State-of-Charge Estimation of Lithium-Ion Battery Based on an Improved Kalman Filter

Accurate state-of-charge (SOC) estimation is essential to battery management system. The widely adopted estimation methods based on Kalman Filter (KF) fail to take the variable environmental conditions into consideration, which may result in a poor accuracy. This paper proposes a novel estimation model based on KF method to estimate SOC of Lithium-ion battery. In the proposed model, the noise variances are optimized for the system current state at each iteration, a variable forgetting factor is introduced to improve the algorithm's convergence and accuracy of estimation, and the artificial neural network (ANN) is applied for the measurement equation of KF. The experiments, based on Lithium-ion Battery set of NASA, show that the proposed SOC estimation model is valid and can improve the algorithm performance and accuracy and robustness.