Prediction of remaining useful life of battery cell using logistic regression based on strong tracking particle filter

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 prediction through logistic regression combining least square support vector machine. There are two main contributions: first, we combine logistic regression with least square support vector machine for RUL estimation; second, we introduce logistic regression with particle update by a strong tracking particle filter.