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arxiv: 2605.21665 · v1 · pith:D4AZTGLOnew · submitted 2026-05-20 · 💻 cs.MA · cs.AI

Planning, Scheduling, and Behavior in EV Charging Systems: A Critical Survey and Trilemma Framework

Peiyan Xiao , Yuheng Li , Ayan Mukhopadhyay , Sai Krishna Ghanta , Sabur Baidya , Yanhai Xiong This is my paper

Pith reviewed 2026-05-22 08:20 UTC · model grok-4.3

classification 💻 cs.MA cs.AI
keywords electric vehicle chargingplanningschedulinguser behaviortrilemmasurveyinfrastructuregrid integration
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The pith

EV charging systems face a trilemma where realistic integration of planning, scheduling, and behavior generally requires reducing fidelity in at least one layer.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This survey organizes EV charging research into three interdependent layers: planning of where and how much infrastructure to build, scheduling of charging dispatch and pricing, and behavior capturing how users select stations and durations. It argues that substantial progress exists within each layer yet cross-layer work relies on simplifying assumptions that hold the omitted layer fixed or replace it with a static aggregate. These choices enable tractability but leave out long-term investment feedbacks, temporal grid and emissions effects, and heterogeneous user responses including equity outcomes. The paper identifies the resulting fidelity-tractability tradeoff as the PSB trilemma and flags open challenges in new charging technologies, behavioral incentives, equity metrics, and city-scale learning methods. A reader would care because better coupled models matter for designing networks that support rising EV adoption without excessive grid strain or unequal access.

Core claim

The authors introduce a Planning-Scheduling-Behavior (PSB) framework that classifies studies by decision horizon, actor objective, and coupling structure. They show that each layer remains computationally difficult alone and that realistic integration across layers generally requires reducing the fidelity of at least one layer. Review of the three pairwise literatures reveals that the missing layer is typically fixed exogenously or represented by a static aggregate surrogate, which supports analysis but obscures investment feedbacks, dynamic grid and emissions behavior, or heterogeneous user responses and equity considerations.

What carries the argument

The PSB trilemma, a fidelity-tractability tradeoff in which realistic cross-layer integration of planning, scheduling, and behavior requires lowering the detail or accuracy of at least one layer.

If this is right

  • Holding behavior fixed as an aggregate misses how pricing and station choice alter grid load and emissions over time.
  • Treating planning decisions as exogenous overlooks feedback from scheduling policies to future infrastructure sizing.
  • Static user surrogates hide equity differences arising from heterogeneous driver responses to incentives and locations.
  • These simplifications limit evaluation of emerging options such as vehicle-to-grid or wireless charging under coupled conditions.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same trilemma pattern may appear in other layered infrastructure problems such as urban mobility networks or distributed energy resources.
  • Learning-based city-scale approaches could be tested by measuring whether they reduce the fidelity cost compared with traditional layered models on shared datasets.
  • Equity-focused policy work would benefit from replacing aggregate behavior models with explicit heterogeneity to capture access disparities.

Load-bearing premise

That the body of EV charging research divides cleanly into the three layers of planning, scheduling, and behavior, with the omitted layer in any pairwise study typically treated as fixed or replaced by a simple static aggregate.

What would settle it

A computational study or deployed system that maintains high-fidelity models of all three PSB layers simultaneously for a realistic city-scale network without prohibitive computation time or loss of predictive accuracy.

Figures

Figures reproduced from arXiv: 2605.21665 by Ayan Mukhopadhyay, Peiyan Xiao, Sabur Baidya, Sai Krishna Ghanta, Yanhai Xiong, Yuheng Li.

Figure 1
Figure 1. Figure 1: Planning–Scheduling–Behavior framework for EV charging systems. The ver [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript surveys EV charging research and organizes it under a three-layer Planning-Scheduling-Behavior (PSB) framework defined by decision horizon, actor objective, and coupling structure. It diagnoses a fidelity-tractability tradeoff (the PSB trilemma) in which each layer is hard in isolation and joint modeling of any two layers typically treats the third as exogenous or via a static aggregate surrogate. The survey reviews the three pairwise-coupling literatures, catalogs the resulting simplifications and their costs (e.g., obscured investment feedback or equity outcomes), and lists open challenges in emerging technologies, behavioral incentives, equity metrics, and city-scale learning methods.

Significance. If the trilemma diagnosis holds, the framework supplies a coherent lens for a fragmented literature and usefully flags the recurring modeling compromises that limit policy relevance. The explicit mapping of costs to each omitted layer and the forward-looking challenges section could help steer future work toward more balanced cross-layer models.

major comments (1)
  1. [Abstract / pairwise-coupling review] Abstract, paragraph on pairwise-coupling literatures: the central claim that 'the omitted third layer is typically fixed exogenously or represented by a static aggregate surrogate' is load-bearing for the trilemma. The manuscript should supply a concise table or count (e.g., 'X of Y papers in the Planning-Scheduling review') with representative citations to demonstrate that this pattern is representative rather than anecdotal.
minor comments (2)
  1. [Abstract] The abstract is clear but would benefit from a brief statement of the literature search scope (time window, databases, keywords) so readers can gauge coverage.
  2. [Open challenges] In the open-challenges section on equity metrics, naming one or two concrete metrics already used in the Behavior layer (e.g., accessibility indices or demographic disparity measures) would make the gap more actionable.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the supportive assessment and the recommendation for minor revision. The single major comment is addressed point by point below.

read point-by-point responses

  1. Referee: [Abstract / pairwise-coupling review] Abstract, paragraph on pairwise-coupling literatures: the central claim that 'the omitted third layer is typically fixed exogenously or represented by a static aggregate surrogate' is load-bearing for the trilemma. The manuscript should supply a concise table or count (e.g., 'X of Y papers in the Planning-Scheduling review') with representative citations to demonstrate that this pattern is representative rather than anecdotal.

    Authors: We agree that the claim is central to the trilemma diagnosis and that explicit quantification would make the pattern more convincing rather than potentially anecdotal. In the revised manuscript we will insert a new summary table (or set of counts) immediately following the abstract paragraph on pairwise couplings. The table will report, for each of the three reviewed literatures, the number of papers that treat the omitted layer as fixed exogenously or via a static aggregate surrogate, together with one or two representative citations per category. This addition will be cross-referenced in the abstract and will preserve the existing narrative while supplying the requested evidence. revision: yes

Circularity Check

0 steps flagged

No significant circularity in the survey's organizational framework

full rationale

This paper is a literature survey that synthesizes existing EV charging research under the newly proposed Planning-Scheduling-Behavior (PSB) framework and diagnoses the fidelity-tractability tradeoff from observed patterns across the pairwise-coupling literatures. No new derivations, equations, or formal proofs are advanced; the central claims follow from the stated review that each layer is difficult in isolation and that the omitted layer is typically fixed exogenously or represented by a static aggregate surrogate. The argument is therefore self-contained as a descriptive classification and organizing lens, with no reduction of results to fitted inputs or self-referential definitions by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The framework rests on the domain assumption that the three layers capture the main decision horizons and actors in EV charging systems; no free parameters or invented physical entities are introduced.

axioms (1)
  • domain assumption The three layers (Planning, Scheduling, Behavior) are interdependent and decisions in one affect the others.
    Stated in the abstract as the basis for the PSB framework.
invented entities (1)
  • PSB trilemma no independent evidence
    purpose: Conceptual device to describe the fidelity-tractability tradeoff across layers.
    Introduced in the abstract as a named tradeoff; no independent empirical test provided.

pith-pipeline@v0.9.0 · 5815 in / 1257 out tokens · 21923 ms · 2026-05-22T08:20:05.376014+00:00 · methodology

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Works this paper leans on

201 extracted references · 201 canonical work pages

  1. [1]

    Jaramillo, S

    P. Jaramillo, S. K. Ribeiro, P. Newman, S. Kobayashi, T. Boulou- chos, D. Bun, J. Katima, D. S. Lee, S. B. Nugroho, R. Slade, A. V. de Carvalho, L. Fulton, Transport, in: P. Shukla, J. Skea, R. Slade, A. A. Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P.Vyas, R.Fradera, M.Belkacemi, A.Hasija, G.Lisboa, S.Luz, J.Mal- ley (Eds.), Climate Chan...

    work page doi:10.1017/9781009157926.012 2022

  2. [2]

    M. S. Koroma, A. Alwosheel, Aligning vehicle electrification with power sector transitions: life cycle insights across diverse grids, 57 npj Sustainable Mobility and Transport (2026).doi:10.1038/ s44333-025-00076-y

    work page 2026

  3. [3]

    URLhttps://www.iea.org/reports/global-ev-outlook-2025

    International Energy Agency, Global EV outlook 2025: Expanding sales in diverse markets, IEA, Paris, licence: CC BY 4.0 (2025). URLhttps://www.iea.org/reports/global-ev-outlook-2025

    work page 2025

  4. [4]

    Wang, C.-C

    Y.-W. Wang, C.-C. Lin, Locating multiple types of recharging stations for battery-powered electric vehicle transport, Transportation Re- search Part E: Logistics and Transportation Review 58 (2013) 76–87. doi:https://doi.org/10.1016/j.tre.2013.07.003. URLhttps://www.sciencedirect.com/science/article/pii/ S1366554513001403

    work page doi:10.1016/j.tre.2013.07.003 2013

  5. [6]

    Church, C

    R. Church, C. ReVelle, The maximal covering location problem, Papers in Regional Science 32 (1) (1974) 101–118.doi:10.1007/BF01942293

    work page doi:10.1007/bf01942293 1974

  6. [7]

    Ghanbari Motlagh, L

    S. Ghanbari Motlagh, L. Li, A review on electric vehicle charg- ing station planning: Infrastructure placement, sizing, upgrades, and uncertainties, Journal of Energy Storage 141 (2026) 119325. 58 doi:https://doi.org/10.1016/j.est.2025.119325. URLhttps://www.sciencedirect.com/science/article/pii/ S2352152X25040381

    work page doi:10.1016/j.est.2025.119325 2026

  7. [8]

    H.-Y. Mak, Y. Rong, M. Shen, Infrastructure planning for electric vehicles with battery swapping, Management Science 59 (03 2012). doi:10.2139/ssrn.2022651

    work page doi:10.2139/ssrn.2022651 2012

  8. [9]

    Zhang, Z

    J. Zhang, Z. Wang, E. J. Miller, D. Cui, P. Liu, Z. Zhang, Z. Sun, Multi-period planning of locations and capacities of public charging stations, Journal of Energy Storage 72 (2023) 108565. doi:https://doi.org/10.1016/j.est.2023.108565. URLhttps://www.sciencedirect.com/science/article/pii/ S2352152X2301962X

    work page doi:10.1016/j.est.2023.108565 2023

  9. [10]

    J. Cui, Y. Cao, B. Wang, J. Wu, Multi-stage adaptive expansion of ev charging stations considering impacts from the transporta- tion network and power grid, Applied Energy 386 (2025) 125544. doi:https://doi.org/10.1016/j.apenergy.2025.125544. URLhttps://www.sciencedirect.com/science/article/pii/ S0306261925002740

    work page doi:10.1016/j.apenergy.2025.125544 2025

  10. [11]

    URLhttp://dx.doi.org/10.1109/TSG.2010.2090910 59

    E.Sortomme, M.A.El-Sharkawi, Optimalchargingstrategiesforunidi- rectionalvehicle-to-grid, IEEETransactionsonSmartGrid2(1)(2011) 131–138.doi:10.1109/TSG.2010.2090910. URLhttp://dx.doi.org/10.1109/TSG.2010.2090910 59

    work page doi:10.1109/tsg.2010.2090910 2011

  11. [12]

    Ghanbari Motlagh, J

    S. Ghanbari Motlagh, J. Oladigbolu, L. Li, A review on elec- tric vehicle charging station operation considering market dy- namics and grid interaction, Applied Energy 392 (2025) 126058. doi:https://doi.org/10.1016/j.apenergy.2025.126058. URLhttps://www.sciencedirect.com/science/article/pii/ S0306261925007883

    work page doi:10.1016/j.apenergy.2025.126058 2025

  12. [13]

    Hermans, S

    B. Hermans, S. Walker, J. Ludlage, L. Özkan, Model predictive control of vehicle charging stations in grid-connected microgrids: An implementation study, Applied Energy 368 (2024) 123210. doi:https://doi.org/10.1016/j.apenergy.2024.123210. URLhttps://www.sciencedirect.com/science/article/pii/ S0306261924005932

    work page doi:10.1016/j.apenergy.2024.123210 2024

  13. [14]

    D. Qiu, Y. Wang, W. Hua, G. Strbac, Reinforcement learning for electric vehicle applications in power systems:a critical review, Renewable and Sustainable Energy Reviews 173 (2023) 113052. doi:https://doi.org/10.1016/j.rser.2022.113052. URLhttps://www.sciencedirect.com/science/article/pii/ S1364032122009339

    work page doi:10.1016/j.rser.2022.113052 2023

  14. [15]

    Zhang, B

    L. Zhang, B. Shaffer, T. Brown, G. Scott Samuelsen, The optimization of dc fast charging deployment in california, Applied Energy 157 (2015) 111–122.doi:https://doi.org/10.1016/j.apenergy.2015.07.057. 60 URLhttps://www.sciencedirect.com/science/article/pii/ S0306261915008971

    work page doi:10.1016/j.apenergy.2015.07.057 2015

  15. [16]

    X. Xi, R. Sioshansi, V. Marano, Simulation–optimization model for location of a public electric vehicle charging infrastructure, Trans- portation Research Part D: Transport and Environment 22 (2013) 60–69.doi:https://doi.org/10.1016/j.trd.2013.02.014. URLhttps://www.sciencedirect.com/science/article/pii/ S1361920913000345

    work page doi:10.1016/j.trd.2013.02.014 2013

  16. [17]

    Huang, K

    Y. Huang, K. M. Kockelman, Electric vehicle charging station loca- tions: Elastic demand, station congestion, and network equilibrium, Transportation Research Part D: Transport and Environment 78 (2020) 102179.doi:https://doi.org/10.1016/j.trd.2019.11.008. URLhttps://www.sciencedirect.com/science/article/pii/ S1361920919306406

    work page doi:10.1016/j.trd.2019.11.008 2020

  17. [18]

    F. He, Y. Yin, S. Lawphongpanich, Network equilibrium mod- els with battery electric vehicles, Transportation Research Part B: Methodological 67 (2014) 306–319.doi:https: //doi.org/10.1016/j.trb.2014.05.010. URLhttps://www.sciencedirect.com/science/article/pii/ S0191261514000915

    work page doi:10.1016/j.trb.2014.05.010 2014

  18. [19]

    Z. Yi, B. Chen, X. C. Liu, R. Wei, J. Chen, Z. Chen, An agent- based modeling approach for public charging demand estimation 61 and charging station location optimization at urban scale, Com- puters, Environment and Urban Systems 101 (2023) 101949. doi:https://doi.org/10.1016/j.compenvurbsys.2023.101949. URLhttps://www.sciencedirect.com/science/article/pi...

    work page doi:10.1016/j.compenvurbsys.2023.101949 2023

  19. [20]

    Y. Yang, S. Yang, H. Moon, J. Woo, Analyzing heteroge- neous electric vehicle charging preferences for strategic time- of-use tariff design and infrastructure development: A la- tent class approach, Applied Energy 374 (2024) 124074. doi:https://doi.org/10.1016/j.apenergy.2024.124074. URLhttps://www.sciencedirect.com/science/article/pii/ S0306261924014570

    work page doi:10.1016/j.apenergy.2024.124074 2024

  20. [21]

    Prakash, M

    K. Prakash, M. Ali, M. N. I. Siddique, A. K. Karmaker, C. A. Macana, D.Dong, H.R.Pota, Bi-levelplanningandschedulingofelectricvehicle charging stations for peak shaving and congestion management in low voltage distribution networks, Computers and Electrical Engineering 102 (2022) 108235.doi:10.1016/j.compeleceng.2022.108235

    work page doi:10.1016/j.compeleceng.2022.108235 2022

  21. [22]

    Tostado-Véliz, A

    M. Tostado-Véliz, A. Rezaee Jordehi, Y. Zhou, S. A. Mansouri, F. Jurado, Best-case-aware planning of photovoltaic-battery systems for multi-mode charging stations, Renewable Energy 225 (2024) 120300.doi:https://doi.org/10.1016/j.renene.2024.120300. 62 URLhttps://www.sciencedirect.com/science/article/pii/ S0960148124003653

    work page doi:10.1016/j.renene.2024.120300 2024

  22. [23]

    Zheng, Y

    Y. Zheng, Y. Chen, Q. Yang, A stackelberg-based competition model for optimal participation of electric vehicle load aggrega- tors in demand response programs, Energy 315 (2025) 134414. doi:https://doi.org/10.1016/j.energy.2025.134414. URLhttps://www.sciencedirect.com/science/article/pii/ S0360544225000568

    work page doi:10.1016/j.energy.2025.134414 2025

  23. [24]

    Cheng, G

    R. Cheng, G. Liu, W. Liu, Q. Shi, Q. Chen, A personalized network-aware price design for electric vehicle charging using a sequential stackelberg game, Applied Energy 381 (2025) 125145. doi:https://doi.org/10.1016/j.apenergy.2024.125145. URLhttps://www.sciencedirect.com/science/article/pii/ S0306261924025297

    work page doi:10.1016/j.apenergy.2024.125145 2025

  24. [25]

    S. Bae, B. Kulcsár, S. Gros, Personalized dynamic pricing policy for electric vehicles: Reinforcement learning approach, Transporta- tion Research Part C: Emerging Technologies 161 (2024) 104540. doi:https://doi.org/10.1016/j.trc.2024.104540. URLhttps://www.sciencedirect.com/science/article/pii/ S0968090X24000615

    work page doi:10.1016/j.trc.2024.104540 2024

  25. [26]

    D. Qiao, G. Wang, M. Xu, Fast-charging station location problem: A two-phase approach with mathematical program 63 with equilibrium constraints considering charging choice be- haviour, Sustainable Cities and Society 96 (2023) 104678. doi:https://doi.org/10.1016/j.scs.2023.104678. URLhttps://www.sciencedirect.com/science/article/pii/ S2210670723002895

    work page doi:10.1016/j.scs.2023.104678 2023

  26. [27]

    Unterluggauer, J

    T. Unterluggauer, J. Rich, P. B. Andersen, S. Hashemi, Electric vehicle charging infrastructure planning for integrated transportation and power distribution networks: A review, eTransportation 12 (2022) 100163.doi:https://doi.org/10.1016/j.etran.2022.100163. URLhttps://www.sciencedirect.com/science/article/pii/ S2590116822000091

    work page doi:10.1016/j.etran.2022.100163 2022

  27. [28]

    Sadeghian, A

    O. Sadeghian, A. Oshnoei, B. Mohammadi-ivatloo, V. Vahidi- nasab, A. Anvari-Moghaddam, A comprehensive review on elec- tric vehicles smart charging: Solutions, strategies, technologies, and challenges, Journal of Energy Storage 54 (2022) 105241. doi:https://doi.org/10.1016/j.est.2022.105241. URLhttps://www.sciencedirect.com/science/article/pii/ S2352152X22012403

    work page doi:10.1016/j.est.2022.105241 2022

  28. [29]

    Dolgui, S

    A. Dolgui, S. Kovalev, M. Y. Kovalyov, Scheduling electric vehicle regular charging tasks: A review of deterministic models, Euro- pean Journal of Operational Research 325 (2) (2025) 221–232. doi:https://doi.org/10.1016/j.ejor.2024.11.044. 64 URLhttps://www.sciencedirect.com/science/article/pii/ S0377221724009354

    work page doi:10.1016/j.ejor.2024.11.044 2025

  29. [30]

    LaMonaca, L

    S. LaMonaca, L. Ryan, The state of play in electric vehicle charging services – a review of infrastructure provision, players, and policies, Renewable and Sustainable Energy Reviews 154 (2022) 111733. doi:https://doi.org/10.1016/j.rser.2021.111733. URLhttps://www.sciencedirect.com/science/article/pii/ S1364032121010066

    work page doi:10.1016/j.rser.2021.111733 2022

  30. [31]

    H. Liu, D. Z. Wang, Locating multiple types of charging facili- ties for battery electric vehicles, Transportation Research Part B: Methodological 103 (2017) 30–55, green Urban Transportation. doi:https://doi.org/10.1016/j.trb.2017.01.005. URLhttps://www.sciencedirect.com/science/article/pii/ S0191261516305641

    work page doi:10.1016/j.trb.2017.01.005 2017

  31. [32]

    C. Bian, H. Li, F. Wallin, A. Avelin, L. Lin, Z. Yu, Find- ing the optimal location for public charging stations – a gis-based milp approach, Energy Procedia 158 (2019) 6582– 6588, innovative Solutions for Energy Transitions.doi:https: //doi.org/10.1016/j.egypro.2019.01.071. URLhttps://www.sciencedirect.com/science/article/pii/ S1876610219300803 65

    work page doi:10.1016/j.egypro.2019.01.071 2019

  32. [33]

    Luo, J.-S

    Z.-Q. Luo, J.-S. Pang, D. Ralph, Mathematical Programs with Equi- librium Constraints, Cambridge University Press, 1996

    work page 1996

  33. [34]

    Obeid, A

    H. Obeid, A. T. Ozturk, W. Zeng, S. J. Moura, Learn- ing and optimizing charging behavior at pev charging sta- tions: Randomized pricing experiments, and joint power and price optimization, Applied Energy 351 (2023) 121862. doi:https://doi.org/10.1016/j.apenergy.2023.121862. URLhttps://www.sciencedirect.com/science/article/pii/ S0306261923012266

    work page doi:10.1016/j.apenergy.2023.121862 2023

  34. [35]

    G. Zhou, Q. Dong, Y. Zhao, H. Wang, L. Jian, Y. Jia, Bilevel optimization approach to fast charging station planning in electri- fied transportation networks, Applied Energy 350 (2023) 121718. doi:https://doi.org/10.1016/j.apenergy.2023.121718. URLhttps://www.sciencedirect.com/science/article/pii/ S0306261923010826

    work page doi:10.1016/j.apenergy.2023.121718 2023

  35. [36]

    Lamontagne, M

    S. Lamontagne, M. Carvalho, E. Frejinger, B. Gendron, M. F. Anjos, R.Atallah, Optimisingelectricvehiclechargingstationplacementusing advanced discrete choice models, INFORMS Journal on Computing 35 (5) (2023) 909–1213, published online May 25, 2023.doi:10.1287/ ijoc.2022.0185

    work page arXiv 2023

  36. [37]

    Najafi-Ghalelou, M

    A. Najafi-Ghalelou, M. Khorasany, R. Razzaghi, Stochastic two-stage coordination of electric vehicles in distribution networks: A multi- 66 follower bi-level approach, Journal of Cleaner Production 414 (2023) 137610.doi:https://doi.org/10.1016/j.jclepro.2023.137610. URLhttps://www.sciencedirect.com/science/article/pii/ S0959652623017687

    work page doi:10.1016/j.jclepro.2023.137610 2023

  37. [38]

    B. Li, J. Li, Z. Liu, H. Su, Network equilibrium of a transportation-power distribution coupled system: A stackel- berg–nash game model, Renewable Energy 241 (2025) 122250. doi:https://doi.org/10.1016/j.renene.2024.122250. URLhttps://www.sciencedirect.com/science/article/pii/ S0960148124023188

    work page doi:10.1016/j.renene.2024.122250 2025

  38. [39]

    Lasry, P.-L

    J.-M. Lasry, P.-L. Lions, Mean field games, Japanese Journal of Math- ematics 2 (1) (2007) 229–260.doi:10.1007/s11537-007-0657-8

    work page doi:10.1007/s11537-007-0657-8 2007

  39. [40]

    Shariatzadeh, M

    M. Shariatzadeh, M. A. Lopes, C. Henggeler Antunes, Electric vehicle users’ charging behavior: A review of influential factors, meth- ods and modeling approaches, Applied Energy 396 (2025) 126167. doi:https://doi.org/10.1016/j.apenergy.2025.126167. URLhttps://www.sciencedirect.com/science/article/pii/ S0306261925008979

    work page doi:10.1016/j.apenergy.2025.126167 2025

  40. [41]

    Liang, T

    Z. Liang, T. Qian, M. Korkali, R. Glatt, Q. Hu, A vehicle-to-grid plan- ning framework incorporating electric vehicle user equilibrium and dis- tribution network flexibility enhancement, Applied Energy 376 (2024) 67 124231.doi:https://doi.org/10.1016/j.apenergy.2024.124231. URLhttps://www.sciencedirect.com/science/article/pii/ S0306261924016143

    work page doi:10.1016/j.apenergy.2024.124231 2024

  41. [42]

    Z. Sun, Y. He, H. Wu, A. Y. Wu, Bi-level planning of elec- tric vehicle charging stations considering charging demand: A nash bargaining game approach, Energy 332 (2025) 137137. doi:https://doi.org/10.1016/j.energy.2025.137137. URLhttps://www.sciencedirect.com/science/article/pii/ S0360544225027793

    work page doi:10.1016/j.energy.2025.137137 2025

  42. [43]

    M. A. Kazemi, M. Sedighizadeh, M. J. Mirzaei, O. Homaee, Optimal sitingandsizingofdistributionsystemoperatorownedEVparkinglots, Applied Energy 179 (2016) 1176–1184.doi:10.1016/j.apenergy. 2016.06.125

    work page doi:10.1016/j.apenergy 2016

  43. [44]

    Z. Yi, J. Smart, A framework for integrated dispatching and charg- ing management of an autonomous electric vehicle ride-hailing fleet, TransportationResearchPartD:TransportandEnvironment95(2021) 102822.doi:10.1016/j.trd.2021.102822

    work page doi:10.1016/j.trd.2021.102822 2021

  44. [45]

    J. Wang, H. D. Kaushik, R. A. Jacob, J. Zhang, Spatiotemporal plan- ning of electric vehicle charging infrastructure: Demand estimation and grid-aware optimization under uncertainty, iScience 28 (9) (2025) 113368.doi:https://doi.org/10.1016/j.isci.2025.113368. 68 URLhttps://www.sciencedirect.com/science/article/pii/ S2589004225016293

    work page doi:10.1016/j.isci.2025.113368 2025

  45. [46]

    Toregas, R

    C. Toregas, R. Swain, C. ReVelle, L. Bergman, The location of emer- gency service facilities, Operations Research 19 (6) (1971) 1363–1373. doi:10.1287/opre.19.6.1363

    work page doi:10.1287/opre.19.6.1363 1971

  46. [47]

    Yazır, A

    K. Yazır, A. Karasan, İhsan Kaya, Optimization and prioritization of electric vehicle charging locations for intercity transport: A dual-phase approach, Engineering Applications of Artificial Intelligence 159 (2025) 111786.doi:https://doi.org/10.1016/j.engappai.2025.111786. URLhttps://www.sciencedirect.com/science/article/pii/ S0952197625017889

    work page doi:10.1016/j.engappai.2025.111786 2025

  47. [48]

    S. L. Hakimi, Optimum locations of switching centers and the absolute centers and medians of a graph, Operations Research 12 (3) (1964) 450–459.doi:10.1287/opre.12.3.450

    work page doi:10.1287/opre.12.3.450 1964

  48. [49]

    F. J. Faustino, J. C. Lopes, J. D. Melo, T. Sousa, A. Padilha-Feltrin, J. A. Brito, C. O. Garcia, Identifying charging zones to allocate public charging stations for electric vehicles, Energy 283 (2023) 128436. doi:https://doi.org/10.1016/j.energy.2023.128436. URLhttps://www.sciencedirect.com/science/article/pii/ S0360544223018303

    work page doi:10.1016/j.energy.2023.128436 2023

  49. [50]

    Ljósheim, S

    H. Ljósheim, S. Jenkins, K. Searle, J. Wolff, Optimal placement of 69 electric vehicle slow-charging stations: A continuous facility location problem under uncertainty, Computers & Operations Research 185 (2026) 107289.doi:https://doi.org/10.1016/j.cor.2025.107289. URLhttps://www.sciencedirect.com/science/article/pii/ S0305054825003181

    work page doi:10.1016/j.cor.2025.107289 2026

  50. [51]

    M. J. Kłos, G. Sierpiński, Siting of electric vehicle charging sta- tions method addressing area potential and increasing their ac- cessibility, Journal of Transport Geography 109 (2023) 103601. doi:https://doi.org/10.1016/j.jtrangeo.2023.103601. URLhttps://www.sciencedirect.com/science/article/pii/ S096669232300073X

    work page doi:10.1016/j.jtrangeo.2023.103601 2023

  51. [52]

    S. L. Hakimi, Optimum distribution of switching centers in a communi- cation network and some related graph theoretic problems, Operations Research 13 (3) (1965) 462–475.doi:10.1287/opre.13.3.462

    work page doi:10.1287/opre.13.3.462 1965

  52. [53]

    M. Kchaou-Boujelben, Charging station location problem: A comprehensive review on models and solution approaches, Trans- portation Research Part C: Emerging Technologies 132 (2021) 103376. doi:https://doi.org/10.1016/j.trc.2021.103376. URLhttps://www.sciencedirect.com/science/article/pii/ S0968090X21003776

    work page doi:10.1016/j.trc.2021.103376 2021

  53. [54]

    B. D. Chung, S. Park, C. Kwon, Equitable distribution of recharging stations for electric vehicles, Socio-Economic Planning Sciences 63 70 (2018) 1–11.doi:https://doi.org/10.1016/j.seps.2017.06.002. URLhttps://www.sciencedirect.com/science/article/pii/ S0038012116302579

    work page doi:10.1016/j.seps.2017.06.002 2018

  54. [55]

    C. M. S. Lezcano, C. D. Harper, D. Nock, Siting for demand and equity: Optimizing level 2 electric vehicle charger place- ment, Journal of Transport Geography 128 (2025) 104369. doi:https://doi.org/10.1016/j.jtrangeo.2025.104369. URLhttps://www.sciencedirect.com/science/article/pii/ S0966692325002601

    work page doi:10.1016/j.jtrangeo.2025.104369 2025

  55. [56]

    M. Kuby, S. Lim, The flow-refueling location problem for alternative- fuel vehicles, Socio-Economic Planning Sciences 39 (2) (2005) 125–145. doi:10.1016/j.seps.2004.03.001

    work page doi:10.1016/j.seps.2004.03.001 2005

  56. [57]

    Ghamami, A

    M. Ghamami, A. Zockaie, Y. M. Nie, A general corri- dor model for designing plug-in electric vehicle charging in- frastructure to support intercity travel, Transportation Re- search Part C: Emerging Technologies 68 (2016) 389–402. doi:https://doi.org/10.1016/j.trc.2016.04.016. URLhttps://www.sciencedirect.com/science/article/pii/ S0968090X16300298

    work page doi:10.1016/j.trc.2016.04.016 2016

  57. [58]

    F. Xie, Z. Lin, Integrated u.s. nationwide corridor charg- ing infrastructure planning for mass electrification of inter- city trips, Applied Energy 298 (2021) 117142.doi:https: 71 //doi.org/10.1016/j.apenergy.2021.117142. URLhttps://www.sciencedirect.com/science/article/pii/ S0306261921005821

    work page doi:10.1016/j.apenergy.2021.117142 2021

  58. [59]

    Capar, M

    I. Capar, M. Kuby, V. J. Leon, Y.-J. Tsai, An arc cover–path cover formulation and strategic analysis of alternative-fuel station locations, European Journal of Operational Research 227 (1) (2013) 142–151. doi:10.1016/j.ejor.2012.11.033

    work page doi:10.1016/j.ejor.2012.11.033 2013

  59. [60]

    Miralinaghi, Y

    M. Miralinaghi, Y. Lou, B. B. Keskin, A. Zarrinmehr, R. Shabanpour, Refueling station location problem with traffic deviation considering route choice and demand uncertainty, International Journal of Hydro- gen Energy 42 (5) (2017) 3335–3351.doi:10.1016/j.ijhydene.2016. 12.137

    work page doi:10.1016/j.ijhydene.2016 2017

  60. [61]

    Dimatulac, H

    T. Dimatulac, H. Maoh, R. Carriveau, Electricity demand assessment and charging infrastructure planning for long-haul electric vehicle operations in ontario, canada, Transport Policy 178 (2026) 103970. doi:https://doi.org/10.1016/j.tranpol.2025.103970. URLhttps://www.sciencedirect.com/science/article/pii/ S0967070X2500513X

    work page doi:10.1016/j.tranpol.2025.103970 2026

  61. [62]

    Borlaug, V

    B. Borlaug, V. Caristo, F. Ouren, F. Yang, E. Wood, L. Rober- son, Economics of electric vehicle corridor fast charging in the united states, Advances in Applied Energy 21 (2026) 100257. 72 doi:https://doi.org/10.1016/j.adapen.2025.100257. URLhttps://www.sciencedirect.com/science/article/pii/ S2666792425000514

    work page doi:10.1016/j.adapen.2025.100257 2026

  62. [63]

    Speth, P

    D. Speth, P. Plötz, M. Wietschel, An optimal capacity-constrained fast charging network for battery electric trucks in germany, Trans- portation Research Part A: Policy and Practice 193 (2025) 104383. doi:https://doi.org/10.1016/j.tra.2025.104383. URLhttps://www.sciencedirect.com/science/article/pii/ S0965856425000114

    work page doi:10.1016/j.tra.2025.104383 2025

  63. [64]

    Anadón Martínez, A

    V. Anadón Martínez, A. Sumper, A. E. Saldaña-Gonzalez, V. Gadelha, Planning fast-charging stations along highways us- ing probability distribution functions and traffic data, Sustain- able Energy Technologies and Assessments 82 (2025) 104547. doi:https://doi.org/10.1016/j.seta.2025.104547. URLhttps://www.sciencedirect.com/science/article/pii/ S2213138825003789

    work page doi:10.1016/j.seta.2025.104547 2025

  64. [65]

    Nareshkumar, D

    K. Nareshkumar, D. Das, Optimal location and sizing of elec- tric vehicles charging stations and renewable sources in a coupled transportation-power distribution network, Renew- able and Sustainable Energy Reviews 203 (2024) 114767. doi:https://doi.org/10.1016/j.rser.2024.114767. 73 URLhttps://www.sciencedirect.com/science/article/pii/ S1364032124004933

    work page doi:10.1016/j.rser.2024.114767 2024

  65. [66]

    Q. Ye, P. Bansal, B. Adey, Multi-period charging infras- tructure planning under uncertainty: Challenges and oppor- tunities, Sustainable Cities and Society 116 (2024) 105908. doi:https://doi.org/10.1016/j.scs.2024.105908. URLhttps://www.sciencedirect.com/science/article/pii/ S2210670724007327

    work page doi:10.1016/j.scs.2024.105908 2024

  66. [67]

    K. Jain, V. Vazirani, Approximation algorithms for metric facility location and k-median problems using the primal-dual schema and lagrangian relaxation, Journal of The ACM - JACM 48 (03 2001). doi:10.1145/375827.375845

    work page doi:10.1145/375827.375845 2001

  67. [68]

    Y. Li, J. Luo, C.-Y. Chow, K.-L. Chan, Y. Ding, F. Zhang, Grow- ing the charging station network for electric vehicles with trajectory data analytics, in: 2015 IEEE 31st International Conference on Data Engineering, 2015, pp. 1376–1387.doi:10.1109/ICDE.2015.7113384

    work page doi:10.1109/icde.2015.7113384 2015

  68. [69]

    M. Song, L. Cheng, M. Du, C. Sun, J. Ma, H. Ge, Charging sta- tion location problem for maximizing the space-time-electricity accessibility: A lagrangian relaxation-based decomposition scheme, Expert Systems with Applications 222 (2023) 119801. doi:https://doi.org/10.1016/j.eswa.2023.119801. 74 URLhttps://www.sciencedirect.com/science/article/pii/ S095741...

    work page doi:10.1016/j.eswa.2023.119801 2023

  69. [70]

    L. Yin, Y. Zhang, Particle swarm optimization based on data driven for ev charging station siting, Energy 310 (2024) 133197. doi:https://doi.org/10.1016/j.energy.2024.133197. URLhttps://www.sciencedirect.com/science/article/pii/ S0360544224029724

    work page doi:10.1016/j.energy.2024.133197 2024

  70. [71]

    G. Zhou, Z. Zhu, S. Luo, Location optimization of elec- tric vehicle charging stations: Based on cost model and ge- netic algorithm, Energy 247 (2022) 123437.doi:https: //doi.org/10.1016/j.energy.2022.123437. URLhttps://www.sciencedirect.com/science/article/pii/ S0360544222003401

    work page doi:10.1016/j.energy.2022.123437 2022

  71. [72]

    Frade, A

    I. Frade, A. Ribeiro, G. Gonçalves, A. Pais Antunes, Optimal location of charging stations for electric vehicles in a neighborhood in lisbon, portugal, TRR 2252 (12 2011).doi:10.3141/2252-12

    work page doi:10.3141/2252-12 2011

  72. [73]

    A. H. K. Babar, A. Yousaf, Optimization of charging infrastructure planning for plug-in electric vehicles based on a dynamic program- ming model, Transportation Planning and Technology 45 (1) (2022) 59–75.arXiv:https://doi.org/10.1080/03081060.2021.2017207, doi:10.1080/03081060.2021.2017207. URLhttps://doi.org/10.1080/03081060.2021.2017207 75

    work page doi:10.1080/03081060.2021.2017207 2022

  73. [74]

    Z. Bi, G. A. Keoleian, T. Ersal, Wireless charger deploy- ment for an electric bus network: A multi-objective life cycle optimization, Applied Energy 225 (2018) 1090–1101. doi:https://doi.org/10.1016/j.apenergy.2018.05.070. URLhttps://www.sciencedirect.com/science/article/pii/ S030626191830789X

    work page doi:10.1016/j.apenergy.2018.05.070 2018

  74. [75]

    Pourvaziri, H

    H. Pourvaziri, H. Sarhadi, N. Azad, H. Afshari, M. Taghavi, Planning of electric vehicle charging stations: An integrated deep learning and queueing theory approach, Transportation Research Part E: Logistics and Transportation Review 186 (2024) 103568. doi:https://doi.org/10.1016/j.tre.2024.103568. URLhttps://www.sciencedirect.com/science/article/pii/ S13...

    work page doi:10.1016/j.tre.2024.103568 2024

  75. [76]

    D. Xiao, S. An, H. Cai, J. Wang, H. Cai, An optimization model for electric vehicle charging infrastructure planning considering queuing behavior with finite queue length, Journal of Energy Storage 29 (2020) 101317.doi:https://doi.org/10.1016/j.est.2020.101317. URLhttps://www.sciencedirect.com/science/article/pii/ S2352152X19309053

    work page doi:10.1016/j.est.2020.101317 2020

  76. [77]

    O. B. Kınay, F. Gzara, S. A. Alumur, Charging station location and sizing for electric vehicles under congestion, Transportation Science 76 57 (6) (2023) 1433–1451.doi:10.1287/trsc.2021.0494. URLhttps://doi.org/10.1287/trsc.2021.0494

    work page doi:10.1287/trsc.2021.0494 2023

  77. [78]

    A., Hekker, S., Stello, D., Guti ´errez-Soto, J., Handberg, R., Huber, D., et al

    W. Whitt, Approximations for the GI/G/m queue, Production and Operations Management 2 (2) (1993) 114–161.doi:10.1111/j. 1937-5956.1993.tb00094.x

    work page doi:10.1111/j 1993

  78. [80]

    I. S. Bayram, A. Tajer, M. Abdallah, K. Qaraqe, Capacity planning frameworks for electric vehicle charging stations with multiclass cus- tomers, IEEE Transactions on Smart Grid 6 (4) (2015) 1934–1943. doi:10.1109/TSG.2015.2406532

    work page doi:10.1109/tsg.2015.2406532 2015

  79. [81]

    Pourvaziri, M

    H. Pourvaziri, M. Taghavi, H. Sarhadi, H. Afshari, N. Azad, Multi- objective planning of electric vehicles charging stations by integrating drivers’ preferences and fairness considerations: A case study in halifax, canada, Computers & Industrial Engineering 201 (2025) 110886.doi:https://doi.org/10.1016/j.cie.2025.110886. URLhttps://www.sciencedirect.com/s...

    work page doi:10.1016/j.cie.2025.110886 2025

  80. [82]

    J. Yang, J. Dong, L. Hu, A data-driven optimization-based approach 77 for siting and sizing of electric taxi charging stations, Transporta- tion Research Part C: Emerging Technologies 77 (2017) 462–477. doi:https://doi.org/10.1016/j.trc.2017.02.014. URLhttps://www.sciencedirect.com/science/article/pii/ S0968090X17300542

    work page doi:10.1016/j.trc.2017.02.014 2017

Showing first 80 references.