非线性系统
均方误差
电池(电)
健康状况
荷电状态
人工神经网络
控制理论(社会学)
计算机科学
锂离子电池
算法
人工智能
数学
功率(物理)
统计
物理
控制(管理)
量子力学
作者
Zhenhai Gao,Haicheng Xie,Xianbin Yang,Wentao Wang,Yongfeng Liu,Youqing Xu,Baosong Ma,Xinhua Liu,Siyan Chen
标识
DOI:10.1016/j.est.2023.109690
摘要
The burgeoning growth of green energy in the transportation sector has resulted in increased expectations for battery longevity and safety. However, the capacity of lithium-ion batteries (LIBs) decreases with each successive charge and discharge cycle. And under harsh operating conditions, the capacity decay can exhibit strong nonlinearity. To enable effective battery management under such complex conditions, it is crucial to possess precise understanding of the state of health (SOH) of LIB. In this study, low-temperature aging experiments were designed to obtain capacity attenuation data of LIBs. Then the nonlinear component in capacity decay is identified and transformed into model nonlinear correction. Subsequently, we developed a long short-term memory (LSTM) neural network and extracted incremental capacity (IC) features based on experimentally collected data to train the model. The findings indicate that by training with only one cell aging dataset, the model can estimate the SOH for other samples. Furthermore, we introduced nonlinear correction features with an attention mechanism (AM) and analyzed the improvement of prediction accuracy through feature optimization and network adjustment to achieve SOH estimation with a maximum root mean square error (RMSE) of 0.92 %. This approach provides a potential solution for predicting SOH of LIBs at low temperature.
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