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S. Mounika is an Assistant Professor in the Department of Computer Science and Engineering at Velalar College of Engineering and Technology, Erode, Tamil Nadu, India. She is involved in teaching and academic activities in computer science and engineering. Her contribution explores multimodal AI techniques for electric vehicle energy analysis and intelligent transportation optimization.
The growing prevalence of EVs has generated novel prospects for eco-friendly transportation, diminished greenhouse gas emissions, and enhanced energy efficiency. Nevertheless, the effective functioning of electric vehicles is affected by multiple interrelated elements, such as driving habits, traffic density, road inclines, environmental temperature, battery status, vehicle weight, and the accessibility of charging infrastructure. ML, deep learning, temporal forecasting, graph-based optimisation, and reinforcement learning can leverage these diverse data sources to predict energy usage and determine energy-efficient pathways amongst fluctuating operational circumstances. This chapter explores the principles of predicting energy usage in electric vehicles, integrating multimodal data, modelling with battery considerations, optimising dynamic routes, selecting charging stations, and making real-time decisions. The chapter establishes a theoretical basis for intelligent electric vehicle navigation systems aimed at enhancing energy efficiency, trip dependability, and sustainable transport strategies.