State of health estimation of large-cycle lithium-ion batteries based on error compensation of autoregressive model

Abstract

Accurate assessment of Lithium-ion battery state of health (SOH) is the core issue of Lithium-ion battery safety management. A novel SOH estimation model for large-cycle lithium-ion batteries with error compensation mechanism—combining autoregressive model, equal voltage discharge time and relevance vector machine (AR-EVDTRVM) model is proposed in this paper. Firstly, the SOH prediction framework of large-cycle Lithium-ion batteries based on the autoregressive (AR) model is established. Then, using the relevance vector machine (RVM) algorithm, an error compensation mechanism characterized by the equal voltage discharge time (EVDT) of Lithium-ion batteries is proposed. Finally, the experimental verification of AR-EVDTRVM model is carried out on the large-cycle and small-cycle lithium battery datasets, respectively. Compared with other methods, results show that the AR-EVDTRVM model has high estimation accuracy and strong robustness.