Abstract

The transition from internal combustion engine (ICE) to electric vehicles (EVs) introduces several challenges, including limited charging infrastructure, unpredictable charging wait times, and inefficient selection of charging points (CPs). To address these issues, we propose SMART-CHARGE, a framework that efficiently assigns EVs to CPs through an edge-level coordination mechanism within each service region, enforced by roadside units (RSUs). Operating under a novel subscription-based charging model, SMART-CHARGE enforces predefined charging time limits via service-level agreements (SLAs). The EV-CP assignment problem is formulated as a one-to-many matching game that captures EV user preferences. To construct bounded yet efficient EV coalitions at each CP, we introduce three strategies: Preferred Coalition Greedy (PCG) for computational efficiency, Preferred Coalition Dynamic (PCD) for globally optimal coalition formation, and Preferred Coalition Local (PCL), a local search-based method designed to handle arbitrary EV arrival sequences. The resulting assignment is formulated as an optimization problem that incorporates CP capacity, battery constraints, SLAs, and spatially varying charging costs. We establish stability guarantees, analyze computational intractability, and derive asymptotic bounds for the proposed solution. Extensive evaluations using real-world charging datasets compare coalition strategies under variable pricing and SLA constraints. Results show that SMART-CHARGE achieves a polynomial-time solution that improves resource allocation and bounds EV waiting times, delivering at least a 39% improvement over state-of-the-art methods in the overall objective that jointly optimizes charging cost and detour distance.

Department(s)

Computer Science

Publication Status

Full Text Access

Comments

National Science Foundation, Grant OAC-2104078

Keywords and Phrases

Charge point assignment; Dynamic programming; EVs; Greedy; Matching theory; Subscription model

International Standard Serial Number (ISSN)

1574-1192

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2026 Elsevier, All rights reserved.

Publication Date

01 May 2026

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