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

Arindam Khanda, Anurag Satpathy, Anusha Vangala, Sajal K. Das

TL;DR

SMEVCA addresses the challenge of assigning EVs to charging points under limited infrastructure and stochastic demand by formulating the problem as a stable one-to-many matching with SLA-driven subscriptions. It introduces two coalition strategies, PCG (greedy) and PCD (dynamic programming), to form preferred coalitions at CPs while ensuring SLA compliance, prioritizing in-network charging to reduce vendor costs. Empirical results show that PCG and PCD achieve 14.6% and 20.8% gains over random allocation in terms of in-network charging, with 3% and 0.1% of EVs unserved, respectively, and PCD delivering higher in-network transfer at the cost of greater computation. The framework offers a scalable, vendor-centric approach that can improve charging utilization and SLA adherence in subscription-based EV charging networks, with future work extending to dynamic arrivals, traffic effects, and time-slot preferences.

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.

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

TL;DR

SMEVCA addresses the challenge of assigning EVs to charging points under limited infrastructure and stochastic demand by formulating the problem as a stable one-to-many matching with SLA-driven subscriptions. It introduces two coalition strategies, PCG (greedy) and PCD (dynamic programming), to form preferred coalitions at CPs while ensuring SLA compliance, prioritizing in-network charging to reduce vendor costs. Empirical results show that PCG and PCD achieve 14.6% and 20.8% gains over random allocation in terms of in-network charging, with 3% and 0.1% of EVs unserved, respectively, and PCD delivering higher in-network transfer at the cost of greater computation. The framework offers a scalable, vendor-centric approach that can improve charging utilization and SLA adherence in subscription-based EV charging networks, with future work extending to dynamic arrivals, traffic effects, and time-slot preferences.

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.

Paper Structure

This paper contains 22 sections, 2 theorems, 8 equations, 13 figures, 1 table, 6 algorithms.

Table of Contents

  1. Introduction
  2. Research Challenges
  3. Our Contributions
  4. Related Work
  5. System Model and Assumptions
  6. Subscription Model
  7. Charging Infrastructure and Cost Model
  8. Assumptions
  9. Problem Formulation
  10. SLA-Compliant EV-CP Assignment
  11. Solution Approach
  12. EV to CP Assignment as a Matching Game
  13. Working of the Matching Algorithm
  14. Preferences of EVs
  15. Matching Algorithm
  16. ...and 7 more sections

Key Result

Theorem 1

SLA-compliant EV-CP assignment is $\mathcal{NP}$-Hard.

Figures (13)

  • Figure 1: The Overall Architecture of SMEVCA.
  • Figure 2: EV Assignments. $q_j^{\theta, \eta} = 2$
  • Figure 3: Total Charge Transferred. $q_j^{\theta, \eta} = 2$
  • Figure 4: In Network Charge Transferred. $q_j^{\theta, \eta} = 2$
  • Figure 6: Execution Time (s).
  • ...and 8 more figures

Theorems & Definitions (6)

  • Theorem 1
  • Definition 1
  • Definition 2
  • Definition 3
  • Definition 4
  • Theorem 2