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As an important part of prognostics and health management, accurate remaining useful life (RUL) prediction for lithium (Li)-ion batteries can provide helpful reference for when to maintain the batteries in advance. This paper presents a novel method to predict the RUL of Li-ion batteries. This method is based on the framework of improved particle learning (PL). The PL framework can prevent particle...
The RUL prediction of battery is an effective approach to improve the battery reliability and service life. This paper proposes a novel evaluation algorithm of battery states which is named logistic regression based on strong tracking particle filter for battery RUL prediction. The core idea of this algorithm is to approximate the non-linear and non-Gaussian process of state update of battery RUL...
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