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Advila Lakkidapu is associated with the Department of Electrical and Electronics Engineering at Avanthi Institute of Engineering and Technology, Visakhapatnam, Andhra Pradesh, India. Her academic interests include Artificial Intelligence, electric vehicles, smart energy systems, machine learning, and intelligent power management. She actively participates in research involving sustainable energy technologies and AI-enabled engineering applications.
The growing prevalence of Electric Vehicles (EVs) has intensified the demand for smart charging infrastructure that can reduce charging duration, optimise battery efficiency, and augment user convenience. Precise forecasting of electric vehicle charging duration is crucial for enhancing charging station efficiency, minimising wait times, advancing energy management, and facilitating smart grid functionality. Traditional charge estimating techniques frequently overlook dynamic elements such battery State of Charge (SOC), battery condition, charging power, environmental temperature, charging station accessibility, driving patterns, and traffic circumstances. Machine Learning (ML), Artificial Intelligence (AI), Internet of Things (IoT), Cloud Computing, Edge Computing, and Explainable Artificial Intelligence (XAI) offer sophisticated methods for accurately forecasting charging times. Through the ongoing examination of both historical and real-time charging information, machine learning models provide astute charging scheduling, route optimisation, battery enhancement, and energy-efficient transit. This chapter elucidates the principles, framework, machine learning methodologies, applications, obstacles, and prospective research avenues related to the prediction of electric car charging durations using machine learning.