Loading...
Dr. S. Jeyasudha is a Professor in the Department of Electrical and Electronics Engineering at K. Ramakrishnan College of Technology, Tiruchirappalli, Tamil Nadu, India. She is actively involved in teaching and research in electrical engineering, smart energy systems, and Artificial Intelligence applications. Her research interests include electric vehicles, battery management systems, renewable energy technologies, intelligent control systems, and predictive analytics. She contributes significantly to academic research and engineering education.
The swift uptake of Electric Vehicles (EVs) has heightened the necessity for sophisticated Battery Management Systems (BMS) that can guarantee battery safety, dependability, efficiency, and durability. Lithium-ion batteries, prevalent in contemporary electric vehicles, undergo progressive deterioration influenced by charging cycles, thermal conditions, driving environments, and ageing. Precise assessment of Battery State of Health (SOH), State of Charge (SOC), State of Power (SOP), and Remaining Useful Life (RUL) is crucial for optimising vehicle efficiency, minimising maintenance expenses, augmenting battery safety, and promoting sustainable mobility. Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Internet of Things (IoT), Cloud Computing, Digital Twins, and Explainable Artificial Intelligence (XAI) have surfaced as formidable technologies for advanced battery surveillance and predictive analysis. AI-powered systems perpetually scrutinise battery sensor information, identify deterioration trends, forecast malfunctions, and propose ideal charging and upkeep methodologies. This chapter elucidates the principles, frameworks, artificial intelligence methodologies, applications, obstacles, and prospective research trajectories for the prediction of battery health and remaining useful life in AI-driven electric vehicles.