1. School of Materials Science & Engineering, Beijing Institute of Technology, Beijing 100081; 2. Beijing Institute of Technology Chongqing Innovation Center, Chongqing 401120
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.
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2023, Vol. 24 
