非线性系统
电池(电)
马尔可夫链
锂离子电池
稳健性(进化)
维纳过程
工程类
控制理论(社会学)
计算机科学
在线模型
过程(计算)
电池容量
可靠性工程
人工智能
数学
机器学习
物理
控制(管理)
功率(物理)
量子力学
数学分析
生物化学
化学
统计
基因
操作系统
作者
Yixing Zhang,Feng Fan,Shunli Wang,Jinhao Meng,Jiale Xie,Rui Ling,Hongpeng Yin,Ke Zhang,Yi Chai
出处
期刊:Applied Energy
[Elsevier]
日期:2023-07-01
卷期号:341: 121043-121043
被引量:12
标识
DOI:10.1016/j.apenergy.2023.121043
摘要
The accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is very important for battery management systems and predictive maintenance. However, lithium-ion batteries have a high degree of internal nonlinearity. There are two switching states during the operation of batteries operating, while the switching time point is also uncertain. In different switching states and random switching times, various unpredictable phenomena, such as capacity recovery or capacity decline could occur, which renders the accurate prediction of RUL challenging. To address this problem, a method for predicting the RUL was proposed in this work based on the nonlinear-drift-driven Wiener process and the Markov chain switching model. First, the nonlinear-drift-driven Wiener process was used to describe the time-varying battery degradation characteristics. The switching model was then applied to predict the future battery working state. Finally, the fuzzy system was employed to integrate the two by combining the battery degradation characteristics. The online update strategy of the model was simulated and validated, resulting in good adaptability and robustness. Two sets of real-case battery data from the National Aeronautics and Space Administration were also included during the validation process. The proposed method was systematically compared to other models in predicting the RUL of the batteries. From the acquired results, it was demonstrated that the proposed method was superior in predicting the RUL of batteries with improved accuracy and safety.
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