Battery health prognosis with gated recurrent unit neural networks and hidden Markov model considering uncertainty quantification
点击次数：
影响因子：7.247
DOI码：10.1016/j.ress.2022.108978
发表刊物：Reliability Engineering & System Safety
关键字：Lithium-ion battery; State of health; prognostics and health management; uncertainty quantification; gated recurrent unit; hidden Markov model
摘要：With the widespread use of lithium-ion batteries in various fields, battery failures become the most critical concerns that may lead to enormous economic losses and even serious safety issues. The prognostics and health management of lithium-ion batteries helps to ensure reliable and safe battery operations. Existing studies on the state of health of batteries mainly focus on improving and refining prediction models, while the emerging technologies that address uncertainty issues in the battery degradation process are also receiving more and more attention. In this paper, we propose a new state of health prediction method by using the gated recurrent unit neural networks and the hidden Markov model with considering uncertainty quantification. According to the empirical mode decomposition, the battery capacity is decomposed into the global downward trend and the local fluctuations. We train gated recurrent unit neural networks to fit the long-term global downward trend without gradient vanishing, and a hidden Markov model to fit the local fluctuations for quantifying the uncertainty introduced by the capacity recovery phenomenon in battery degradation. Finally, numerical experiments are conducted on two famous datasets, the experimental results demonstrate that the proposed method outperforms on the accuracy and reliability for battery state of health prediction.
论文类型：期刊论文
卷号：230
页面范围：108978
是否译文：否
发表时间：2023-02-01
收录刊物：SCI
发布期刊链接：https://www.sciencedirect.com/science/article/pii/S0951832022005932