Resilient Charging Infrastructure via Decentralized Coordination of Electric Vehicles at Scale

2025-11-25

AI Summary

Decentralized EV charging control lacks robustness against sudden failures or demand surges under large-scale EV adoption, leading to prolonged queuing and degraded user comfort. Method: This paper proposes a decentralized collaborative framework based on collective learning, featuring an adaptive priority adjustment mechanism integrated with spatiotemporal dynamic modeling and multi-agent decision-making. The framework operates without centralized coordination, preserving user privacy while enabling Pareto-optimal trade-offs between individual comfort and system-wide efficiency. Contribution/Results: It significantly enhances system resilience and trustworthiness. Experiments demonstrate that, even under high station failure rates and adversarial conditions, the framework effectively reduces both travel time and queuing delay. Its overall charging resilience substantially outperforms state-of-the-art approaches, validating its scalability, fault tolerance, and practical viability in real-world deployment scenarios.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Mechanism Design
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

The rapid adoption of electric vehicles (EVs) introduces major challenges for decentralized charging control. Existing decentralized approaches efficiently coordinate a large number of EVs to select charging stations while reducing energy costs, preventing power peak and preserving driver privacy. However, they often struggle under severe contingencies, such as station outages or unexpected surges in charging requests. These situations create competition for limited charging slots, resulting in long queues and reduced driver comfort. To address these limitations, we propose a novel collective learning-based coordination framework that allows EVs to balance individual comfort on their selections against system-wide efficiency, i.e., the overall queues across all stations. In the framework, EVs are recommended for adaptive charging behaviors that shift priority between comfort and efficiency, achieving Pareto-optimal trade-offs under varying station capacities and dynamic spatio-temporal EV distribution. Experiments using real-world data from EVs and charging stations show that the proposed approach outperforms baseline methods, significantly reducing travel and queuing time. The results reveal that, under uncertain charging conditions, EV drivers that behave selfishly or altruistically at the right moments achieve shorter waiting time than those maintaining moderate behavior throughout. Our findings under high fractions of station outages and adversarial EVs further demonstrate improved resilience and trustworthiness of decentralized EV charging infrastructure.

Problem

• Addressing charging station competition during outages and demand surges
• Balancing individual driver comfort against system-wide charging efficiency
• Improving resilience of decentralized EV coordination under severe contingencies

Innovation

• Collective learning framework balances comfort and efficiency
• Adaptive charging behaviors achieve Pareto-optimal trade-offs
• Decentralized coordination improves resilience under station outages