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

TL;DR

This paper introduces a fast, heuristic multi-objective A* search method for energy-optimal routing of electric vehicles, capable of efficiently handling uncertainty in initial energy levels without complex profile merging.

Contribution

It presents a novel label-setting approach with a new profile dominance rule, simplifying energy profile search in large-scale road networks for EVs.

Findings

Achieves performance comparable to traditional methods with known initial energy.

Effectively handles uncertainty in initial energy levels.

Demonstrates efficiency on real-world road networks.

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.