State of health estimation of lithium-ion battery with improved radial basis function neural network
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影响因子：8.857
DOI码：10.1016/j.energy.2022.125380
发表刊物：Energy
关键字：Lithium-ion battery; State of health; Improved gray wolf optimization; Improved radial basis function neural network
摘要：Accurate state of health (SOH) estimation for lithium-ion batteries is crucial to ensure the safety and reliability of electric vehicles. However, traditional neural network algorithms to estimate SOH often focus on fitting nonlinear fluctuation and is weak in the overall tracking trend. This paper thus proposes an improved radial basis function neural network (IRBFNN) to estimate the SOH with the simultaneous fitting of general trends and local fluctuations. A polynomial is provided to describe the overall trend of SOH. Meanwhile, the hidden layer of the IRBFNN converts the features nonlinearly to simulate the local battery capacity regeneration. Moreover, the initial parameters of the IRBFNN are obtained after training and then optimized by the improved gray wolf optimization algorithm. Two different datasets are utilized to verify the effectiveness of the presented method by comparing it with several other algorithms. Experimental results show that the IRBFNN-based method can accurately estimate the SOH, and the maximum estimation errors are within ±4%. Therefore, the results imply that the proposed method can effectively alleviate the problem of the poor estimation performance of traditional neural network-based algorithms in the later stage of battery aging.
论文类型：期刊论文
学科门类：工学
文献类型：J
卷号：262
页面范围：125380
是否译文：否
发表时间：2023-01-01
收录刊物：SCI、EI
发布期刊链接：https://www.sciencedirect.com/science/article/pii/S0360544222022629