Battery state of health estimation method and system based on segment charge data

Abstract

The application provides a battery state of health estimation method and system based on charging data of fragments, according to the cycle charging and discharging experimental results of multiple lithium batteries under different working conditions, the charging and discharging data of the complete SOC span under each cycle is obtained, and the battery state of health is calculated according to the total discharging capacity; the charging data is divided into interval sections with a certain span according to the charging voltage distribution interval, the capacity difference of each interval section is calculated, and each capacity section is used as an estimation feature; each feature is combined to form a new combined feature; the similarity between each combined feature is calculated, and the most similar feature combination is selected as the battery state of health calculation feature; the battery state of health is recalculated according to the selected feature combination, and the evaluation result is obtained. The application selects the capacity difference corresponding to the interval voltage in the charging data to define and evaluate the battery state of health SOH, which fully guarantees that the estimation method can have good estimation ability when the charging data has different SOC spans.

Description

Technical Field

[0001] This invention belongs to the field of battery health state estimation technology, specifically relating to a battery health state estimation method and system based on segment charging data. 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] With the widespread adoption of electric vehicles, the safety of lithium-ion batteries has become increasingly prominent. State of health (SOH) is a concrete manifestation of a battery's health status, including its charge, energy, and charge / discharge power. Accurate assessment of SOH allows for a comprehensive understanding of the battery's current condition, enabling adjustments to performance parameters, reducing risk, and allowing for the maintenance and replacement of substandard individual cells, thus lowering operating costs. Therefore, accurate and rapid assessment of battery health has become a key focus and challenge in battery research.

[0004] Current SOH estimation methods can be broadly categorized into experimental estimation, model-based methods, and data-driven methods. While experimental estimation is simple and direct, it requires sophisticated equipment and is time-consuming. Model-based methods offer high accuracy and robustness, enabling online analysis, but they involve significant computational costs and are susceptible to model noise. Data-driven methods, on the other hand, do not require consideration of complex electrochemical reactions during charging and discharging, are model-independent, and exhibit good adaptability, achieving accurate SOH estimation without complex modeling.

[0005] To address the challenge of accurate and rapid State of Health (SOH) estimation, several techniques have been proposed. For example, Chinese invention patent CN202211033008.9 proposes a lithium battery SOH estimation method based on constant-voltage charging current, extracting the current during the constant-voltage charging phase as a feature and inputting it into an LSTM network. This method requires charging data across the entire battery lifecycle, resulting in lengthy and cumbersome data acquisition. Chinese invention patent CN 202310857464.3 proposes an SOH estimation method based on local phase charging data, acquiring a battery aging cycle database, extracting health factors, and establishing a battery health state estimation model based on a neural network. This method requires constructing a neural network model and cannot meet the needs of efficient battery data processing in practical applications.

[0006] Existing data-driven methods for estimating SOH generally consist of several steps: data acquisition, feature extraction, model input, and SOH estimation. These methods require charging or discharging data across the entire SOC range. However, in practice, the charging process depends on user habits, and the charging start voltage or SOC is uncertain, which can significantly affect the estimation accuracy. Summary of the Invention

[0007] To address the aforementioned problems, this invention proposes a battery health state estimation method and system based on segmented charging data. This invention selects the capacity difference corresponding to the interval voltage in the charging data to define and evaluate the battery health state (SOH), ensuring that the estimation method has good estimation capability across different SOC spans in the charging data.

[0008] According to some embodiments, the present invention adopts the following technical solution:

[0009] A battery health state estimation method based on segment charging data includes the following steps:

[0010] Based on the results of cyclic charge-discharge experiments of multiple lithium batteries under different operating conditions, charge-discharge data for the complete SOC span under each cycle were obtained, and the battery health status was calculated based on the total discharge capacity.

[0011] The charging data is divided into intervals with a certain span according to the charging voltage distribution range, and the capacity difference of each interval is calculated. Each capacity segment is used as an estimation feature.

[0012] Combine the features to form new combined features;

[0013] Calculate the similarity between each combination of features, and select the most similar feature combination as the feature for calculating battery health status;

[0014] The battery health status is recalculated based on the selected combination of features to obtain the evaluation results.

[0015] As an alternative implementation, in the process of calculating the battery health status based on the total discharge capacity, the battery health status is a percentage of the current battery capacity and the rated capacity.

[0016] As an alternative implementation method, the capacity difference between each interval segment is the difference between the capacity boundaries of two adjacent interval segments.

[0017] As an alternative implementation, when combining the features, the capacity is added together to form a new combined feature.

[0018] As an alternative implementation method, n features can be combined to obtain n! combined features.

[0019] As an alternative implementation, when calculating the similarity between each combination of features, the Pearson correlation coefficient between each combination of features is calculated.

[0020] As a further implementation, the feature combination with the largest Pearson correlation coefficient is the selected feature combination.

[0021] As an alternative implementation method, the process of recalculating the battery health status based on the selected combination of features is as follows:

[0022]

[0023] C V1 and C V2 The capacity of the selected combination feature with the largest correlation coefficient is given by the upper and lower denominators, which are the capacity differences between the current loop and the initial loop, respectively.

[0024] A battery health state estimation system based on segmented charging data includes:

[0025] The data acquisition module is configured to obtain charge and discharge data for the complete SOC span under each cycle based on the cyclic charge and discharge test results of multiple lithium batteries under different operating conditions, and calculate the battery health status based on the total discharge capacity.

[0026] The segmentation module is configured to divide the charging data into segments with a certain span according to the charging voltage distribution range, calculate the capacity difference between each segment, and use each capacity segment as an estimation feature.

[0027] The feature combination module is configured to combine various features to form new combined features;

[0028] The selection module is configured to calculate the similarity between various combinations of features and select the most similar combination of features as the feature for calculating battery health status.

[0029] The evaluation module is configured to recalculate the battery health status based on the selected combination of features to obtain the evaluation results.

[0030] A computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the steps in the above method.

[0031] 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 steps in the method described above.

[0032] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0033] (1) In practical applications, lithium-ion batteries are not fully charged and discharged, resulting in the inability to obtain complete SOC range charge and discharge data. The present invention proposes a battery health state (SOH) definition method based on fragment charging data. Compared with the original SOH definition method, this method is simple and the data integrity requirement is greatly reduced, which can better meet the actual situation.

[0034] (2) The present invention uses the interval capacity corresponding to the interval voltage during the charging process of lithium-ion battery as a feature, and multiple sets of features are combined in different ways, which greatly improves the flexibility of estimation.

[0035] (3) This invention takes into account the problem that most existing prediction methods require network training of features, while fast and efficient estimation is needed in actual use. By eliminating network training, training time can be reduced, and estimation speed can be greatly improved without sacrificing a large amount of accuracy.

[0036] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0037] 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.

[0038] Figure 1 The SOH flowchart is defined based on segment charging data.

[0039] Figure 2 This is a voltage-time curve of an LCO battery during the charging phase.

[0040] Figure 3 This is a correlation coefficient diagram between combined features;

[0041] Figure 4 This is a SOH estimation diagram for different combinations of characteristics of LCO batteries. Detailed Implementation

[0042] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0043] It should be noted that the following detailed description is illustrative and intended to provide further explanation 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.

[0044] 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 scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0045] Example 1

[0046] The state of health (SOH) of a lithium battery is a crucial parameter characterizing its overall health. During actual use, battery performance gradually deteriorates, and the SOH decreases accordingly. Accurate and rapid estimation of the battery's SOH is essential for ensuring the safe and stable operation of the system. Parameters that change significantly during battery aging, such as capacity, internal resistance, and power, are often used to define SOH. The formula for the capacity definition is:

[0047]

[0048] In the formula: C c C0 represents the current battery capacity; C0 represents the rated capacity.

[0049] The formula for the internal resistance definition method is:

[0050]

[0051] In the formula: R end R is the internal resistance at the end of the battery's life. now R is the current internal resistance of the battery. new This is the internal resistance of a new battery at the time of manufacture.

[0052] The formula for the power definition method is:

[0053]

[0054] In the formula: P is the current actual power of the battery, and P0 is the rated power of the battery.

[0055] Besides the above-mentioned methods for defining State of Harmony (SOH), in actual battery pack applications, SOH can also be defined based on indicators such as battery aging inconsistency and battery self-discharge rate. Most manufacturers define SOH based on the remaining cycle life or cumulative ampere-hours and watt-hours of energy. However, in actual use, there are too many uncertainties, making it impossible to predict the future operating environment of the battery or accurately predict the remaining cycle life. Therefore, these definitions are not very practical. The internal resistance and power definitions require more difficult-to-obtain internal resistance and power data than capacity data; in practice, the capacity definition method is more widely used.

[0056] To address the aforementioned issues, this embodiment provides a method for defining and evaluating the State of Health (SOH) of a battery based on segmented charging data, including the following steps:

[0057] 1. Cyclic charge-discharge experiments were conducted on k lithium batteries under different operating conditions. The charge-discharge data of the complete SOC span under each cycle were obtained. The SOH defined by equation (1) was calculated based on the total discharge capacity.

[0058]

[0059] In the formula, SOHn The SOH is calculated for the k-th cell by the definition in equation (1).

[0060] 2. Divide the charging data into intervals of a set span based on the charging voltage distribution range, and calculate the capacity difference C between each interval. n Each capacity segment is used as an estimation feature.

[0061] C n =C v1 -C v2 (5)

[0062] In the formula, C V1 and C V2 These are the capacity boundaries corresponding to each interval segment.

[0063] 3. Combine the features and add up the capacity to form new combined features. n features can be combined to obtain n! combined features.

[0064]

[0065] 4. Calculate the Pearson correlation coefficient (cor) among the features of each combination. m The closer the correlation coefficient is to 1, the stronger the linear relationship between the data, and the more similar the two sets of data are.

[0066]

[0067] In the formula, D(C) m1 ) and D(C m2 ) is C m1 and C m2 The variance corresponding to the segment.

[0068] 5. Select the feature combination with the highest correlation coefficient as the feature for SOH calculation.

[0069] cor max =max(cor1,cor2,...cor n！ (8)

[0070] 6. Calculate the new definition of SOH based on the selected features.

[0071]

[0072] In the formula, C V1 and C V2 The maximum combined feature capacity is selected in equation (6), with the upper and lower denominators being the capacity difference between the current loop and the initial loop, respectively.

[0073] Equation (9) is the new definition of the SOH calculation formula. The battery health state SOH can be calculated quickly based on the new definition of the SOH calculation formula, which is simple and efficient.

[0074] Example 2

[0075] This embodiment, based on Embodiment 1, conducts a cyclic charge-discharge experiment on an LCO battery at a rate of 0.5C, and preprocesses the obtained data. The voltage-time curve during the battery charging phase is shown below. Figure 2 As shown.

[0076] The voltage range varies between 3.85V and 4.18V. The charging capacity data is categorized into different voltage ranges as shown in Table 1.

[0077] Table 1. Voltage Ranges and Feature Numbers

[0078] 3.85-3.9 1 3.9-3.95 2 3.95-4.0 3 4.0-4.05 4 4.05-4.1 5 4.1-4.15 6

[0079] Feature combinations are performed based on the charging data corresponding to the voltage range. To account for all combinations, all combined features are listed. Feature combinations are shown in Table 2, where horizontal axis i and vertical axis j together represent a combined feature.

[0080] Table 2. Feature Combination Table

[0081]

[0082] Correlation analysis was performed on the data of each combination of features. Since the State of Health (SOH) in this method is defined by capacity data, the more similar the capacity curves, the more similar the trends of the SOH in the healthy state. The larger the correlation coefficient between the feature combination data, the more significant the similarity of the calculated SOH will be. Thus, in practical situations, even when the voltage in the actual vehicle charging data passes through different voltage characteristic segments, relatively accurate and similar SOH estimates can be obtained, ensuring the practicality of this method. The correlation coefficients between the combined features are as follows: Figure 3 As shown.

[0083] Based on the correlation coefficient plot, the feature combination with the highest correlation coefficient when including different numbers of features is obtained. The SOH estimation plot under the selected feature combination is shown below. Figure 4 As shown, the green curve represents the SOH calculated according to equation (1).

[0084] according to Figure 4 It can be seen that the SOH value estimated by this method can well characterize the battery degradation state, and the SOH values ​​under different voltage range combinations are relatively similar, indicating that the method is effective and feasible, and more in line with actual application conditions.

[0085] In summary, the above embodiments propose a method for defining and estimating the State of Health (SOH) of a battery based on segmented charging data. The SOH is defined by selecting the capacity difference corresponding to the voltage intervals in the charging data, and a method for estimating the SOH based on segmented charging data is proposed. Users typically choose to charge a battery from a certain capacity until it is fully charged, making it relatively easy to obtain charging data covering the required voltage range. This method offers flexible feature combinations, ensuring good estimation capabilities across different SOC spans in the charging data.

[0086] Example 3

[0087] A battery health state estimation system based on segmented charging data includes:

[0088] The data acquisition module is configured to obtain charge and discharge data for the complete SOC span under each cycle based on the cyclic charge and discharge test results of multiple lithium batteries under different operating conditions, and calculate the battery health status based on the total discharge capacity.

[0089] The segmentation module is configured to divide the charging data into segments with a certain span according to the charging voltage distribution range, calculate the capacity difference between each segment, and use each capacity segment as an estimation feature.

[0090] The feature combination module is configured to combine various features to form new combined features;

[0091] The selection module is configured to calculate the similarity between various combinations of features and select the most similar combination of features as the feature for calculating battery health status.

[0092] The evaluation module is configured to recalculate the battery health status based on the selected combination of features to obtain the evaluation results.

[0093] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0094] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0095] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0096] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0097] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made by those skilled in the art without creative effort within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A battery health state estimation method based on segmented charging data, characterized in that, Includes the following steps: Based on the results of cyclic charge-discharge experiments of multiple lithium batteries under different operating conditions, charge-discharge data for the complete SOC span under each cycle were obtained, and the battery health status was calculated based on the total discharge capacity. The charging data is divided into intervals with a certain span according to the charging voltage distribution range, and the capacity difference of each interval is calculated. Each capacity difference is used as an estimation feature. Combine the features to form new combined features; Calculate the similarity between each combination of features, and select the most similar feature combination as the feature for calculating battery health status; The battery health status is recalculated based on the selected combination of features to obtain the evaluation results; The process of recalculating the battery health status based on the selected combination of features is as follows: and The capacity of the selected combination feature with the largest correlation coefficient is given by the upper and lower denominators, which are the capacity differences between the current loop and the initial loop, respectively.

2. The battery health state estimation method based on segmented charging data as described in claim 1, characterized in that, In the process of calculating the battery health status based on the total discharge capacity, the battery health status is the percentage of the current battery capacity to the rated capacity.

3. The battery health state estimation method based on segmented charging data as described in claim 1, characterized in that, The capacity difference between each interval is the difference between the capacity boundaries of two adjacent intervals.

4. The battery health state estimation method based on segmented charging data as described in claim 1, characterized in that, When combining features, the capacity is added together to form a new combined feature; n features combined result in n! combined features.

5. The battery health state estimation method based on segmented charging data as described in claim 1, characterized in that, When calculating the similarity between the combined features, the Pearson correlation coefficient between the combined features is calculated.

6. The battery health state estimation method based on segmented charging data as described in claim 5, characterized in that, The feature combination with the highest Pearson correlation coefficient is the selected feature combination.

7. A battery health state estimation system based on segmented charging data, characterized in that, include: The data acquisition module is configured to obtain charge and discharge data for the complete SOC span under each cycle based on the cyclic charge and discharge test results of multiple lithium batteries under different operating conditions, and calculate the battery health status based on the total discharge capacity. The segmentation module is configured to divide the charging data into segments with a certain span according to the charging voltage distribution range, calculate the capacity difference between each segment, and use each capacity difference as an estimation feature. The feature combination module is configured to combine various features to form new combined features; The selection module is configured to calculate the similarity between various combinations of features and select the most similar combination of features as the feature for calculating battery health status. The evaluation module is configured to recalculate the battery health status based on the selected combination of features to obtain the evaluation result. The process of recalculating the battery health status based on the selected combination of features is as follows: and The capacity of the selected combination feature with the largest correlation coefficient is given by the upper and lower denominators, which are the capacity differences between the current loop and the initial loop, respectively.

8. A computer-readable storage medium, characterized in that, Used to store computer instructions, which, when executed by a processor, complete the steps of the method according to any one of claims 1-6.

9. 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 steps of the method according to any one of claims 1-6.

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