Remaining useful life prediction of lithium-ion batteries with adaptive unscented kalman filter and optimized support vector regression

Abstract

To solve the problem of the inaccurate prediction on remaining useful life (RUL) for lithium-ion battery, we proposed an integrated algorithm which combines adaptive unscented kalman filter (AUKF) and genetic algorithm optimized support vector regression (GA-SVR). Firstly, the state space model with double exponential is established to describe the degradation of lithium battery. Then, the AUKF algorithm is introduced to update adaptively both the process noise covariance and the observation noise covariance. Next, the genetic algorithm is utilized to optimize the key parameters of SVR which realizes multi-step prediction. The effectiveness of the proposed method is verified by simulation experiments with NASA of battery dataset. Simulation results show that the proposed AUKF-GA-SVR achieves better prediction accuracy than existed methods such as unscented kalman filter, extended kalman filter, adaptive extended kalman filter (AEKF), adaptive unscented kalman filter, unscented kalman filter and relevance vector regression and AEKF-GA-SVR.