Combined Coulomb-Counting and Open-Circuit Voltage Methods for State of Charge Estimation of Li-Ion Batteries

Li-ion batteries (LIBs) find extensive application in the automotive industry, particularly in electric vehicles (EVs). Given their unparalleled adoption, LIBs have become a staple choice for powering these eco-friendly modes of transportation. Therefore, accurately calculating the state of charge (SOC) of an electric vehicle (EV) battery is paramount to using it consistently and safely. This study aims to compare the commonly employed methods, Coulomb-Counting (CC) and Open-Circuit Voltage (OCV), for estimating the SOC of the battery. The temperature and error readings render the OCV method ineffectual, whereas the initial SOC and measurement uncertainty problems generate a cumulative error in the CC methodology. To circumvent these problems, we propose a technique that uses an OCV-SOC polynomial connection fitted with the Particle Swarm Optimization (PSO) algorithm. By adopting a polynomial relationship that considers these impacts, this method tackles some of the problems with the OCV method, including temperature and load changes. The OCV-SOC relationship’s ideal polynomial coefficients can be discovered using the PSO algorithm, a heuristic optimization technique. The battery’s SOC is then monitored during charge and discharge cycles using the Coulomb-Counting approach, which starts with the estimated SOC as its initial value.