A method and system for evaluating the aging nonlinearity of a lithium-ion power battery

Abstract

The application discloses a lithium ion power battery aging nonlinearity evaluation method and system, which comprises the following steps: obtaining the battery residual capacity and the termination cycle number of the lithium ion power battery after each charge-discharge experiment, and obtaining a nonlinear aging path according to the battery residual capacity and the termination cycle number; taking the direct line between the starting point and the ending point of the nonlinear aging path as a linear aging path; obtaining a first aging nonlinearity according to the tangent angle at the starting point and the ending point of the nonlinear aging path; obtaining a second aging nonlinearity according to the maximum ratio between the nonlinear aging path and the linear aging path; obtaining a third aging nonlinearity according to the correlation between the nonlinear aging path and the linear aging path; and obtaining a comprehensive evaluation result of the aging nonlinearity by performing weighted summation on the first aging nonlinearity, the second aging nonlinearity and the third aging nonlinearity, so that the result is objective and accurate.

Description

Technical Field

[0001] This invention relates to the field of lithium-ion power battery evaluation technology, and in particular to a method and system for evaluating the aging nonlinearity of lithium-ion power batteries. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] During charging and discharging, lithium ions travel back and forth between the positive and negative electrode materials inside the battery through the electrolyte, converting electrical energy into chemical energy through electron transfer. During the use of lithium-ion power batteries, the battery gradually ages due to the increasing number of charge-discharge cycles, eventually reaching the end of its life (EOL). After EOL, the battery performance will significantly decline, further leading to operational failures and accidents. Complex changes occur internally within lithium-ion power batteries during operation, such as SEI film growth, electrolyte degradation, and graphite material shedding, resulting in a non-linear capacity decay.

[0004] Studies have shown that the nonlinear aging of lithium-ion power batteries can be divided into three stages: the first stage is characterized by slow capacity degradation, with an overall linear aging trend; the second stage sees a rapid shift in capacity degradation from its previous relatively gradual state, marking a turning point in the aging trend; and the third stage is a rapid decline in capacity, with accelerated internal reactions and faster aging, which may lead to battery failure or complete inoperability if continued use continues. These three aging stages exhibit distinct degradation characteristics, but overall, lithium-ion power battery aging demonstrates a nonlinear nature. Therefore, accurately defining and evaluating the nonlinearity of lithium-ion power battery aging is a crucial indicator for assessing battery aging. Accurately quantifying the nonlinearity of battery aging is of significant importance for battery health assessment, lithium-ion power battery retirement, and secondary utilization.

[0005] Currently, many scholars have conducted extensive research on methods for quantifying the nonlinearity of lithium-ion power battery aging. However, the current methods for quantifying the nonlinearity of lithium-ion power battery aging are relatively simple and computationally complex.

[0006] For example, Chinese invention patent CN202120980627.8 calculates the nonlinear aging degree of a battery based on the characteristic area of ​​its capacity, but this method is complex and inefficient.

[0007] Chinese invention patent CN202010306912.7 establishes a lithium battery aging model based on the electrochemical model of lithium-ion power batteries and combined with external characteristics. This method requires a large amount of data to correct the model.

[0008] Chinese invention patent CN201910659908.6 assesses the aging degree of lithium-ion power batteries based on their current usable capacity. However, to accurately determine the current usable capacity of lithium-ion power batteries, constant current discharge is often used to deplete the battery. This method is accurate but time-consuming.

[0009] Therefore, current research methods for lithium-ion power battery aging are quite complex, and a complete theoretical system and methodology have not yet been formed, making it impossible to efficiently assess and effectively monitor the aging state of existing batteries. Summary of the Invention

[0010] To address the aforementioned issues, this invention proposes a method and system for evaluating the aging nonlinearity of lithium-ion power batteries. The method plots a capacity degradation curve based on the remaining capacity and the number of cycles terminated after each charge-discharge experiment, and employs multiple aging nonlinearity evaluation methods for comprehensive quantification, resulting in objective and accurate results.

[0011] To achieve the above objectives, the present invention adopts the following technical solution:

[0012] In a first aspect, the present invention provides a method for evaluating the aging nonlinearity of lithium-ion power batteries, comprising:

[0013] The remaining capacity and number of cycles after each charge-discharge experiment of the lithium-ion power battery are obtained, and a nonlinear aging path is obtained based on this. The direct connection between the start and end points of the nonlinear aging path is taken as the linear aging path.

[0014] The first aging nonlinearity is obtained by the angle between the tangents at the start and end points of the nonlinear aging path;

[0015] The second aging nonlinearity is obtained by the maximum ratio between the nonlinear aging path and the linear aging path.

[0016] The third aging nonlinearity is obtained based on the correlation between the nonlinear aging path and the linear aging path.

[0017] The comprehensive evaluation result of aging nonlinearity is obtained by weighted summation of the first, second, and third aging nonlinearities.

[0018] As an alternative implementation method, the rated capacity of the lithium-ion power battery is determined by conducting a charge-discharge experiment, with the rated capacity as the starting point; when the remaining capacity of the battery is lower than a set threshold, the charge-discharge experiment is stopped, and the number of termination cycles and the remaining capacity of the battery at this point are obtained, with the remaining capacity of the battery at this point as the endpoint.

[0019] As an alternative implementation, the first aging nonlinearity is: drawing tangents at the start and end points of the nonlinear aging path, and obtaining the first aging nonlinearity AND1 based on the angle θ formed by the intersection of the two tangents; specifically:

[0020] As an alternative implementation method, the second aging nonlinearity is:

[0021] After each charge-discharge experiment, obtain the actual remaining battery capacity on the nonlinear aging path and the theoretical remaining battery capacity on the linear aging path.

[0022] The difference between the actual remaining battery capacity and the theoretical remaining battery capacity is used to obtain the remaining capacity difference.

[0023] Take the maximum value of the differences among all remaining capacity differences, and use the ratio of the maximum value of the difference to the corresponding actual remaining battery capacity as the second aging nonlinearity.

[0024] As an alternative implementation, the third aging nonlinearity is obtained by calculating the Pearson correlation coefficient between the nonlinear aging path and the linear aging path.

[0025] As an alternative implementation method, the third aging nonlinearity AND3 is:

[0026] AND3 = log 100 {100*[1-cor(L,L o )]}*100%

[0027] Where, cor(L,L) o ) represents the nonlinear aging path L and the linear aging path L o The Pearson correlation coefficient.

[0028] As an alternative implementation, the first aging nonlinearity, the second aging nonlinearity, and the third aging nonlinearity are weighted, and the weights of the three are added together to be 1.

[0029] Secondly, the present invention provides a lithium-ion power battery aging nonlinearity evaluation system, comprising:

[0030] The aging trend mapping module is configured to obtain the remaining capacity and number of termination cycles of the lithium-ion power battery after each charge-discharge experiment, and to obtain a non-linear aging path based on this. The direct connection between the start and end points of the non-linear aging path is used as the linear aging path.

[0031] The first evaluation module is configured to obtain the first aging nonlinearity based on the angle between the tangents at the start and end points of the nonlinear aging path.

[0032] The second evaluation module is configured to obtain the second aging nonlinearity based on the maximum ratio between the nonlinear aging path and the linear aging path.

[0033] The third evaluation module is configured to obtain the third aging nonlinearity based on the correlation between the nonlinear aging path and the linear aging path.

[0034] The comprehensive evaluation module is configured to perform a weighted summation of the first aging nonlinearity, the second aging nonlinearity, and the third aging nonlinearity to obtain a comprehensive evaluation result of the aging nonlinearity.

[0035] Thirdly, the present invention provides an electronic device including a memory and a processor, and computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, perform the method described in the first aspect.

[0036] Fourthly, the present invention provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the method described in the first aspect.

[0037] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0038] This invention proposes a method and system for evaluating the aging nonlinearity of lithium-ion power batteries. Based on the remaining capacity and the number of cycles terminated after each charge-discharge experiment, a capacity degradation curve is plotted. Multiple aging nonlinearity evaluation methods are used to accurately and comprehensively quantify the aging nonlinearity of lithium-ion power batteries. The results are objective, accurate, not easily affected by external interference, and highly robust. Different aging nonlinearities can also be selected according to the current aging characteristics of lithium-ion power batteries to evaluate the battery's health status or aging degree, further accurately guiding the retirement and secondary use of lithium-ion power batteries.

[0039] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0040] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0041] Figure 1 This is a flowchart of the lithium-ion power battery aging nonlinearity evaluation method provided in Embodiment 1 of the present invention;

[0042] Figure 2 This is a schematic diagram illustrating the calculation of aging nonlinearity using the double tangent method provided in Embodiment 1 of the present invention;

[0043] Figure 3 This is a schematic diagram illustrating the calculation of aging nonlinearity using the maximum ratio method provided in Embodiment 1 of the present invention;

[0044] Figure 4 This is a schematic diagram of calculating aging nonlinearity using the correlation coefficient method provided in Embodiment 1 of the present invention. Detailed Implementation

[0045] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0046] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0047] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments of the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. Furthermore, it should be understood 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 apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0048] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0049] Example 1

[0050] This embodiment presents a method for evaluating the aging nonlinearity of lithium-ion power batteries, such as... Figure 1 As shown, it includes:

[0051] The remaining capacity and number of cycles after each charge-discharge experiment of the lithium-ion power battery are obtained, and a nonlinear aging path is obtained based on this. The direct connection between the start and end points of the nonlinear aging path is taken as the linear aging path.

[0052] The first aging nonlinearity is obtained by the angle between the tangents at the start and end points of the nonlinear aging path;

[0053] The second aging nonlinearity is obtained by the maximum ratio between the nonlinear aging path and the linear aging path.

[0054] The third aging nonlinearity is obtained based on the correlation between the nonlinear aging path and the linear aging path.

[0055] The comprehensive evaluation result of aging nonlinearity is obtained by weighted summation of the first, second, and third aging nonlinearities.

[0056] In this embodiment, the rated capacity of the lithium-ion power battery is obtained by conducting charge and discharge experiments; the rated capacity of the battery is the starting point of the nonlinear aging path and the linear aging path.

[0057] The remaining capacity of the battery after each charge-discharge test was obtained by conducting a cyclic charge-discharge experiment on the lithium-ion power battery.

[0058] When the remaining battery capacity is lower than the set threshold (set to 80% of the initial rated capacity in this embodiment), the cyclic charge-discharge experiment is stopped, and the number of termination cycles and the remaining battery capacity at this time are obtained. The remaining battery capacity at this time is the endpoint of the nonlinear aging path and the linear aging path.

[0059] Therefore, based on the remaining battery capacity and the number of cycles terminated after each charge-discharge experiment, a nonlinear aging path L is plotted, which is the actual capacity degradation curve of the lithium-ion power battery; the direct line connecting the start and end points of the nonlinear aging path is taken as the linear aging path L. o This refers to the theoretical capacity degradation curve of lithium-ion power batteries.

[0060] In this embodiment, the first aging nonlinearity is obtained using the double-tangent method, specifically:

[0061] Tangents are drawn at the start and end points of the nonlinear aging path, and the first aging nonlinearity AND1 is obtained based on the angle θ formed by the intersection of the two tangents; specifically:

[0062]

[0063] In this embodiment, the second aging nonlinearity is obtained using the maximum ratio method, specifically:

[0064] After each charge-discharge experiment, obtain the actual remaining battery capacity C along the nonlinear aging path. k , and the theoretical remaining battery capacity corresponding to the linear aging path;

[0065] The difference between the actual remaining battery capacity and the theoretical remaining battery capacity is used to obtain the remaining capacity difference ΔQ. k , where k is the kth charge-discharge experiment;

[0066] MaxΔQ is the maximum difference among all remaining capacity differences. k MaxΔQ, the maximum difference k With the corresponding actual remaining battery capacity C k The ratio of AND2 is used as the second aging nonlinearity;

[0067]

[0068] In this embodiment, the correlation coefficient method is used to obtain the third aging nonlinearity, specifically:

[0069] Calculate the nonlinear aging path L and the linear aging path L o The Pearson correlation coefficient C (C∈(0,1)); L and L o The larger the correlation coefficient C, the lower the probability of the corresponding battery aging path distribution. Since the logarithmic function is monotonically increasing within its domain, taking the logarithm does not change the relative relationship of the data and can optimize the data structure distribution; therefore, the third aging nonlinearity AND3 is:

[0070] AND3 = log 100 {100*[1-cor(L,L o )]}*100% (3)

[0071] In this embodiment, the first aging nonlinearity, the second aging nonlinearity, and the third aging nonlinearity are weighted and summed to obtain the comprehensive evaluation result of aging nonlinearity.

[0072]

[0073] In the formula, ω1, ω2 and ω3 are the weights corresponding to the first aging nonlinearity, the second aging nonlinearity and the third aging nonlinearity, respectively.

[0074] This embodiment uses lithium-ion phosphate (LFP) / graphite batteries and conducts cyclic charge-discharge experiments in a horizontal cylindrical fixture with a 48-channel Arbin LBT potentiometer in a forced convection temperature chamber set at 30°C. The battery has a rated capacity of 1.1 Ah and a rated voltage of 3.3 V. All individual cells are discharged at a constant current of 4C during the discharge phase. Data such as temperature, voltage, current, and time for each cycle are recorded in the aging cycle. The analysis examines lithium-ion power batteries with the same lifespan but exhibiting different nonlinear aging paths. The capacity aging curves are biaxially normalized on a coordinate system, and the aging nonlinearity of the lithium-ion power batteries is analyzed using three different methods.

[0075] like Figure 2 The diagram shows the calculation of aging nonlinearity using the double-tangent method. The included angles of the tangents of the nonlinear aging paths for cells 41 and 42 are θ1 = 2.1796 and θ2 = 1.8116, respectively. The resulting aging nonlinearities are... and

[0076] like Figure 3The diagram shows the calculation of aging nonlinearity using the maximum ratio method. After each cycle, the remaining capacity difference between the nonlinear and linear aging paths for cells 41 and 42 is obtained, with the maximum values ​​being MaxΔQ. 41 and MaxΔQ 42 , and their respective current remaining battery capacity C 41 C 42 By taking the ratio, the aging nonlinearity was obtained as follows: and

[0077] like Figure 4 The diagram shows the calculation of aging nonlinearity using the correlation coefficient method. The nonlinear aging path for cells 41 and 42 is L. 41 and L 42 Calculate the linear aging path L respectively. o The Pearson correlation coefficients are C0 and C0, respectively. 41 =0.9787, C 42 =0.8285, then the corresponding aging nonlinearities are respectively and

[0078] Furthermore, by taking weights The calculated comprehensive evaluation results of aging nonlinearity are as follows: and Different weights can be applied depending on the occasion and requirements to comprehensively evaluate battery aging.

[0079] Example 2

[0080] This embodiment provides a lithium-ion power battery aging nonlinearity evaluation system, including:

[0081] The aging trend mapping module is configured to obtain the remaining capacity and number of termination cycles of the lithium-ion power battery after each charge-discharge experiment, and to obtain a non-linear aging path based on this. The direct connection between the start and end points of the non-linear aging path is used as the linear aging path.

[0082] The first evaluation module is configured to obtain the first aging nonlinearity based on the angle between the tangents at the start and end points of the nonlinear aging path.

[0083] The second evaluation module is configured to obtain the second aging nonlinearity based on the maximum ratio between the nonlinear aging path and the linear aging path.

[0084] The third evaluation module is configured to obtain the third aging nonlinearity based on the correlation between the nonlinear aging path and the linear aging path.

[0085] The comprehensive evaluation module is configured to perform a weighted summation of the first aging nonlinearity, the second aging nonlinearity, and the third aging nonlinearity to obtain a comprehensive evaluation result of the aging nonlinearity.

[0086] It should be noted that the above modules correspond to the steps described in Embodiment 1, and the examples and application scenarios implemented by the above modules and the corresponding steps are the same, but are not limited to the content disclosed in Embodiment 1. It should also be noted that the above modules, as part of the system, can be executed in a computer system such as a set of computer-executable instructions.

[0087] In further embodiments, the following is also provided:

[0088] An electronic device includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, perform the method described in Embodiment 1. For brevity, further details are omitted here.

[0089] It should be understood that in this embodiment, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0090] Memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of memory may also include non-volatile random access memory. For example, memory may also store information about the device type.

[0091] A computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the method described in Embodiment 1.

[0092] The method in Example 1 can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor. The software modules can reside in readily available storage media in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, a detailed description is not provided here.

[0093] Those skilled in the art will recognize that the units, i.e., algorithm steps, of the various examples described in connection with this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0094] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for evaluating the aging nonlinearity of lithium-ion power batteries, characterized in that, include: The remaining capacity and number of cycles after each charge-discharge experiment of the lithium-ion power battery are obtained, and a nonlinear aging path is obtained based on this. The direct connection between the start and end points of the nonlinear aging path is taken as the linear aging path. The first aging nonlinearity is obtained by the angle between the tangents at the start and end points of the nonlinear aging path; The second aging nonlinearity is obtained by the maximum ratio between the nonlinear aging path and the linear aging path. The third aging nonlinearity is obtained based on the correlation between the nonlinear aging path and the linear aging path. The third degree of aging nonlinearity is obtained by calculating the Pearson correlation coefficient between the nonlinear and linear aging paths; the third degree of aging nonlinearity... for: in, Nonlinear aging path Compared with linear aging path The Pearson correlation coefficient; The comprehensive evaluation result of aging nonlinearity is obtained by weighted summation of the first, second, and third aging nonlinearities.

2. The method for evaluating the aging nonlinearity of lithium-ion power batteries as described in claim 1, characterized in that, The rated capacity of a lithium-ion power battery is determined by conducting charge-discharge experiments, with the rated capacity as the starting point. When the remaining capacity of the battery is lower than a set threshold, the charge-discharge experiment is stopped, and the number of termination cycles and the remaining capacity of the battery at this point are obtained, with the remaining capacity of the battery at this point as the endpoint.

3. The method for evaluating the aging nonlinearity of lithium-ion power batteries as described in claim 1, characterized in that, The first aging nonlinearity is defined as follows: Tangents are drawn at the start and end points of the nonlinear aging path, and the angle formed by the intersection of the two tangents is... Obtain the first aging nonlinearity Specifically: .

4. The method for evaluating the aging nonlinearity of lithium-ion power batteries as described in claim 1, characterized in that, The second aging nonlinearity is: After each charge-discharge experiment, obtain the actual remaining battery capacity on the nonlinear aging path and the theoretical remaining battery capacity on the linear aging path. The difference between the actual remaining battery capacity and the theoretical remaining battery capacity is used to obtain the remaining capacity difference. Take the maximum value of the differences among all remaining capacity differences, and use the ratio of the maximum value of the difference to the corresponding actual remaining battery capacity as the second aging nonlinearity.

5. The method for evaluating the aging nonlinearity of lithium-ion power batteries as described in claim 1, characterized in that, The first aging nonlinearity, the second aging nonlinearity, and the third aging nonlinearity are weighted, and the weights of the three are added together to 1.

6. A lithium-ion power battery aging nonlinearity evaluation system, characterized in that, Implementing the lithium-ion power battery aging nonlinearity evaluation method as described in any one of claims 1-5, comprising: The aging trend mapping module is configured to obtain the remaining capacity and number of termination cycles of the lithium-ion power battery after each charge-discharge experiment, and to obtain a non-linear aging path based on this. The direct connection between the start and end points of the non-linear aging path is used as the linear aging path. The first evaluation module is configured to obtain the first aging nonlinearity based on the angle between the tangents at the start and end points of the nonlinear aging path. The second evaluation module is configured to obtain the second aging nonlinearity based on the maximum ratio between the nonlinear aging path and the linear aging path. The third evaluation module is configured to obtain the third aging nonlinearity based on the correlation between the nonlinear aging path and the linear aging path. The comprehensive evaluation module is configured to perform a weighted summation of the first aging nonlinearity, the second aging nonlinearity, and the third aging nonlinearity to obtain a comprehensive evaluation result of the aging nonlinearity.

7. An electronic device, characterized in that, It includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, which, when executed by the processor, perform the method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, Used to store computer instructions, which, when executed by a processor, perform the method described in any one of claims 1-5.

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