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

2025-12-01

AI Summary

This work addresses energy-optimal routing for electric vehicles (EVs) in large-scale road networks under initial-state energy uncertainty—specifically, the “energy-optimal contour search,” which simultaneously computes optimal paths for all feasible initial battery states. Traditional algorithms struggle with negative edge costs induced by regenerative braking on downhill segments and the high-dimensional, non-convex nature of energy-consumption contours. Method: We propose a multi-objective A*-based label-setting algorithm featuring a novel contour-dominance relation that avoids explicit construction or manipulation of high-dimensional energy contours. The approach integrates heuristic pruning, four optimization variants, and realistic energy-consumption modeling. Contribution/Results: Experiments on real-world road networks show that our method achieves path quality comparable to standard A* with known initial energy, while accelerating contour search by orders of magnitude. It provides a scalable, real-time solution for routing under uncertain energy constraints.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Heuristic Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Optimizes EV routes for all initial energy levels
• Addresses uncertainty in electric vehicle energy availability
• Simplifies complex energy profile search in road networks

Innovation

• Multi-objective A* search for energy profiles
• Novel profile dominance rule to simplify handling
• Four variants evaluated on real-world road networks