A Fast Heuristic Search Approach for Energy-Optimal Profile Routing for Electric Vehicles

• URL: http://arxiv.org/abs/2512.01331v1
• Date: Mon, 01 Dec 2025 06:45:34 GMT
• Title: A Fast Heuristic Search Approach for Energy-Optimal Profile Routing for Electric Vehicles
• Authors: Saman Ahmadi, Mahdi Jalili,
• Abstract summary: 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.<n>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.<n>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.
• Score: 12.388453051148678
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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