基于膨胀应力的锂离子电池剩余使用寿命预测

Remaining Useful Life Prediction of Lithium-ion Batteries Based on Expansion Stress

准确快速预测锂离子电池剩余使用寿命(Remaining useful life, RUL)对系统安全稳定运行至关重要。然而,电池内部退化机理复杂,外部运行工况多变,给RUL预测带来了极大挑战。为此,提出了一种基于电池膨胀应力的RUL预测方法。提取电池膨胀应力信息,分别分析可逆膨胀和不可逆膨胀与容量之间的关系,并计算相关性。将可逆膨胀和不可逆膨胀作为特征参数,构建并训练长短期记忆(Long short-term memory, LSTM)神经网络,实现RUL精准快速预测。通过在UMBL公开数据集上验证,利用膨胀应力特征能更好地学习电池老化状态,捕捉电池容量下降趋势。结果表明,在不同循环起点和多种老化条件下,RMSE和MAE分别小于0.82%和0.70%,所提出的方法能够精准快速预测RUL,鲁棒性强。

Accurate and fast prediction of the remaining useful life(RUL)of lithium-ion batteries is crucial for safe and stable system operation.However,the complex internal degradation mechanism and the changeable external operating conditions of the battery bring great challenges to RUL prediction.Therefore,a RUL prediction method based on battery expansion stress is proposed in this paper.The battery expansion stress information is extracted,the relationship between reversible expansion as well as irreversible expansion and capacity is analyzed respectively,and the correlation is calculated.The reversible expansion and irreversible expansion are used as feature parameters,and long short-term memory(LSTM)neural network is constructed and trained to achieve accurate and fast RUL prediction.Through the verification on UMBL public dataset,the use of expansion stress features enables better learning of the battery aging state and captures the battery capacity degradation trend.The results show that the RMSE and MAE are within 0.82%and 0.70%,respectively,under different cycle starting points and various aging conditions.The proposed method can predict RUL with strong robustness accurately and quickly.