IRI - Large-scale EV charging coordination: a detailed exploration of mean-field and reinforcement learning approaches

Publication

Large-scale EV charging coordination: a detailed exploration of mean-field and reinforcement learning approaches

Journal Article (2026)

Journal

Control Engineering Practice

Pages

106669

Volume

168

Doc link

https://doi.org/10.1016/j.conengprac.2025.106669

File

Download the digital copy of the doc pdf document

Authors

Caballé, Josep
Segovia Castillo, Pau
Ocampo Martínez, Carlos A.
Quijano Silva, Nicanor

Abstract

The rapid proliferation of electric vehicles (EVs) presents significant challenges to power-grid stability, especially when thousands of cars charge simultaneously without coordination. This paper investigates two complementary families of scalable control schemes-(i) Mean-Field Control (Mean-Field Games and Mean-Field-Type Games), and (ii) model-free Reinforcement Learning (classical Reinforcement Learning and Deep Reinforcement Learning)-that capture stochastic arrivals, nodal capacity limits, ambient-temperature effects and battery degradation. Analytical mean-field-based formulations yield decentralized charging policies that depend only on population statistics and enjoy ϵ-Nash optimality as fleet size grows, while reinforcement-learning-based agents learn directly from interaction histories and cope naturally with partial observability and non-stationary price signals. A common set of nine key performance indicators is applied to two benchmark scenarios: a one-night, three-node grid and a seven-day, heterogeneous 1 000-EV testbed built on real distribution-network data. Results show that Mean-Field-Type Games minimizes unmet energy ( ≥ 1 %) and queueing delays, Deep Reinforcement Learning maximizes average final State-of-Charge ( ≈ 81 %) under volatile tariffs, and classical Reinforcement Learning provides the most interpretable albeit least efficient baseline. These quantified trade-offs clarify when model-based equilibrium methods suffice and when adaptive, data-driven controllers become indispensable, providing actionable guidance for large-scale, battery-health-aware EV-charging deployments.

Author keywords

Electric vehicles; Charging coordination; Mean-Field Games; Mean-Field-Type Games; Reinforcement learning; Deep reinforcement learning

Scientific reference

J. Caballé, P. Segovia, C. Ocampo-Martínez and N. Quijano. Large-scale EV charging coordination: a detailed exploration of mean-field and reinforcement learning approaches. Control Engineering Practice, 168: 106669, 2026.