State of Charge Estimation for Rechargeable Batteries Based on the Nonlinear Double-Capacitor Model

Abstract

State of charge (SOC) estimation plays a foundational role in advanced battery management systems, having attracted much attention in the past decade. It is widely acknowledged that the accuracy of SOC estimation largely depends on the accuracy of the selected model. In this thesis, SOC estimation methods are developed based on the nonlinear double-capacitor (NDC) model, a novel equivalent circuit model that is distinctly capable of simulating the charge diffusion inside an electrode of a battery and capturing the battery’s nonlinear voltage behavior simultaneously. With improved predictive accuracy, the NDC model provides a new opportunity for enabling more accurate SOC estimation. With this motivation, the well-known extended Kalman filter (EKF) and unscented Kalman filter (UKF) are utilized to perform SOC estimationbased on the NDC model. The EKF is desirable here as it leads to efficient computation, straightforward implementation, and good convergence in its application to the NDC model, which is low-dimensional and governed by linear dynamics along with nonlinear output. The UKF is another popular version of the Kalman filter that belongs to the sigma-point filter family, and provably offers second-order accuracy under certain conditions, contrasting with the first-order accuracy of the EKF. The proposed SOC estimation methods are validated through simulations and experimental data under various conditions, showing significant accuracy as well as robustness to different levels of initialization error and noise.