A Comprehensive Review of Categorization and Perspectives on State-of-Charge Estimation Using Deep Learning Methods for Electric Transportation

Lithium-ion batteries are an excellent choice for electric transportation because of their high energy density, minimum self-discharge, and prolonged cycle life. The performance of electric transportation depends on the battery management system (BMS) for efficient functioning in vehicles. The state of charge (SOC) is one of the crucial BMS parameters to indicate the available charge in the vehicle. A reliable and accurate SOC prediction is crucial for an effective electric vehicle operation but SOC estimation is challenging since it depends on multiple variables including ambient temperature, battery age, charging, and discharging current. The data-driven techniques use an approach to run sophisticated algorithms on a vast quantity of measured battery data to understand its behavior. Lithium-ion battery state of charge assessment poses a complex difficulty. Temperature and aging affect the non-linear connection between voltage and SOC, accurate current measurement is an essential parameter that requires rigorous calibration to manage inaccuracies. Estimation is further complicated by hysteresis effects during charge and discharge cycles, different C-rate dependencies, and state of health parameters. To solve critical challenges, the paper highlights the recent advancements in model-based approaches, coulomb counting techniques, and machine learning methodologies. By summarizing the basic principles and presenting a comprehensive overview of SOC estimation through deep learning, the review paper aims to serve as a valuable resource for researchers, and practitioners in the field of battery management systems for electric transportation applications.