A Fast Heuristic Search Approach for Energy-Optimal Profile Routing for Electric Vehicles
This addresses routing efficiency for electric vehicles in scenarios with energy uncertainty, representing an incremental improvement over existing methods.
The paper tackled the energy-optimal profile routing problem for electric vehicles under uncertain initial energy levels by proposing a multi-objective A* search with a novel profile dominance rule, achieving performance comparable to energy-optimal A* with known initial energy in real-world road networks.
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.