相关向量机预测锂离子电池剩余有效寿命

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Abstract With the rapid development of new energy vehicles, lithium-ion batteries have been widely used. Accurately predicting its remaining useful life (RUL) is crucial for rational planning of battery usage. At present, machine algorithms and model prediction have been widely used to predict the battery remaining useful life. This study adopts a data-driven method to predict the RUL of lithium-ion battery. By using the relevance vector machine (RVM), the long-term forecast is divided into multiple short-term forecasts, which is combined with auto-correlation function, grey correlation model, Kalman filter (KF) to optimize and improve the model. The relative errors of the prediction based on the modified RVM model in the three group of target cells are 5.46%, 7.14%, and 6.29%, respectively. The results show that the prediction results of this model are better than other models.

Key words： remain useful life (RUL) lithium-ion battery relevance vector machine (RVM) grey correlation model

Received: 21 October 2022

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	Articles by authors
	YU Peiwen
	YU Yajuan
	CHANG Zeyu
	ZHANG Zhiqi
	CHEN Lai

Cite this article:

YU Peiwen,YU Yajuan,CHANG Zeyu等. Remain useful life prediction of lithium-ion battery based on relevance vector machine[J]. Electrical Engineering, 2023, 24(2): 1-5.

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http://dqjs.cesmedia.cn/EN/Y2023/V24/I2/1

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