State-of-Charge Estimation of Lithium-Ion Battery Based on Extended Kalman Filter Algorithm

As the core component of electric vehicle, lithium-ion battery needs to adopt effective battery management method to prolong battery life and improve the reliability and safety. The accurate estimation of the battery SOC can be used to prevent the battery over charge and over discharge, reduce damage to the battery and improve battery performance, which plays a vital role in the battery management system. The study of battery SOC estimation mainly focused on the battery model construction and SOC estimation algorithm. Aiming at the problem that the state of charge (SOC) of electric vehicle is difficult to be accurately estimated under complex operating conditions, based on the parameter identification of the equivalent circuit of a ternary polymer lithium-ion battery, an Extended Kalman Filter (EKF) algorithm was used to estimate the SOC of the ternary polymer lithium-ion battery. Simulation and experimental results show that the estimation of SOC can be carried out by using the EKF algorithm under the conditions of China Passenger Car Condition (Chinacar) and new European driving cycle (NEDC) Compared with the coulomb counting method, the average error of SOC estimation can be realized is 1.042% and 1.138% respectively, the maximum error within 4%. Application of this algorithm to achieve SOC estimation has good robustness and convergence.