Abstract：The state of health is the core of battery health management. An accurate estimation of the state of health is of great significance to ensure the safe, reliable, and long-life operation of lithium-ion batteries. To this end, this paper proposes a method for estimating the state of health of lithium-ion batteries based on incremental capacity curves and gray correlation analysis long short-term memory neural networks. This method extracts several different aging characteristics by analyzing the attenuation mode of battery charging incremental capacity curves during the aging process. In order to reduce the computational complexity, the gray correlation analysis method is introduced for features analysis and screening. Then, the extracted aging features are used as the input to train the long short-term memory network and estimate the battery health status. Finally, accelerated battery aging tests based on three different working conditions are conducted to verify the proposed method. The experimental results show that the proposed method exhibits excellent performance in estimating the state of health of the battery, and it shows good robustness under different working conditions and different number of training cycles.
周才杰, 汪玉洁, 李凯铨, 陈宗海. 基于灰色关联度分析-长短期记忆神经网络的锂离子电池健康状态估计[J]. 电工技术学报, 2022, 37(23): 6065-6073.
Zhou Caijie, Wang Yujie, Li Kaiquan, Chen Zonghai. State of Health Estimation for Lithium-Ion Battery Based on Gray Correlation Analysis and Long Short-Term Memory Neural Network. Transactions of China Electrotechnical Society, 2022, 37(23): 6065-6073.
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