基于多任务学习下的快速充电锂离子电池SOH估计方法

doi:10.19595/j.cnki.1000-6753.tces.250144

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Abstract In the application of lithium-ion batteries (LIBs), the popularity of fast charging technology has brought significant convenience to consumers, particularly for portable devices such as electric vehicles and smartphones, with an increasing demand for fast charging. However, fast charging strategies have a significant impact on the state of health (SOH) of LIBs, particularly in a multistage constant-current fast charging environment. Although fast charging improves charging efficiency, battery chemistry and thermal management issues accelerate the battery's aging due to the drastic changes in charging current and voltage, which affect its performance and lifetime. Therefore, it becomes imperative to accurately estimate the SOH of Li-ion batteries under such charging conditions.
Existing SOH estimation methods suffer from insufficient robustness and high computational cost under fast charging conditions. Building on the previous innovative method, this study utilizes the charging and discharging voltage profiles to extract the sampling counts within the equal voltage range as the dual inputs for the health features. This method leverages critical information from the battery charging and discharging process, thereby significantly reducing computational complexity by simplifying the feature extraction process.
A multi-task learning framework is used to enhance the robustness of the SOH estimation further. The framework utilizes the shared layer of the LSTM model to share information among multiple related tasks, thereby optimizing the learning process and performance, especially in cases where some health features are missing. In this way, the model can better adapt to the uncertainty and noise in the data, thereby improving its performance under fast charging strategies.
A large public fast charging dataset is used for extensive testing. The results demonstrate that the proposed method performs well under multistage constant-current fast-charging conditions, with the root mean square error (RMSE) and mean absolute error (MAE) of the SOH estimation within 1%. The coefficient of determination (R²) reaches more than 0.98. In addition, the proposed method demonstrates strong robustness and stability when handling various charging strategies and missing features.
In summary, the voltage profile-based SOH estimation method proposed in this study provides a new solution for the health management of Li-ion batteries under fast charging conditions, which applies to fast charging scenarios of electric vehicles and portable devices. It provides essential theoretical support for the design, optimization, and management of LIBs in the future.

Key words： Lithium-ion battery state of health multi-task learning framework long short-term memory

Received: 21 January 2025

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TM912

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	Mao Ling
	Lin Tao
	Zhao Jianhui
	Zhao Jinbin

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Mao Ling,Lin Tao,Zhao Jianhui等. SOH Estimation Method for Fast Charging Lithium Battery Based on Multi-Task Learning[J]. Transactions of China Electrotechnical Society, 2026, 41(2): 714-724.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.250144 OR https://dgjsxb.ces-transaction.com/EN/Y2026/V41/I2/714

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