Abstract：Lithium-ion batteries are widely utilized in electric vehicles and energy storage systems due to their superior performance. However, with the continuous operation of lithium-ion batteries, the performance of lithium-ion batteries deteriorates over time, which indirectly leads to the degradation of equipment performance or a catastrophic occurrence. Therefore, accurately predicting the remaining useful life (RUL) of the Lithium-ion batteries can maintain and replace the battery in time to ensure the safe and reliable operation of the battery system. This paper extracts indirect health factors that can characterize the degradation of battery performance from the charging process, and analyzes the correlation between health factors and capacity via the correlation analysis methods of Pearson and Spearman. A prediction method of an improved ant lion optimization algorithm (IALO) and support vector regression (SVR) based on indirect health factors is proposed, which can achieve accurate online prediction of RUL for Lithium-ion batteries. The battery data set of NASA is utilized to verify the IALO-SVR method. Compared with the BP and SVR methods, the experimental results show that the IALO-SVR method can accurately predict the RUL of lithium-ion battery.
徐佳宁, 倪裕隆, 朱春波. 基于改进支持向量回归的锂电池剩余寿命预测[J]. 电工技术学报, 2021, 36(17): 3693-3704.
Xu Jianing, Ni Yulong, Zhu Chunbo. Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Improved Support Vector Regression. Transactions of China Electrotechnical Society, 2021, 36(17): 3693-3704.
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2021, Vol. 36 
