Publication

Abstract : Accurate State of Charge (SoC) estimation is critical for the efficient and safe operation of lithium-ion (Li-ion) batteries. Traditional methods for SoC estimation have limitations, and recent research has focused on using machine learning techniques to improve SoC estimation accuracy. In this research article, we present a study on SoC estimation for Li-ion batteries using machine learning techniques. We collected a dataset of battery voltage, current, and temperature measurements and used machine learning algorithms to estimate SoC. We compared the performance of machine learning algorithms with traditional methods such as coulomb counting and open-circuit voltage (OCV) method. Our results show that machine learning algorithms outperform traditional methods in estimating SoC, with an average error of less than 1 %. We discuss the advantages and limitations of using machine learning for SoC estimation and the potential for future research in this area. The findings of this study have implications for the development of battery management systems that can improve battery performance, safety, and lifetime.