SMEVCA: Stable Matching-based EV Charging Assignment in Subscription-Based Models

TL;DR

This paper introduces SMEVCA, a stable matching-based framework for EV charging assignment in subscription models, optimizing allocation with two strategies and demonstrating significant improvements in charge transfer efficiency.

Contribution

The paper presents a novel stable matching framework for EV charging assignment that ensures SLA compliance and improves efficiency over random methods.

Findings

PCG achieves 14.6% gain over random elimination.

PCD achieves 20.8% gain over random elimination.

Both strategies significantly reduce unserved EVs.

Abstract

The rapid shift from internal combustion engine vehicles to battery-powered electric vehicles (EVs) presents considerable challenges, such as limited charging points (CPs), unpredictable wait times, and difficulty selecting appropriate CPs. To address these challenges, we propose a novel end-to-end framework called Stable Matching EV Charging Assignment (SMEVCA) that efficiently assigns charge-seeking EVs to CPs with assistance from roadside units (RSUs). The proposed framework operates within a subscription-based model, ensuring that the subscribed EVs complete their charging within a predefined time limit enforced by a service level agreement (SLA). The framework SMEVCA employs a stable, fast, and efficient EV-CP assignment formulated as a one-to-many matching game with preferences. The matching process identifies the preferred coalition (a subset of EVs assigned to the CPs) using two strategies: (1) Preferred Coalition Greedy (PCG) that offers an efficient, locally optimal heuristic solution and (2) Preferred Coalition Dynamic (PCD) that is more computation-intensive but delivers a globally optimal coalition. Extensive simulations reveal that PCG and PCD achieve a gain of 14.6% and 20.8% over random elimination for in-network charge transferred with only 3% and 0.1% EVs unserved within the RSUs vicinity.