An Enhanced Lithium-Ion Battery Model for Estimating the State of Charge and Degraded Capacity Using an Optimized Extended Kalman Filter

Abstract

Lithium-ion batteries have become the most appropriate batteries to use in modern electric vehicles due to their high-power density, long lifecycle, and low self-discharge rate. The precise estimation of the state of charge (SOC) in lithium-ion batteries is essential to assure their safe use, increase the battery lifespan, and achieve better management. Various methods of SOC estimation for lithium-ion batteries have been used. Among these methods, the model-based estimation method is the most practical and reliable. The accuracy of the utilized model is a crucial factor in realizing better SOC estimation in the model-based method. In this paper, an enhanced battery model is proposed to estimate the SOC precisely via an optimized extended Kalman filter. The model considers the most influencing factors on the estimation accuracy, such as temperature, aging, and self-discharge. The parameterization of the model has defined the dependency of sensitive parameters on state estimation. As a fundamental step before estimating the SOC, the capacity degradation is evaluated using a straightforward approach. Later, a particle swarm optimization algorithm is utilized to optimize the vector of process noise covariance to enhance the state estimation. The performance of the proposed method is compared to recent techniques in the literature. The results indicate the effectiveness of the proposed approach in terms of both accuracy and computational simplicity.