Dynamic Energy-Efficient Path Planning for Electric Vehicles Using an Enhanced Ant Colony Algorithm

: Electric vehicles (EVs) energy efficient path planning is crucial for maximizing the range of EVs. However, existing path planning algorithms often prioritize least time or shortest path without considering energy efficiency, leading to issues such as long computation time, slow convergence, and suboptimal solutions in complex environments. To address these challenges, this study proposes an improved ant colony optimization (E-ACO) algorithm for dynamic energy efficient path planning of EVs. The E-ACO algorithm incorporates a traffic flow prediction model and an energy consumption model specific to EVs. By redesigning heuristic factors and state transition rules, the algorithm enhances the efficiency and accuracy of path planning. Moreover, to address the challenge of selecting optimal charging station locations based on existing battery levels, a charging path planning method is introduced. This method utilizes the E-ACO algorithm and employs charging station pre-screening strategies to identify the most suitable charging station for completing the charging process. Experimental results show that the E-ACO algorithm reduces energy consumption by approximately 7% compared to the traditional ant colony optimization (ACO) algorithm. Additionally, through data analysis, a pre-screening threshold of 10 charging stations is determined based on the relationship between distance and energy consumption. To provide a visual representation of the path planning results, software is used to display the optimized paths. This allows users to easily interpret and analyze the recommended routes. Overall, the proposed E-ACO algorithm offers an effective and efficient solution for energy-efficient path planning in EVs. The incorporation of charging station pre-screening strategies further enhances the charging process. The study's findings contribute to the development of more sustainable and efficient EV routing strategies, benefiting both EV users and the environment.