Published Paper Details:

Electric vehicle batteries are complex electrochemical systems whose performance, safety, and lifespan strongly depend on accurate state estimation and effective health monitoring. Conventional battery management systems rely on rule-based and model-driven approaches that often struggle with nonlinear battery behavior, aging effects, and dynamic operating conditions. Recent advancements in Artificial Intelligence and Internet of Things technologies have enabled a new generation of intelligent and connected battery management systems for electric vehicles. This survey presents a comprehensive analysis of AI- and IoT-based techniques applied to battery state estimation and health monitoring, including state-of-charge, state-of-health, and remaining useful life prediction. The reviewed literature is systematically classified based on employed AI models, monitored battery states, and IoT deployment architectures such as edge, cloud, and hybrid systems. Comparative insights highlight the strengths, limitations, and practical feasibility of existing approaches under real-world conditions. Key challenges related to data availability, model generalization, real-time implementation, and system security are critically discussed, and promising future research directions are outlined to support the development of reliable, scalable, and intelligent battery management systems for next-generation electric vehicles.