Smart Charging of Electric Vehicles

The global transition from conventional vehicles to electric mobility is accelerating, driven by environmental concerns, regulatory frameworks, and advances in energy infrastructure. As electric vehicles (EVs) become increasingly embedded within smart grid, intelligent energy management systems are required to address variable demand, battery aging, charging variability, and grid stability. Artificial Intelligence (AI) has become a key enabler by providing efficiency and reliability. This chapter reviews AI-driven strategies for battery charging and bidirectional energy exchange, focusing on Computational Intelligence (CI) and Machine Learning (ML). Their applications in state estimation, charging control, optimization, anomaly detection, and smart grid coordination are analyzed. Particular attention is given to V2G and G2V architectures and the critical role of Human-Machine Interaction (HMI), which translates AI-generated insights into understandable, user centered charging decisions. By comparing CI and ML approaches and identifying open challenges, this chapter offers a structured perspective on intelligent and HMI integrated EV charging systems.