A Two-Step Parameter Optimization Method for Low-Order Model-Based State-of-Charge Estimation

Abstract

The state-of-charge (SOC) estimation is an enabling technique for the efficient management and control of lithium-ion batteries (LIBs). This article proposes a novel method for online SOC estimation, which manifests itself with both high accuracy and low complexity. Particularly, the particle swarm optimization (PSO) algorithm is exploited to optimize the model parameters to ensure high modeling accuracy. Following this endeavor, the PSO algorithm is used to tune the error covariances of extended Kalman filter (EKF) leveraging the early stage segmental data of LIB utilization. Within this PSO-based tuning framework, the searching boundary is derived by scrutinizing the error transition property of the system. Experiments are performed to validate the proposed two-step PSO-optimized SOC estimation method. Results show that even by using a simple first-order model, the proposed method can give rise to a high SOC accuracy, which is comparative to those using complex high-order models. The proposed method is validated to excavate fully the potential of model-based estimators so that the computationally expensive model upgrade can be avoided.