State of Charge Estimation of Lithium-ion Batteries for Whole Life Cycle based on Dual Adaptive Extended Kalman Filter Algorithm

Electric vehicles have developed rapidly all over the world. The performance of battery management system (BMS) has a great influence on the energy management and safety of batteries. State of charge (SOC) estimation is one of the core functions of BMS. In this paper, a method for whole-life-cycle SOC estimation based on simplified first principle (SFP) model and dual adaptive extended Kalman filter (DAEKF) algorithm is proposed. Firstly, the aging experiment of lithium batteries is carried out, and parameters of SFP model is identified offline. Secondly, the sensitivity of parameters is analyzed and the parameters that need to be corrected online are selected. Then, parameters are updated online based on DAEKF algorithm, so as to obtain high accurate SOC estimation results for the whole life. It can be known from experiment that the SOC estimation errors in different aging stages are all below 2.5%.