Abstract：The accurate estimation of the state of health (SOH) of lithium-ion batteries is a key technology in battery management system (BMS). This paper presents an online SOH prediction method combined with data-driven and empirical models. Through incremental capacity analysis (ICA), the time spent under two voltage rise segments with high correlation with SOH was identified as the external health factor (HF) of the battery, and a data-driven model of battery aging was established using gaussian process regression (GPR). The data-driven model was used to predict the SOH of the battery in the initial working period, and the exponential empirical model was fitted with the predicted values. Then the SOH of the subsequent cycle was predicted by the exponential model. And the parameters of the exponential model were modified by the observer every fixed number of cycles to ensure the accuracy of SOH prediction. The experimental results show that the proposed method can keep the prediction accuracy at a high level while reducing the burden of battery monitoring equipment.
王萍, 弓清瑞, 张吉昂, 程泽. 一种基于数据驱动与经验模型组合的锂电池在线健康状态预测方法[J]. 电工技术学报, 2021, 36(24): 5201-5212.
Wang Ping, Gong Qingrui, Zhang Ji’ang, Cheng Ze. An Online State of Health Prediction Method for Lithium Batteries Based on Combination of Data-Driven and Empirical Model. Transactions of China Electrotechnical Society, 2021, 36(24): 5201-5212.
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2021, Vol. 36 
