Application of Unscented Particle Filter in Remaining Useful Life Prediction of Lithium-ion Batteries
نویسندگان
چکیده
Accurate prediction of the remaining useful life of a faulty component is important to the health management of the system. It gives operators information about when the component should be replaced. This paper studied the remaining useful life prediction of the lithium-ion batteries. Some work has been done to solve this problem, but it still remains challengeable. Particle filter (PF) is a relatively effective method. However, the accuracy is not high. This paper introduces an improved PF algorithm unscented particle filter (UPF) into the prediction. First, PF algorithm and UPF algorithm are described separately. Then, a degradation model is built based on the understanding of lithiumion batteries. Finally, the model is combined with the training data and the algorithms to get the prediction results. According to the analysis results, it can be seen that UPF can predict the actual RUL with an error less than 4%. Keywords-PF;UPF;Remaining useful life; Degradation model.
منابع مشابه
Remaining useful life prediction of lithium-ion battery with unscented particle filter technique
Accurate prediction of the remaining useful life of a faulty component is important to the prognosis and health management of a system. It gives operators information about when the component should be replaced. In recent years, a lot of research has been conducted on battery reliability and prognosis, especially the remaining useful life prediction of the lithium-ion batteries. Particle filter...
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