Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on Improved Artificial Fish Swarms Forgetting Factor Least Squares and Differential Evolution Extended Kalman Filter

Abstract

State of Charge (SOC) estimation is the focus of battery management systems, and it is critical to accurately estimate battery SOC in complex operating environments. To weaken the impact of unreasonable forgetting factor values on parameter estimation accuracy, an artificial fish swarm (AFS) strategy is introduced to optimize the forgetting factor of forgetting factor least squares (FFRLS) and to model the lithium-ion battery using a first-order RC model. A new method AFS-FFRLS is proposed for online parameter identification of the first-order RC model. In SOC estimation, it is not reasonable to fix the process noise covariance, and the differential evolution (DE) algorithm is combined with the extended Kalman filter (EKF) algorithm to achieve dynamic adjustment of the process noise covariance. A joint algorithm named AFS-FFRLS-DEEKF is proposed to estimate the SOC. to verify the reasonableness of the proposed algorithm, experiments are conducted under HPPC, BBDST and DST conditions, and the average errors of the joint algorithm under the three conditions are 1.9%, 2.7% and 2.4%, respectively. The validation results show that the joint algorithm improves the accuracy of SOC estimation.