Remaining useful life prediction of lithium-ion battery based on auto-regression and particle filter

Purpose-With the rapid development and stable operated application of lithium-ion batteries used in uninterruptible power supply(UPS),the prediction of remaining useful life(RUL)for lithium-ion battery played an important role.More and more researchers paid more attentions on the reliability and safety for lithium-ion batteries based on prediction of RUL.The purpose of this paper is to predict the life of lithium-ion battery based on auto regression and particle filter method.Design/methodology/approach-In this paper,a simple and effective RUL prediction method based on the combination method of auto-regression(AR)time-series model and particle filter(PF)was proposed for lithiumion battery.The proposed method deformed the double-exponential empirical degradation model and reduced the number of parameters for such model to improve the efficiency of training.By using the PF algorithm to track the process of lithium-ion battery capacity decline and modified observations of the state space equations,the proposed PF t AR model fully considered the declined process of batteries to meet more accurate prediction of RUL.Findings-Experiments on CALCE dataset have fully compared the conventional PF algorithm and the AR t PF algorithm both on original exponential empirical degradation model and the deformed doubleexponential one.Experimental results have shown that the proposed PFtAR method improved the prediction accuracy,decreases the error rate and reduces the uncertainty ranges of RUL,which was more suitable for the deformed double-exponential empirical degradation model.Originality/value-In the running of UPS device based on lithium-ion battery,the proposed AR t PF combination algorithm will quickly,accurately and robustly predict the RUL of lithium-ion batteries,which had a strong application value in the stable operation of laboratory and other application scenarios.