Planning, Scheduling, and Behavior in EV Charging Systems: A Critical Survey and Trilemma Framework
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866910242595405824 |
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| author | Xiao, Peiyan Li, Yuheng Mukhopadhyay, Ayan Ghanta, Sai Krishna Baidya, Sabur Xiong, Yanhai |
| author_facet | Xiao, Peiyan Li, Yuheng Mukhopadhyay, Ayan Ghanta, Sai Krishna Baidya, Sabur Xiong, Yanhai |
| contents | The rapid growth of electric vehicles is shifting the main constraint on transport electrification from vehicle adoption to the deployment and operation of charging infrastructure. Charging-network design requires decisions across three interdependent layers: Planning, which determines where and how much infrastructure to build; Scheduling, which governs charging dispatch, pricing, and grid interaction; and Behavior, which captures how users choose stations, charging times, and charging durations. Existing studies have advanced each layer substantially, but the literature remains fragmented, and cross-layer interactions are often treated through simplifying assumptions. This survey develops a three-layer Planning-Scheduling-Behavior (PSB) framework to organize EV charging research according to decision horizon, actor objective, and coupling structure. We further identify a fidelity-tractability tradeoff, termed the PSB trilemma: each layer is computationally difficult in isolation, and realistic integration across layers generally requires reducing the fidelity of at least one layer. Reviewing the three pairwise-coupling literatures - Planning-Scheduling, Scheduling-Behavior, and Planning-Behavior - we show that the omitted third layer is typically fixed exogenously or represented by a static aggregate surrogate. These simplifications enable tractability but impose distinct costs: they can obscure long-term investment feedback, temporal grid and emissions dynamics, or heterogeneous user response and equity outcomes. Building on this diagnosis, we identify open challenges in emerging charging technologies, behavioral incentives, equity metrics, and city-scale learning-based methods that balance fidelity, interpretability, and policy relevance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_21665 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Planning, Scheduling, and Behavior in EV Charging Systems: A Critical Survey and Trilemma Framework Xiao, Peiyan Li, Yuheng Mukhopadhyay, Ayan Ghanta, Sai Krishna Baidya, Sabur Xiong, Yanhai Multiagent Systems Artificial Intelligence The rapid growth of electric vehicles is shifting the main constraint on transport electrification from vehicle adoption to the deployment and operation of charging infrastructure. Charging-network design requires decisions across three interdependent layers: Planning, which determines where and how much infrastructure to build; Scheduling, which governs charging dispatch, pricing, and grid interaction; and Behavior, which captures how users choose stations, charging times, and charging durations. Existing studies have advanced each layer substantially, but the literature remains fragmented, and cross-layer interactions are often treated through simplifying assumptions. This survey develops a three-layer Planning-Scheduling-Behavior (PSB) framework to organize EV charging research according to decision horizon, actor objective, and coupling structure. We further identify a fidelity-tractability tradeoff, termed the PSB trilemma: each layer is computationally difficult in isolation, and realistic integration across layers generally requires reducing the fidelity of at least one layer. Reviewing the three pairwise-coupling literatures - Planning-Scheduling, Scheduling-Behavior, and Planning-Behavior - we show that the omitted third layer is typically fixed exogenously or represented by a static aggregate surrogate. These simplifications enable tractability but impose distinct costs: they can obscure long-term investment feedback, temporal grid and emissions dynamics, or heterogeneous user response and equity outcomes. Building on this diagnosis, we identify open challenges in emerging charging technologies, behavioral incentives, equity metrics, and city-scale learning-based methods that balance fidelity, interpretability, and policy relevance. |
| title | Planning, Scheduling, and Behavior in EV Charging Systems: A Critical Survey and Trilemma Framework |
| topic | Multiagent Systems Artificial Intelligence |
| url | https://arxiv.org/abs/2605.21665 |