SOC estimation of Lithiumion battery based on an Extended Hinfinity filter The accuracy of the state of charge (SOC) is of great significance to electric vehicles and is one of the key technical issues in the development of electric vehicles. Therefore, an accurate SOC estimation strategy is important for the research of electric vehicle battery management. In this paper, the extended Hinfinity filter (EHF) algorithm obtained by combined the extended Kalman filter (EKF) with the H infinity filter (HIF) is chosen as the SOC estimation method. The algorithm can compensate for the error caused by the accuracy of the sensor and the lack of accuracy of the initial value to some extent. The experimental results show that this is an ideal SOC estimation method for electric vehicle batteries.

State of Charge Estimation for LithiumIon Batteries Based on NARX Neural Network and UKF Lithiumion batteries have been widely used as the energy storage systems in electric vehicles. State of Charge (SOC) is one of the most important characteristics of battery system. It is essential for efficient use of the battery and ensures the safety of the electric vehicle. In this paper, a nonlinear autoregressive with exogenous inputs neural network (NARXNN) architecture is designed to estimate the SOC. The proposed method requires no model or knowledge of batteryxe2x80x99s internal parameters, but rather uses the batteryxe2x80x99s voltage, chargedischarge currents in various ambient temperature to accurately estimate batteryxe2x80x99s SOC. An unscented Kalman filter is used to reduce the errors in the neural networkbased SOC estimation. The adaptability, efficiency, and robustness of the model are evaluated using the FUDS, US06 and DST driving cycles at varying temperatures conditions. The results prove that the proposed NARXNNUKF model achieves higher accuracy with less computational time under different temperature conditions and electric vehicle driving cycles.