Electric Vehicle Charging Route Planning for Shortest Travel Time Based on Improved Ant Colony Optimization

Electric vehicles (EVs) are gaining significant attention as an environmentally friendly transportation solution. However, limitations in battery technology continue to restrict EV range and charging speed, resulting in range anxiety, which hampers widespread adoption. While there has been increasing research on EV route optimization, personalized path planning that caters to individual user needs remains underexplored. To bridge this gap, we propose the electric vehicle charging route planning based on user requirements (EVCRP-UR) problem, which aims to integrate user preferences and multiple constraints. Our approach utilizes topology optimization to reduce computational complexity and improve path planning efficiency. Furthermore, we introduce an improved ant colony optimization (IACO) algorithm incorporating novel heuristic functions and refined probability distribution models to select optimal paths and charging stations. To further enhance charging strategies, we develop a discrete electricity dynamic programming (DE-DP) algorithm to determine charging times at efficiently chosen stations. By combining these methods, the proposed IACO algorithm leverages the strengths of each approach, overcoming their individual limitations and delivering superior performance in EV routing and charging optimization.