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arxiv: 2510.24758 · v2 · submitted 2025-10-21 · 📡 eess.SY · cs.CY· cs.MA· cs.SY

A Digital Twin Framework for Decision-Support and Optimization of EV Charging Infrastructure in Localized Urban Systems

Bui Khanh Linh Do , Thanh H. Nguyen , Nghi Huynh Quang , Doanh Nguyen-Ngoc , Laurent El Ghaoui This is my paper

Pith reviewed 2026-05-18 05:05 UTC · model grok-4.3

classification 📡 eess.SY cs.CYcs.MAcs.SY
keywords digital twinEV charging infrastructureagent-based simulationmetaheuristic optimizationsolar-powered chargersurban mobilitypolicy evaluation
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The pith

A digital twin framework shows that targeted policies and optimized charger mixes improve EV charging operations in urban campus settings.

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

This paper introduces a digital twin that uses agent-based simulations and optimization to model electric vehicle charging at a university campus in Hanoi. It tests various policies and hardware configurations against goals of user satisfaction, energy efficiency, and financial returns. If the approach works as described, planners could use such tools to design better charging networks before building them. Readers interested in sustainable transport would care because electric vehicle growth demands smarter infrastructure management to avoid bottlenecks and waste.

Core claim

The digital twin framework integrates agent-based decision support with embedded metaheuristic optimization to dynamically simulate EV charging behaviors, infrastructure layouts, and policy responses. In the Hanoi campus case, it identifies that dynamic notifications improve user satisfaction, gasoline bans and idle fees enhance slot turnover, and near-optimal mixes of 30kW fast and 11kW standard solar-powered chargers balance energy performance, profitability, and demand, while noting a 20% seasonal drop in solar efficiency.

What carries the argument

The integrated digital twin that combines agent-based modeling of user behaviors with metaheuristic optimization routines for evaluating charging scenarios.

If this is right

  • Dynamic notifications of newly available charging slots increase user satisfaction.
  • Gasoline bans and idle fees improve slot turnover rates with minimal added system complexity.
  • Near-optimal mixes of fast and standard solar chargers can be identified efficiently for balancing performance metrics.
  • Seasonal solar efficiency variations of around 20% highlight the need for adaptive renewable integration in charging plans.
  • The modular design supports extension from single sites to larger urban networks.

Where Pith is reading between the lines

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

  • Similar digital twin models could help test EV policies in other cities by adjusting the agent behaviors to local conditions.
  • Real-time integration of live usage data might further improve the accuracy of the optimization recommendations over time.
  • Extending the model to include battery degradation or grid impact could address additional practical concerns in EV infrastructure planning.

Load-bearing premise

The agent-based simulations and inputs like solar efficiency accurately reflect actual user behaviors and conditions at the Hanoi campus site.

What would settle it

Comparing the simulation outputs for user satisfaction and turnover to measured data from actual EV charging stations installed on the campus after implementing the recommended policies.

Figures

Figures reproduced from arXiv: 2510.24758 by Bui Khanh Linh Do, Doanh Nguyen-Ngoc, Laurent El Ghaoui, Nghi Huynh Quang, Thanh H. Nguyen.

Figure 1
Figure 1. Figure 1: The model environment replicates the campus, manages vehicle flow, parking, and charging, and distinguishes vehicle agents: gasoline (blue circle) & EVs (yellow circles). 3.1.2. Model entities The model consists of three interconnected sub-models: the charging sta￾tion sub-model, the energy sub-model and the traffic sub-model. These com￾ponents interact dynamically, reflecting the influence of EV charging … view at source ↗
Figure 2
Figure 2. Figure 2: Decision-making process of vehicle agents, expanded from (Kam et al., 2019; Lee et al., 2020). Each timestep follows the same loop for gasoline and EVs, except in "Do Assign Slot," where EV parking depends on SoC and station preferences [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Digital Twin dashboard for real-time EV charging visualization, integrating (1) Interactive parameters control panel, (2) GIS-based interactive multi-agent simulation, and (3) Performance charts tracking user satisfaction, energy efficiency, and financial viability. Our dashboard, shown in [PITH_FULL_IMAGE:figures/full_fig_p017_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Average one-month simulation results for Quarter 1 & Quarter 3, showing total energy consumption (kWh), renewable energy usage (kWh), and self-sufficiency (%) without wind energy integration. As shown in [PITH_FULL_IMAGE:figures/full_fig_p018_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Average one-month simulation results for Q1, comparing self-sufficiency (%) with (orange) and without (blue) wind energy integration using Wilcoxon Test. To better utilize solar energy, prioritizing BESS expansion over wind in￾vestment would be more effective, given the weak wind energy potential in Vietnam. While the simulation includes an 80 kW BESS, further optimiza￾19 [PITH_FULL_IMAGE:figures/full_fig… view at source ↗
Figure 6
Figure 6. Figure 6: EV satisfaction scores for six combinations of four policy interventions, including gasoline bans, idle fees, EV relocation, and real-time notifications (defined in [PITH_FULL_IMAGE:figures/full_fig_p020_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: EV satisfaction scores across six policy combination cases (defined in [PITH_FULL_IMAGE:figures/full_fig_p021_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Satisfaction scores for different 30kW (horizontal axis) and 11kW (vertical axis) charger combinations under two demand scenarios: 100 EVs (left) and 200 EVs (right). Regarding solar panel integration in the 50-EV scenario, [PITH_FULL_IMAGE:figures/full_fig_p022_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Relationship between number of solar panels and two key metrics: the nor￾malized payback period (blue) and the self-sufficiency and self-consumption (orange and green, respectively) for the 50 EVs scenario. The optimal configurations derived from the Objective Function eval￾uation ( [PITH_FULL_IMAGE:figures/full_fig_p023_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Heatmap of the Total-Order Sobol Sensitivity Indices (ST) of key factors affecting EV satisfaction, station efficiency, and financial viability. For energy performance, solar panel capacity has the strongest influence 24 [PITH_FULL_IMAGE:figures/full_fig_p024_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Comparison of PSO optimization vs. full-case results across solar panels and charging port configurations (11 kW & 30 kW). Each point represents configurations for 50 EVs, 100 EVs, 150 EVs, and 200 EVs, with PSO results shown as bigger markers. EV100 and EV150 (1.238 and 1.345) stem from the relative influence of so￾lar panel quantities in the optimization process, as they contribute more significantly to… view at source ↗
read the original abstract

As Electric Vehicle (EV) adoption accelerates in urban environments, optimizing charging infrastructure is vital for balancing user satisfaction, energy efficiency, and financial viability. This study advances beyond static models by proposing a digital twin framework that integrates agent-based decision support with embedded optimization to dynamically simulate EV charging behaviors, infrastructure layouts, and policy responses across scenarios. Applied to a localized urban site (a university campus) in Hanoi, Vietnam, the model evaluates operational policies, EV station configurations, and renewable energy sources. The interactive dashboard enables seasonal analysis, revealing a 20% drop in solar efficiency from October to March, with wind power contributing under 5% of demand, highlighting the need for adaptive energy management. Simulations show that dynamic notifications of newly available charging slots improve user satisfaction, while gasoline bans and idle fees enhance slot turnover with minimal added complexity. Embedded metaheuristic optimization identifies near-optimal mixes of fast (30kW) and standard (11kW) solar-powered chargers, balancing energy performance, profitability, and demand with high computational efficiency. This digital twin provides a flexible, computation-driven platform for EV infrastructure planning, with a transferable, modular design that enables seamless scaling from localized to city-wide urban contexts.

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

2 major / 1 minor

Summary. The manuscript proposes a digital twin framework combining agent-based simulation of EV user behaviors with embedded metaheuristic optimization to evaluate policies (dynamic notifications, gasoline bans, idle fees) and charger configurations (mixes of 30 kW fast and 11 kW standard solar-powered units) at a Hanoi university campus. It reports a 20% seasonal solar efficiency drop (October–March), wind contribution below 5%, improved user satisfaction from notifications, better slot turnover from bans and fees, and near-optimal charger mixes balancing energy performance, profitability, and demand, with a modular design for scaling to city-wide contexts.

Significance. If the agent-based model were shown to match real campus data, the framework would provide a useful, transferable platform for integrating renewables and policy testing in localized EV systems. The embedded optimization and dashboard for seasonal analysis represent practical strengths for decision support.

major comments (2)
  1. [Agent-based simulation section] Agent-based simulation section: The user decision rules (arrival times, willingness to wait, response to notifications, choice between fast/standard chargers) are not calibrated or validated against any real charging session logs or baseline metrics from the Hanoi site. This is load-bearing for the central claims, as all quantitative outcomes on satisfaction and turnover improvements are generated internally by the simulator.
  2. [Renewable energy results] Renewable energy results: The reported 20% drop in solar efficiency and wind contribution under 5% of demand are presented without cited sources, site-specific measurements, or sensitivity sweeps on environmental parameters, which directly supports the adaptive energy management recommendations.
minor comments (1)
  1. [Abstract] The abstract states 'high computational efficiency' for the metaheuristic without reporting runtime metrics, iteration counts, or comparisons to alternative solvers.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed review of our manuscript. We address each major comment point by point below, providing clarifications on our methodological approach and indicating revisions where they strengthen the work without misrepresenting the simulation-based nature of the study.

read point-by-point responses

  1. Referee: Agent-based simulation section: The user decision rules (arrival times, willingness to wait, response to notifications, choice between fast/standard chargers) are not calibrated or validated against any real charging session logs or baseline metrics from the Hanoi site. This is load-bearing for the central claims, as all quantitative outcomes on satisfaction and turnover improvements are generated internally by the simulator.

    Authors: We acknowledge the importance of empirical grounding for the agent-based component. The decision rules were parameterized based on peer-reviewed studies of EV user behavior in urban environments, including arrival distributions and charger selection preferences. In the revised manuscript, we have expanded the methods section with explicit citations to these sources and a transparent discussion of all assumptions. We also added a limitations paragraph clarifying that the quantitative outcomes illustrate relative policy effects within the modeled scenarios rather than absolute real-world predictions. Site-specific charging logs from the Hanoi campus were not available to the authors for calibration or validation, as this study relied on a simulation framework for exploratory analysis; we have noted this as an avenue for future collaborative work with local operators. revision: partial

  2. Referee: Renewable energy results: The reported 20% drop in solar efficiency and wind contribution under 5% of demand are presented without cited sources, site-specific measurements, or sensitivity sweeps on environmental parameters, which directly supports the adaptive energy management recommendations.

    Authors: We agree that additional sourcing and analysis would improve transparency. The 20% seasonal solar efficiency reduction reflects documented performance in Hanoi's tropical climate during the October–March period, and we have now added citations to relevant regional meteorological and photovoltaic studies. The wind contribution estimate draws from preliminary local resource assessments indicating limited potential at the campus scale. We have incorporated a new sensitivity analysis subsection that varies solar irradiance and wind speed parameters across plausible ranges, demonstrating that the adaptive management recommendations remain robust. These revisions are included in the updated manuscript. revision: yes

Circularity Check

0 steps flagged

No circularity: agent-based simulations and metaheuristic optimization derive outputs from independent behavioral rules and inputs

full rationale

The paper presents a digital twin framework using agent-based modeling to simulate EV user behaviors, policy effects, and renewable energy inputs, followed by embedded metaheuristic optimization for charger configurations. No equations, fitted parameters, or self-citations are described that define target outcomes (e.g., satisfaction scores or optimal mixes) in terms of themselves or reduce predictions directly to calibration data. The reported results on notifications, fees, and charger mixes emerge from the model's stated rules and external assumptions like the 20% solar drop, making the chain self-contained rather than tautological.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The framework rests on standard domain assumptions in agent-based modeling and optimization without introducing new physical entities or heavily fitted parameters visible in the abstract; specific numerical inputs such as charger ratings appear chosen for the case study rather than derived.

free parameters (1)
  • Charger power ratings (30 kW fast, 11 kW standard)
    Specific values selected for the optimization scenarios and solar-powered charger mix evaluation.
axioms (1)
  • domain assumption Agent-based models with decision rules can produce realistic representations of EV user charging behavior and policy responses.
    Central to the simulation component described in the abstract.

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Works this paper leans on

54 extracted references · 54 canonical work pages

  1. [1]

    Aldossary, M., Alharbi, H.A., Ayub, N.,

    doi:10.3390/su141710517. Aldossary, M., Alharbi, H.A., Ayub, N.,

    work page doi:10.3390/su141710517

  2. [2]

    Alhasan, A., Umar, D., Alkawsi, G., Alkahtani, A., Alomari, M., Aris, H.,

    doi:10.3390/math12172627. Alhasan, A., Umar, D., Alkawsi, G., Alkahtani, A., Alomari, M., Aris, H.,

    work page doi:10.3390/math12172627

  3. [3]

    Information Sciences Letters 12, 3013–3023

    Towards wind energy-based charging stations: A review of optimization methods. Information Sciences Letters 12, 3013–3023. doi:10.18576/isl/120909. Aliedani, A., Loke, S.W.,

    work page doi:10.18576/isl/120909

  4. [4]

    Computers Environment and Urban Systems 77, 101256

    Cooperative car parking using vehicle-to- vehicle communication: An agent-based analysis. Computers Environment and Urban Systems 77, 101256. doi:10.1016/j.compenvurbsys.2018.06

    work page doi:10.1016/j.compenvurbsys.2018.06 2018

  5. [5]

    URL:https: //link.springer.com/book/9789819663880

    Agent-based modelling approach to support strategic planning for ev charging stations in a university campus, in: Proceedings of the 13th International Conference on Com- putational Data and Social Networks (CSoNet 2024). URL:https: //link.springer.com/book/9789819663880. accepted, to appear. Asna, M., Shareef, H., Achikkulath, P., Mokhlis, H., Errouissi,...

    work page arXiv 2024

  6. [6]

    IEEE Access 9, 73678–73694

    Analysis of an optimal planning model for electric vehicle fast- charging stations in al ain city, united arab emirates. IEEE Access 9, 73678–73694. doi:10.1109/access.2021.3081020. 29 Battiti, R., Tecchiolli, G.,

    work page doi:10.1109/access.2021.3081020 2021

  7. [7]

    INFORMS Journal on Computing 6, 126–140

    The reactive tabu search. INFORMS Journal on Computing 6, 126–140. doi:10.1287/ijoc.6.2.126. Belfadel, A., Hörl, S., Tapia, R.J., Politaki, D., Kureshi, I., Tavasszy, L., Puchinger, J.,

    work page doi:10.1287/ijoc.6.2.126

  8. [8]

    Computers Environment and Urban Systems 103, 101989

    A conceptual digital twin framework for city logistics. Computers Environment and Urban Systems 103, 101989. doi:10.1016/ j.compenvurbsys.2023.101989. Bright, J.M.,

    work page arXiv 2023

  9. [9]

    Solar Energy 189, 435–449

    Solcast: Validation of a satellite-derived solar irradiance dataset. Solar Energy 189, 435–449. doi:10.1016/j.solener.2019.07

    work page doi:10.1016/j.solener.2019.07 2019

  10. [10]

    Technology in Society 67, 101771

    Electric vehicle industry sustain- able development with a stakeholder engagement system. Technology in Society 67, 101771. doi:10.1016/j.techsoc.2021.101771. Caruso, M., Livreri, P., Miceli, R., Viola, F., Martino, M.,

    work page doi:10.1016/j.techsoc.2021.101771 2021

  11. [11]

    Ev charging station at university campus, in: 2017 IEEE International Telecommunica- tions Energy Conference (INTELEC), pp. 615–623. doi:10.1109/INTLEC. 2017.8214206. Cui, X., Zhang, Z., Liu, F., Liu, J.,

    work page doi:10.1109/intlec 2017

  12. [12]

    Journal of Intelligent & Fuzzy Systems 41, 4735–4747

    Layout evaluation for electric vehicle charging pile based on charging frequency. Journal of Intelligent & Fuzzy Systems 41, 4735–4747. doi:10.3233/jifs-189960. Davidov, S., Pantoš, M.,

    work page doi:10.3233/jifs-189960

  13. [13]

    Energy 166, 886–894

    Optimization model for charging infrastruc- ture planning with electric power system reliability check. Energy 166, 886–894. doi:10.1016/j.energy.2018.10.150. Diaz, W.F.S., Vargas, J.T., Martinez, F.,

    work page doi:10.1016/j.energy.2018.10.150 2018

  14. [14]

    IEEE Access 10, 53873–53893

    On the role of renewable energy policies and electric vehicle deployment in- centives for a greener sector coupling. IEEE Access 10, 53873–53893. doi:10.1109/access.2022.3176012. 30 Francisco, A.M.B., Monteiro, J., Cardoso, P.J.S.,

    work page doi:10.1109/access.2022.3176012 2022

  15. [15]

    IEEE Access 11, 125664– 125683

    A digital twin of charging stations for fleets of electric vehicles. IEEE Access 11, 125664– 125683. doi:10.1109/ACCESS.2023.3330833. Glover, F.,

    work page doi:10.1109/access.2023.3330833 2023

  16. [16]

    INFORMS Journal on Computing 1, 190–206

    Tabu Search—Part i. INFORMS Journal on Computing 1, 190–206. doi:10.1287/ijoc.1.3.190. Glover, F.,

    work page doi:10.1287/ijoc.1.3.190

  17. [17]

    INFORMS Journal on Computing 2, 4–32

    Tabu Search—Part II. INFORMS Journal on Computing 2, 4–32. doi:10.1287/ijoc.2.1.4. Goldberg, D.E., Holland, J.H.,

    work page doi:10.1287/ijoc.2.1.4

  18. [18]

    Machine Learning 3, 95–99

    Genetic algorithms and machine learn- ing. Machine Learning 3, 95–99. doi:10.1023/a:1022602019183. Gorbunova, A.D., Anisimov, I.A.,

    work page doi:10.1023/a:1022602019183

  19. [19]

    Emerging Science Journal 4, 539–550

    Assessment of the use of renewable energy sources for the charging infrastructure of electric vehicles. Emerging Science Journal 4, 539–550. doi:10.28991/esj-2020-01251. Holland, J.H.,

    work page doi:10.28991/esj-2020-01251 2020

  20. [20]

    In- troduction of electric vehicle charging stations to university campuses: A case study for the university of georgia from 2014 to

    work page 2014

  21. [21]

    Hussain, M.T., Sulaiman, N.B., Hussain, M.S., Jabir, M.,

    doi:10.3390/batteries4020027. Hussain, M.T., Sulaiman, N.B., Hussain, M.S., Jabir, M.,

    work page doi:10.3390/batteries4020027

  22. [22]

    Journal of Energy Storage 33, 102114

    Optimal management strategies to solve issues of grid having electric vehicles (ev): A review. Journal of Energy Storage 33, 102114. doi:10.1016/j.est. 2020.102114. IEA,

    work page doi:10.1016/j.est 2020

  23. [23]

    Kam, M., Peters, A., Alkemade, F.,

    URL:https://www.iea.org/reports/ global-ev-outlook-2024. Kam, M., Peters, A., Alkemade, F.,

    work page 2024

  24. [24]

    Kennedy, J., Eberhart, R.,

    doi:10.18564/jasss.4133. Kennedy, J., Eberhart, R.,

    work page doi:10.18564/jasss.4133

  25. [25]

    Kennedy and R

    Particle swarm optimization, in: Pro- ceedings of ICNN’95 - International Conference on Neural Networks, pp. 1942–1948 vol.4. doi:10.1109/ICNN.1995.488968. 31 Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.,

    work page doi:10.1109/icnn.1995.488968 1942

  26. [26]

    Kirkpatrick, C

    Optimization by simulated annealing. Science 220, 671–680. doi:10.1126/science.220.4598.671. Lee, Z.J., Sharma, S., Johansson, D., Low, S.H.,

    work page doi:10.1126/science.220.4598.671

  27. [27]

    arXiv (Cornell University) doi:10.48550/arxiv.2012.02809

    ACN-SIM: An Open- Source Simulator for Data-Driven Electric Vehicle Charging Research. arXiv (Cornell University) doi:10.48550/arxiv.2012.02809. Lesquoy, B., Taillandier, P., Gaudou, B., Chapuis, K., Grignard, A., Huynh, N.Q., et al.,

    work page doi:10.48550/arxiv.2012.02809 2012

  28. [28]

    Li, H., Yu, L., Zhao, Y., Wu, H., Tu, H., 2024a

    doi:10.1016/j.energy.2021.121713. Li, H., Yu, L., Zhao, Y., Wu, H., Tu, H., 2024a. Deployment of public charg- ing stations for BEVs using an Agent-Based Modeling approach. Interna- tional Journal of Transportation Science and Technology doi:10.1016/j. ijtst.2024.10.003. Li, J., Rombaut, E., Vanhaverbeke, L.,

    work page doi:10.1016/j.energy.2021.121713 2021

  29. [29]

    Computers Environment and Urban Systems 89, 101686

    A systematic review of agent- based models for autonomous vehicles in urban mobility and logistics: Pos- sibilities for integrated simulation models. Computers Environment and Urban Systems 89, 101686. doi:10.1016/j.compenvurbsys.2021.101686. Li, J., Rombaut, E., Vanhaverbeke, L., 2024b. How far are we towards sustainable Carfree cities combining shared a...

    work page doi:10.1016/j.compenvurbsys.2021.101686 2021

  30. [30]

    Optimal planning of charging station for electric vehicle based on particle swarm optimization, in: IEEE PES Innovative Smart Grid Technologies (ISGT-Asia), IEEE, Tianjin, China. pp. 1–5. doi:10.1109/ISGT-Asia.2012.6303112. Lopez, N.S., Allana, A., Biona, J.B.M.,

    work page doi:10.1109/isgt-asia.2012.6303112 2012

  31. [31]

    32 Marcucci, E., Gatta, V., Pira, M.L., Hansson, L., Bråthen, S.,

    doi:10.3390/en14133734. 32 Marcucci, E., Gatta, V., Pira, M.L., Hansson, L., Bråthen, S.,

    work page doi:10.3390/en14133734

  32. [32]

    Sustainability 12, 10623

    Digital Twins: A critical discussion on their potential for supporting Policy-Making and Planning in urban Logistics. Sustainability 12, 10623. doi:10.3390/su122410623. Marmaras, C., Xydas, E., Cipcigan, L.,

    work page doi:10.3390/su122410623

  33. [33]

    Transportation Research Part C Emerging Technologies 80, 239–256

    Simulation of electric ve- hicle driver behaviour in road transport and electric power networks. Transportation Research Part C Emerging Technologies 80, 239–256. doi:10.1016/j.trc.2017.05.004. Mehrjerdi, H., Rakhshani, E.,

    work page doi:10.1016/j.trc.2017.05.004 2017

  34. [34]

    Journal of Cleaner Production 229, 463–469

    Vehicle-to-gridtechnologyforcostreduc- tion and uncertainty management integrated with solar power. Journal of Cleaner Production 229, 463–469. doi:10.1016/j.jclepro.2019.05.023. Merei, G., Moshövel, J., Magnor, D., Sauer, D.U.,

    work page doi:10.1016/j.jclepro.2019.05.023 2019

  35. [35]

    Chudnovsky and S

    Optimization of self-consumption and techno-economic analysis of PV-battery systems in commercial applications. Applied Energy 168, 171–178. doi:10.1016/j. apenergy.2016.01.083. Minh Duc, P., Chapuis, K., Drogoul, A., Gaudou, B., Grignard, A., Mar- illeau, N., Tri, N.H.,

    work page doi:10.1016/j 2016

  36. [36]

    Hoankiemair: simulating impacts of urban management practices on traffic and air pollution using a tangible agent- based model, in: 2020 RIVF International Conference on Computing and Communication Technologies (RIVF), pp. 1–7. doi:10.1109/RIVF48685. 2020.9140787. Mouhy-Ud-Din, M.A., Azeem, F., Memon, Z., Arshad, J.,

    work page doi:10.1109/rivf48685 2020

  37. [37]

    Energy for Sustainable Development 78, 101355

    Compar- ative techno-economic assessment of electric vehicle charging preferences: A quantitative study for sustainable ev policy in pakistan. Energy for Sustainable Development 78, 101355. doi:10.1016/j.esd.2023.101355. Mudiyanselage, M., Aghdam, F., Kazemi-Razi, S., Chaudhari, K., Marzband, M., Ikpehai, A., Abusorrah, A., Harris, G.,

    work page doi:10.1016/j.esd.2023.101355 2023

  38. [38]

    IEEE Transactions on Transportation Electrification 10, 2844–2857

    A multiagent framework for electric vehicles charging power forecast and smart planning of ur- ban parking lots. IEEE Transactions on Transportation Electrification 10, 2844–2857. doi:10.1109/TTE.2023.3289196. Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.,

    work page doi:10.1109/tte.2023.3289196 2023

  39. [39]

    Energy 92, 466–475

    Agent based modelling and energy planning – Utilization of 33 MATSim for transport energy demand modelling. Energy 92, 466–475. doi:10.1016/j.energy.2015.05.091. Pagani, M., Korosec, W., Chokani, N., Abhari, R.S.,

    work page doi:10.1016/j.energy.2015.05.091 2015

  40. [40]

    Pearl, J.,

    doi:10.1016/j.apenergy.2019.113680. Pearl, J.,

    work page doi:10.1016/j.apenergy.2019.113680 2019

  41. [41]

    Nature Climate Change 6, 556–562

    Agent-based modelling of consumer energy choices. Nature Climate Change 6, 556–562. doi:10.1038/nclimate2967. Rasca, S.I., Hu, B., Biesinger, B., Prandtstetter, M.,

    work page doi:10.1038/nclimate2967

  42. [42]

    Public Transport 16, 583–618

    Agent-based decision-support model for bus route redesign in networks of small cities and towns: Case study of agder, norway. Public Transport 16, 583–618. doi:10.1007/s12469-024-00358-7. Sa’adati, R., Jafari-Nokandi, M., Saebi, J.,

    work page doi:10.1007/s12469-024-00358-7

  43. [43]

    Sustainable Cities and Society 65, 102527

    Allocation of ress and pev fast-charging station on coupled transportation and distribution net- works. Sustainable Cities and Society 65, 102527. doi:10.1016/J.SCS. 2020.102527. Sun, B.,

    work page doi:10.1016/j.scs 2020

  44. [44]

    Journal of Cleaner Production 288, 125564

    A multi-objective optimization model for fast electric vehicle charging stations with wind, pv power and energy storage. Journal of Cleaner Production 288, 125564. doi:10.1016/j.jclepro.2020.125564. Sun, J., Che, Y., Yang, T., Zhang, J., Cai, Y.,

    work page doi:10.1016/j.jclepro.2020.125564 2020

  45. [45]

    Energy Engineering 120, 367–384

    Location and capacity determination method of electric vehicle charging station based on simu- lated annealing immune particle swarm optimization. Energy Engineering 120, 367–384. doi:10.32604/ee.2023.023661. Taillandier, P., Gaudou, P., Grignard, A., Huynh, Q.N., Marilleau, N., Cail- lou, P., Philippon, D., Drogoul, A.,

    work page doi:10.32604/ee.2023.023661 2023

  46. [46]

    Geoinformatica 23, 299–322

    Building, composing and exper- imenting complex spatial models with the gama platform. Geoinformatica 23, 299–322. doi:10.1007/s10707-018-00339-6. Wang, Y., Infield, D.,

    work page doi:10.1007/s10707-018-00339-6

  47. [47]

    International Journal of Electrical Power and Energy Systems 99, 85–94

    Markov Chain Monte Carlo simulation of elec- tric vehicle use for network integration studies. International Journal of Electrical Power and Energy Systems 99, 85–94. doi:10.1016/j.ijepes. 2018.01.008. 34 Wilcoxon, F.,

    work page doi:10.1016/j.ijepes 2018

  48. [48]

    Xiaoqian Xing and Katsuhisa Maruyama

    doi:10.2307/3001968. Xiao, H., Shu, Z., Guo, Y.,

    work page doi:10.2307/3001968

  49. [49]

    SPIE 12790, Eighth International Conference on Electromechani- cal Control Technology and Transportation (ICECTT 2023), p

    Location and capacity determination model of electric vehicle charging station based on charging demand forecast, in: Proc. SPIE 12790, Eighth International Conference on Electromechani- cal Control Technology and Transportation (ICECTT 2023), p. 127902U. doi:10.1117/12.2689424. Yan, C., Liu, N.,

    work page doi:10.1117/12.2689424 2023

  50. [50]

    Journal of Physics Conference Series 2849, 012012

    Electric vehicle charging station siting planning model considering distribution grids. Journal of Physics Conference Series 2849, 012012. doi:10.1088/1742-6596/2849/1/012012. Yi, Z., Chen, B., Liu, X.C., Wei, R., Chen, J., Chen, Z.,

    work page doi:10.1088/1742-6596/2849/1/012012

  51. [51]

    Computers Environment and Urban Systems 101, 101949

    Anagent-based modeling approach for public charging demand estimation and charging station location optimization at urban scale. Computers Environment and Urban Systems 101, 101949. doi:10.1016/j.compenvurbsys.2023. 101949. Yu, G., Ye, X., Xia, X., Chen, Y.,

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

  52. [52]

    Towards cognitive ev charging stations enabled by digital twin and parallel intelligence, in: 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI), pp. 290–293. doi:10.1109/DTPI52967.2021.9540103. Zeng, B., Dong, H., Xu, F., Zeng, M.,

    work page doi:10.1109/dtpi52967.2021.9540103 2021

  53. [53]

    IEEE Transactions on Industry Applications 56, 2314–2323

    Bilevel programming approach for optimal planning design of ev charging station. IEEE Transactions on Industry Applications 56, 2314–2323. doi:10.1109/tia.2020.2973189. Zhang, C., Lian, J., Min, H., Li, M.,

    work page doi:10.1109/tia.2020.2973189 2020

  54. [54]

    doi:10.3390/su17010091. 35

    work page doi:10.3390/su17010091