The rapid adoption of electric vehicles (EVs) in modern transport systems has made energy-aware routing a critical task in their successful integration, especially within large-scale networks. In cases where an EV's remaining energy is limited and charging locations are not easily accessible, some destinations may only be reachable through an energy-optimal path: a route that consumes less energy than all other alternatives. The feasibility of such energy-efficient paths depends heavily on the accuracy of the energy model used for planning, and thus failing to account for vehicle dynamics can lead to inaccurate energy estimates, rendering some planned routes infeasible in reality. This paper explores the impact of vehicle dynamics on energy-optimal path planning for EVs. We develop an accurate energy model that incorporates key vehicle dynamics parameters into energy calculations, thereby reducing the risk of planning infeasible paths under battery constraints. The paper also introduces two novel online reweighting functions that allow for a faster, pre-processing free, pathfinding in the presence of negative energy costs resulting from regenerative braking, making them ideal for real-time applications. Through extensive experimentation on real-world transport networks, we demonstrate that our approach considerably enhances energy-optimal pathfinding for EVs in both computational efficiency and energy estimation accuracy.

翻译：随着电动汽车在现代交通系统中的快速普及，能量感知路由已成为其成功集成的关键任务，尤其是在大规模网络中。当电动汽车的剩余能量有限且充电站不易到达时，某些目的地可能只能通过能量最优路径抵达：即一条比所有其他替代路线消耗更少能量的路径。此类节能路径的可行性在很大程度上取决于规划所用能量模型的准确性，因此若未能考虑车辆动力学因素，可能导致能量估计不准确，从而使某些规划路线在实际中不可行。本文探讨了车辆动力学对电动汽车能量最优路径规划的影响。我们开发了一个精确的能量模型，将关键的车辆动力学参数纳入能量计算，从而降低了在电池约束下规划不可行路径的风险。本文还引入了两种新颖的在线重加权函数，能够在再生制动产生负能量成本的情况下实现更快速、无需预处理的路径搜索，使其非常适合实时应用。通过对真实交通网络的大量实验，我们证明该方法在计算效率和能量估计准确性方面显著提升了电动汽车的能量最优路径规划能力。