An Electric Vehicle Optimal Charging Path Planning based on an Improved A* Algorithm

Abstract

This study aims to explore the optimal charging path planning for electric vehicles (EVs) based on an improved A* algorithm. During the path planning process, there are several issues such as excessive traversal of invalid nodes leading to low path search efficiency, an excessively fast search rate causing the algorithm to converge prematurely to a non-optimal path, or even being unable to find an effective path to the target, and the inability to balance the weights of exploring new paths and utilizing known information, which can lead the algorithm to fall into a local optimal solution or converge prematurely, thus failing to find the global optimal path. To address these issues, this study proposes the application of optimization goal-directed strategies, linear adaptation strategies, and exponential adaptation strategies. By integrating these strategies, the efficiency of the algorithm in searching for the optimal path is improved, enabling EVs to reach charging stations more quickly. The optimization goal-directed strategy adjusts the calculation method of the estimated cost in the heuristic function by introducing a square term, making the algorithm more inclined to choose nodes closer to the target direction when evaluating nodes. This enhances the directionality of the algorithm during the search process. The linear adaptation strategy dynamically adjusts the weight of the actual cost, guiding the algorithm to gradually slow down the search speed as it approaches the target point. This is more conducive to the algorithm's adaptation to the target location and improves the efficiency of path search. The exponential adaptation strategy dynamically adjusts the weight of the actual cost based on the progress of the path search, balancing the algorithm's choice between searching for new paths and utilizing known optimal paths, further improving search efficiency. This study experimentally verifies the effectiveness of these three strategies. The results show that by introducing these strategies, the search efficiency of the A* algorithm in the charging path planning of new energy vehicles has been significantly improved. These strategies contribute to the optimization of EV charging path planning and help promote the development of electric vehicles and intelligent transportation.