提出快速启发式算法，解决电动车多初始电量下的节能路径规划问题。
A Fast Heuristic Search Approach for Energy-Optimal Profile Routing for Electric Vehicles
- 基于多目标A*搜索，设计新规则避免复杂能量状态组合。
- 实测性能接近已知电量时最优算法，支持全电量范围路径规划。
- 适合城市交通、长途驾驶等需应对电量不确定场景的电动车应用。
我们研究大规模路网中电动车的能量最优最短路径问题，其中下坡路段可回收能量，带来负能耗。传统方法假设已知初始电量，但现实中电量不确定性要求为所有可能初始电量规划最优路径，即能量最优剖面搜索。现有方法依赖标签修正框架中的特殊剖面合并机制，导致搜索复杂剖面。本文提出一种简单有效的标签设置方法，基于多目标A*搜索，引入新型剖面支配规则，避免生成和处理复杂剖面。我们开发了四种该方法的变体，并在包含真实能耗数据的现实路网中进行评估。实验表明，我们的能量剖面A*搜索性能与已知初始电量时的能量最优A*相当。
原文摘要 · Abstract (English)
We study the energy-optimal shortest path problem for electric vehicles (EVs) in large-scale road networks, where recuperated energy along downhill segments introduces negative energy costs. While traditional point-to-point pathfinding algorithms for EVs assume a known initial energy level, many real-world scenarios involving uncertainty in available energy require planning optimal paths for all possible initial energy levels, a task known as energy-optimal profile search. Existing solutions typically rely on specialized profile-merging procedures within a label-correcting framework that results in searching over complex profiles. In this paper, we propose a simple yet effective label-setting approach based on multi-objective A* search, which employs a novel profile dominance rule to avoid generating and handling complex profiles. We develop four variants of our method and evaluate them on real-world road networks enriched with realistic energy consumption data. Experimental results demonstrate that our energy profile A* search achieves performance comparable to energy-optimal A* with a known initial energy level.
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