Recent developments and challenges in state-of-charge estimation techniques for electric vehicle batteries: A review

The escalating use of lithium-ion battery packs in electric vehicles (EVs) has resulted in a pressing demand for accurately and consistently estimating the State of Charge (SOC). For EVs to operate safely and effectively, improving vehicle range anticipation and optimizing battery performance, SOC estimation is essential. This article is a summary of the most recent techniques for estimating the SOC of lithium-ion batteries, which are used in electric cars and vehicles. It discusses the types of battery specifications and their characteristics suitable for electric vehicles, emphasising their benefits. Additionally, it describes the types of SOC estimation that include model-based approaches, data-driven methods, and filter-based models. Also, it looks at the advantages and disadvantages of these models and difficulties faced, such as the need for real-time implementation, sophisticated models, and data needs while implementing the algorithms. This investigation introduces a unique method to intelligent learning that makes use of hybrid models that can adjust to changing temperatures in addition to data- driven models. Lastly, the review study compares the accuracy of various approaches that include hardware and software requirements for technical execution of the same, which will help scientists and researchers develop a state-of-charge estimate approach that is both accurate and dependable for use in upcoming sustainable electric automobile applications.