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Mubashir Alam is an Assistant Professor at the Amity School of Business, Amity University Patna, Bihar, India. His academic interests focus on Artificial Intelligence, business analytics, digital technologies, intelligent decision-making, and emerging management information systems. He is actively engaged in teaching, research, and interdisciplinary studies that bridge business management with advanced computational technologies.
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.