The rapid adoption of electric vehicles (EVs) has created a strong need for intelligent routing systems that address limitations such as limited driving range, limited charging infrastructure availability, and variable energy consumption. Traditional navigation methods, which focus solely on shortest-path computation, are insufficient for EV applications because they do not account for battery constraints and charging requirements. This survey paper provides a comprehensive review of battery-aware route planning and charging decision strategies for electric vehicles. It examines classical graph-based algorithms such as Dijkstra and A*, optimization techniques including genetic algorithms and swarm-based methods, and emerging approaches based on machine learning and reinforcement learning. The paper also discusses charging infrastructure considerations, including station placement, charging time, and cost optimization. A comparative analysis of existing methods is presented to highlight their strengths, limitations, and suitability for EV routing. Finally, future research directions are outlined, emphasizing the role of intelligent systems, smart grids, and AI-driven decision-making in advancing EV navigation. This survey aims to provide a structured understanding of current developments and guide future research in efficient and sustainable EV route planning.
Electric Vehicles (EV), Energy Efficiency, Intelligent Transportation Systems (ITS).
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