Real-Time State of Charge Estimation for Tri-Electrode Rechargeable Zinc–Air Flow Batteries via Pulse Response

Abstract

Accurate estimation of the state of charge (SOC) is essential for the optimal operation of batteries. However, to achieve such accuracy remains challenging for tri-electrode rechargeable zinc–air flow batteries (TRZAFBs) due to their flat voltage profiles. This study presents an innovative SOC identification technique based on the optimization of battery model parameters derived from pulse response data. Model parameters are extracted from pulse steps within the experimental data, establishing correlations between these parameters and SOC. Such correlations are then utilized as constraints in the optimization process. Results indicate that the slope of total resistance effectively identifies SOC with acceptable accuracy. The proposed method is further enhanced by integrating it with an extended Kalman filter (EKF) to enable real-time SOC estimation. Various initial SOC guess conditions and optimization frequencies are tested, demonstrating that EKF combined with the proposed optimization technique accurately tracks the true SOC in real-time and effectively corrects the incorrect initial SOC guesses. Additionally, the results show that the proposed technique can compete with other alternative methods in terms of multiple-cycle stability and surpass them in terms of convergence of true SOC for zinc–air batteries (ZABs).