aging trend, characterized in that: The system comprises: The first construction module is used to construct a positive electrode open circuit voltage model and a negative electrode open circuit voltage model based on the Nernst equation, multiple correction factors, the number of multiple types of lattice sites of the positive electrode material of the sample full battery, and the number of multiple types of lattice sites of the negative electrode material of the sample full battery; A second construction module is used to construct an open circuit voltage model of the full battery to be tested according to the positive electrode open circuit voltage model and the negative electrode open circuit voltage model, wherein the open circuit voltage model of the full battery to be tested is a correspondence between the positive electrode material ion fraction of the full battery to be tested and the negative electrode material ion fraction of the full battery to be tested; and A prediction module predicts the capacity aging trend of the full battery to be tested based on the open circuit voltage model of the full battery to be tested and the multiple open circuit voltages of the full battery to be tested.

7. The system according to claim 1, characterized in that The plurality of correction factors include a first correction factor and a second correction factor, The first construction module is used to construct a positive electrode open circuit voltage model and a negative electrode open circuit voltage model based on the Nernst equation, multiple correction factors, the number of multiple types of lattice sites of the positive electrode material of the sample full battery, and the number of multiple types of lattice sites of the negative electrode material of the sample full battery, including: According to the Nernst equation, the first correction factor γ i , the number of various types of lattice sites N of the positive electrode materials of the sample full battery and the following formula are used to construct the positive electrode open circuit voltage model: as well as According to the Nernst equation, the second correction factor γ j , the number of various types of lattice sites M of the negative electrode material of the sample full battery and the following formula are used to construct the negative electrode open circuit voltage model: Where Δx i represents the site occupancy fraction of the positive electrode, Δx j Indicates the fraction of negative electrode position occupied, E 0,i represents the positive electrode redox potential, E 0,j represents the negative electrode redox potential, It represents the ratio of the number of inserted lattice sites to the number of available lattice sites in the cathode material structure. It represents the ratio of the number of lattice sites for structural insertion to the number of available lattice sites in the negative electrode material. represents the open circuit voltage of the positive electrode, represents the open circuit voltage of the negative electrode, e represents the elementary unit of charge, k represents the Boltzmann constant, and T represents the Kelvin temperature.

8. The system according to claim 7, characterized in that The second construction module, for constructing an open circuit voltage model of the full battery to be tested according to the positive electrode open circuit voltage model and the negative electrode open circuit voltage model, comprises: According to the positive open circuit voltage model Negative open circuit voltage model And the following formula, construct the open circuit voltage model of the full battery to be tested Among them, x PE represents the ion fraction of the positive electrode material of the full battery to be tested, x NE Represents the ion fraction of the negative electrode material of the full battery to be tested.

9. The method according to claim 8, characterized in that The positive electrode material ion fraction x of the full battery to be tested PE And the negative electrode material ion fraction x of the full battery to be tested NE Determined by the following formula: Among them, y PE0 is the initial ion fraction of the positive electrode of the full battery to be tested, y NE0 is the initial ion fraction of the negative electrode of the full battery to be tested, i is the discharge current, t is the discharge time, C PE is the content of positive electrode active material in the full battery to be tested, C NE is the content of negative electrode active material in the full battery to be tested.

10. The system according to claim 9, characterized in that The prediction module, for predicting the capacity aging trend of the full battery to be tested according to the open circuit voltage model of the full battery to be tested and the multiple open circuit voltages of the full battery to be tested, comprises: Determining the contents of multiple positive electrode active materials of the full battery to be tested and the contents of multiple negative electrode active materials of the full battery to be tested according to the open circuit voltage model of the full battery to be tested and the multiple open circuit voltages of the full battery to be tested; and The capacity aging trend of the full battery to be tested is predicted based on the contents of multiple positive electrode active materials of the full battery to be tested and the contents of multiple negative electrode active materials of the full battery to be tested.

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