State of charge estimation techniques of Li-ion battery of electric vehicles

Abstract

The Lithium-ion batteries are widely utilized in the electric car, bus, and two-wheeler industries because of their high energy density, low cost, extended lifespan, high power density, and stable voltage. One of the essential systems that must be present in any electric vehicle (EV) is the battery management system (BMS). One major input to BMS is state of charge to ensure the battery's durability, safety, and reliable operation. The state-of-charge (SoC) estimation of EV batteries plays a crucial role in optimizing their performance and extending their lifespan. As batteries are nonlinear and time-variant devices, estimating the state of charge or instantaneous remaining charge within a battery is a particularly challenging task. This paper covers a deep understanding of SoC estimation techniques for BMS. The two main approaches to explaining estimation of instantaneous remaining charge are model-based which relies on various battery models and their mathematical equations to explain the battery characteristics. The second approach is data-driven which studies large measured battery data sets to understand the behavior of running algorithms. Model-based approaches are based on series-parallel combinations of resistance and capacitance electrical circuits, while data-driven approaches are based on neural networks and machine learning algorithms. The review highlights the strengths and limitations of each technique, suggesting that hybrid approaches could yield more robust results. It emphasizes the importance of future research in integrating multiple information sources and developing standard evaluation procedures to enhance SoC estimation accuracy and its practical application in EVs.