Resilient Charging Infrastructure via Decentralized Coordination of Electric Vehicles at Scale
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911287444766720 |
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| author | Qin, Chuhao Sorici, Alexandru Olaru, Andrei Pournaras, Evangelos Florea, Adina Magda |
| author_facet | Qin, Chuhao Sorici, Alexandru Olaru, Andrei Pournaras, Evangelos Florea, Adina Magda |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_20943 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Resilient Charging Infrastructure via Decentralized Coordination of Electric Vehicles at Scale Qin, Chuhao Sorici, Alexandru Olaru, Andrei Pournaras, Evangelos Florea, Adina Magda Multiagent Systems Artificial Intelligence 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. |
| title | Resilient Charging Infrastructure via Decentralized Coordination of Electric Vehicles at Scale |
| topic | Multiagent Systems Artificial Intelligence |
| url | https://arxiv.org/abs/2511.20943 |