Business model &amp; load profile for commercial EV charging stations

Kamlesh Kumar Khedar; Govind Rai Goyal; Pushpendra Singh; Mohan Lal Kohle; Kamlesh Kumar Khedar; Govind Rai Goyal; Pushpendra Singh; Mohan Lal Kohle

doi:10.3934/energy.2025056

AIMS Energy

2025, Volume 13, Issue 6: 1518-1537. doi: 10.3934/energy.2025056

Previous Article Next Article

Research article Special Issues

Business model & load profile for commercial EV charging stations

1.
University of Engineering & Management Jaipur, Rajasthan—303807, India
2.
Atria University Bengaluru, Karnataka-560032, India
3.
University of Agder, Norway

Received: 24 March 2025 Revised: 11 August 2025 Accepted: 16 October 2025 Published: 09 December 2025

The rapid growth of electric vehicles (EVs) necessitates a robust charging infrastructure to ensure reliability, grid stability, and user convenience. This study developeda data-driven framework for optimal siting and sizing of electric vehicle charging stations (EVCS) using geographic information systems (GISs) combined with multi-criteria decision-making techniques such as fuzzy analytical hierarchy process (FAHP) and multi-attributive border approximation area comparison (MABAC). Using the GIS-FAHP-MABAC method, we found that Site D emerges as the most suitable (suitability score of 0.86). The methodology integrates spatial, technical, and socio-economic factors to evaluate research sites while considering power system constraints and projected demand growth. Simulation results demonstrated that the proposed approach enhances load distribution, reduces grid stress, and improves accessibility compared to conventional planning methods. The findings provide actionable insights for policymakers and urban planners to accelerate sustainable EV adoption while supporting renewable energy integration and reducing dependence on fossil fuels. This paper examined the many factors that influence the business model and load profile of EV charging stations in Kota City, as well as the proportions of their charging voltage (CV) and charging current (CC) for certain stochastic distributions of the various components. The suggested business model for EV charging stations, which includes all stakeholders, can enable car owners to travel at a lower cost.
- electric vehicles,
- charging stations,
- distributed energy resources (DERs),
- business model,
- sustainability
Citation: Kamlesh Kumar Khedar, Govind Rai Goyal, Pushpendra Singh, Mohan Lal Kohle. Business model & load profile for commercial EV charging stations[J]. AIMS Energy, 2025, 13(6): 1518-1537. doi: 10.3934/energy.2025056

Related Papers:

Abstract

The rapid growth of electric vehicles (EVs) necessitates a robust charging infrastructure to ensure reliability, grid stability, and user convenience. This study developeda data-driven framework for optimal siting and sizing of electric vehicle charging stations (EVCS) using geographic information systems (GISs) combined with multi-criteria decision-making techniques such as fuzzy analytical hierarchy process (FAHP) and multi-attributive border approximation area comparison (MABAC). Using the GIS-FAHP-MABAC method, we found that Site D emerges as the most suitable (suitability score of 0.86). The methodology integrates spatial, technical, and socio-economic factors to evaluate research sites while considering power system constraints and projected demand growth. Simulation results demonstrated that the proposed approach enhances load distribution, reduces grid stress, and improves accessibility compared to conventional planning methods. The findings provide actionable insights for policymakers and urban planners to accelerate sustainable EV adoption while supporting renewable energy integration and reducing dependence on fossil fuels. This paper examined the many factors that influence the business model and load profile of EV charging stations in Kota City, as well as the proportions of their charging voltage (CV) and charging current (CC) for certain stochastic distributions of the various components. The suggested business model for EV charging stations, which includes all stakeholders, can enable car owners to travel at a lower cost.

References

[1]	Bi H, Gu Y, Lu F, et al. (2025) Site selection of electric vehicle charging station expansion based on GIS-FAHP-MABAC. Cleaner Prod 507: 145557. https://doi.org/10.1016/j.jclepro.2025.145557 doi: 10.1016/j.jclepro.2025.145557
[2]	Saldarini A, Barelli L, Pelosi D, et al. (2022) Different demand for charging infrastructure along a stretch of highway: Italian case study. 2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I & CPS Europe), 1–6. https://doi.org/10.1109/EEEIC/ICPSEurope54979.2022.9854643
[3]	Teawnarong A, Angaphiwatchawal P, Sompoh C, et al. (2022) Optimal size of EV charging stations in distribution system with consideration of bus voltage and line capacity limits. 2022 19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 1–4. https://doi.org/10.1109/ECTI-CON54298.2022.9795541
[4]	Zhang Y, Xu T, Chen T, et al. (2025) A high-resolution electric vehicle charging transaction dataset with multidimensional features in China. Sci Data 12: 643. https://doi.org/10.1038/s41597-025-04982-1 doi: 10.1038/s41597-025-04982-1
[5]	Guo Z, You L, Zhu R, et al. (2025) A city-scale and harmonized dataset for global electric vehicle charging demand analysis. Sci Data 12: 1254. https://doi.org/10.1038/s41597-025-05584-7 doi: 10.1038/s41597-025-05584-7
[6]	Guerrero-Silva JA, Romero-Gelvez JI, Aristizábal AJ, et al. (2025) Optimization and trends in EV charging infrastructure: A PCA-based systematic review. World Electr Veh J 16: 345. https://doi.org/10.3390/wevj16070345 doi: 10.3390/wevj16070345
[7]	Yuan N, Yu Z, Zhang Y, et al. (2022) Review of electric vehicle ultra-fast DC charging station. 7th Asia Conference on Power and Electrical Engineering (ACPEE), 1–9. https://doi.org/10.1109/ACPEE53904.2022.9783995
[8]	De Faveri F, Makohin DG, Belin PR, et al. (2022) Evolution of electric mobility in brazil and study of charging infrastructure to meet the expected demand. 2022 Intermountain Engineering, Technology and Computing (IETC), 1–6. https://doi.org/10.1109/IETC54973.2022.9796751
[9]	Baek K, Lee E, Kim J (2024) A dataset for multi-faceted analysis of electric vehicle charging transactions. Sci Data 11: 262. https://doi.org/10.1038/s41597-024-02942-9 doi: 10.1038/s41597-024-02942-9
[10]	Güven A, Ateş N, Alotaibi S, et al. (2025) Sustainable hybrid systems for electric vehicle charging infrastructures in regional applications. Sci Rep 15: 4199. https://doi.org/10.1038/s41598-025-87985-7 doi: 10.1038/s41598-025-87985-7
[11]	Hussain S, Sharma SK (2024) Electricity demand forecasting for residential community: A comparative study of RF, KNN and DT models. J Electr Syst, 20. https://doi.org/10.52783/jes.8579 doi: 10.52783/jes.8579
[12]	Hussain S, Prajapati RK, Kumar M, et al. (2025) Sustainable solar energy policies: Significance and impact for sustainable development. Int J Environ Sci 11: 1535–1546. https://doi.org/10.64252/8eegd302 doi: 10.64252/8eegd302
[13]	Manousakis NM, Karagiannopoulos PS, Tsekouras GJ, et al. (2023) Integration of renewable energy and electric vehicles in power systems: A review. Processes 11: 1544. https://doi.org/10.3390/pr11051544 doi: 10.3390/pr11051544
[14]	Monteiro A, Filho AVML, Dantas NKL, et al. (2025) Integrating battery energy storage systems for sustainable EV charging infrastructure. World Electr Veh J 16: 147. https://doi.org/10.3390/wevj16030147 doi: 10.3390/wevj16030147
[15]	Nikkhah MH, Samadi M (2022) Evaluating the effect of electric vehicle charging station locations on line flows: An analytical approach. 2022 30th International Conference on Electrical Engineering. https://doi.org/10.1109/ICEE55646.2022.9827453
[16]	Liu L, Qi X, Liu Y, et al. (2022) Research and application of a combined energy management strategy of EV charging station with PV and BES. 2022 International Symposium on Electrical, Electronics and Information Engineering (ISEEIE), 287–291. https://doi.org/10.1109/ISEEIE55684.2022.00046
[17]	Huang W, Wang J, Wang J, et al. (2024) EV charging load profile identification and seasonal difference analysis via charging sessions data of charging stations. Energy 288: 129771. https://doi.org/10.1016/j.energy.2023.129771 doi: 10.1016/j.energy.2023.129771
[18]	Jones CR, Elgueta H, Chudasama N, et al. (2024) Modelling public intentions to use innovative EV chargers employing hybrid energy storage systems: A UK case study based upon the technology acceptance model. Energies 17: 1405. https://doi.org/10.3390/en17061405 doi: 10.3390/en17061405
[19]	Saeseiw C, Pongpri K, Kaewchum T, et al. (2025) Power management for V2G and V2H operation modes in single-phase PV/BES/EV hybrid energy system. World Electr Veh J 16: 580. https://doi.org/10.3390/wevj16100580 doi: 10.3390/wevj16100580
[20]	Ronanki D, Karneddi H (2023) Electric vehicle charging infrastructure: Review, cybersecurity considerations, potential impacts, countermeasures, and future trends. IEEE J Emerg Sel Top Power Electron 12: 242–256. https://doi.org/10.1109/JESTPE.2023.3336997 doi: 10.1109/JESTPE.2023.3336997
[21]	Nugroho RI, Gnann T, Speth D, et al. (2025) Designing optimal fast-charging infrastructure for various electric vehicle ranges in emerging-market city. Transp Res Interdiscip Perspect 31: 101470. https://doi.org/10.1016/j.trip.2025.101470 doi: 10.1016/j.trip.2025.101470
[22]	Bohara R, Ross M, Joglekar O (2025) Cybersecurity mesh architecture for electric vehicle charging infrastructure. 2025 IEEE International Conference on Cyber Security and Resilience (CSR), 440–446. https://doi.org/10.1109/CSR64739.2025.11129993
[23]	Yu Q, Que T, Cushing LJ, et al. (2025) Equity and reliability of public electric vehicle charging stations in the United States. Nat Commun 16: 5291. https://doi.org/10.1038/s41467-025-60091-y doi: 10.1038/s41467-025-60091-y
[24]	Liu H, Lu C, Hao X, et al. (2024) Optimal performance selection of sustainable mobility service projects based on IFSS—Prospect theory—VIKOR: A case study of electric vehicle sharing program. PLoS One 19: e0309512. https://doi.org/10.1371/journal.pone.0309512 doi: 10.1371/journal.pone.0309512
[25]	Degen F, Schütte M (2022) Life cycle assessment of the energy consumption and GHG emissions of state-of-the-art automotive battery cell production. J Clean Prod 330: 129798. https://doi.org/10.1016/j.jclepro.2021.129798 doi: 10.1016/j.jclepro.2021.129798
[26]	Daneshzand F, Coker PJ, Potter B, et al. (2023) EV smart charging: How tariff selection influences grid stress and carbon reduction. Appl Energy 348: 121482. https://doi.org/10.1016/j.apenergy.2023.121482 doi: 10.1016/j.apenergy.2023.121482
[27]	Ghanbari Motlagh S, Oladigbolu J, Li L (2025) A review on electric vehicle charging station operation considering market dynamics and grid interaction. Appl Energy 392: 126058. https://doi.org/10.1016/j.apenergy.2025.126058 doi: 10.1016/j.apenergy.2025.126058
[28]	Tripathi A (2025) Integration of solar PV panels in electric vehicle charging infrastructure: Benefits, challenges, and environmental implications. Energy Sci Eng 13: 2135–2152. https://doi.org/10.1002/ese3.70014 doi: 10.1002/ese3.70014
[29]	Hao F (2025) Impact of electric vehicle charging demand on clean energy regional power grid control. Energy Inform 8: 83. https://doi.org/10.1186/s42162-025-00538-0 doi: 10.1186/s42162-025-00538-0
[30]	Verma R, Sharma SK, Singh P, et al. (2022) Analysis and sizing of charging stations in Kota City. Sustainability 14: 11759. https://doi.org/10.3390/su141811759 doi: 10.3390/su141811759
[31]	Khalife A, Fay TA, Göhlich D (2022) Optimizing public charging: An integrated approach based on GIS and multi-criteria decision analysis. World Electr Veh J 13: 131. https://doi.org/10.3390/wevj13080131 doi: 10.3390/wevj13080131
[32]	Ameer H, Wang Y, Fan X, et al. (2025) Hybrid optimization of EV charging station placement and pricing using Bender's decomposition and NSGA-Ⅱ algorithm. Appl Energy 397: 126385. https://doi.org/10.1016/j.apenergy.2025.126385 doi: 10.1016/j.apenergy.2025.126385
[33]	Cui J, Cao Y, Wang B, et al. (2025) Multi-stage adaptive expansion of EV charging stations considering impacts from the transportation network and power grid. Appl Energy 386: 125544. https://doi.org/10.1016/j.apenergy.2025.125544 doi: 10.1016/j.apenergy.2025.125544
[34]	Benmouna A, Borderiou L, Becherif M (2024) Charging stations for large-scale deployment of electric vehicles. Batteries 10: 33. https://doi.org/10.3390/batteries10010033 doi: 10.3390/batteries10010033
[35]	Zhang T, Huang Y, Liao H, et al. (2023) A hybrid electric vehicle load classification and forecasting approach based on GBDT algorithm and temporal convolutional network. Appl Energy 351: 121768. https://doi.org/10.1016/j.apenergy.2023.121768 doi: 10.1016/j.apenergy.2023.121768
[36]	Somefun TE, Longe OM (2025) Optimising the reliability of electric vehicle charging stations with vehicle-to-vehicle charging capabilities. Energy Rep 13: 4331–4344. https://doi.org/10.1016/j.egyr.2025.04.014 doi: 10.1016/j.egyr.2025.04.014
[37]	Alhazmi YA (2025) Electric vehicle battery swap stations: an overview and critical review. J Umm Al-Qura Univ Eng Archit. https://doi.org/10.1007/s43995-025-00215-z
[38]	Wu H (2022) A survey of battery swapping stations for electric vehicles: Operation modes and decision scenarios. IEEE Trans Intell Transp Syst 23: 10163–10185. https://doi.org/10.1109/TITS.2021.3125861 doi: 10.1109/TITS.2021.3125861
[39]	LaMonaca S, Ryan L (2022) The state of play in electric vehicle charging services—A review of infrastructure provision, players, and policies. Renewable Sustainable Energy Rev 154: 111733. https://doi.org/10.1016/j.rser.2021.111733 doi: 10.1016/j.rser.2021.111733
[40]	Kumari H, Lal S, Hussain S, et al. (2025) Simulation studies of grid-connected 1 kW wind energy power plant using MATLAB for renewable energy building in RTU Kota, India. Int J Sci Eng Invention 11: 37–51. https://doi.org/10.23958/ijsei/vol11-i03/282 doi: 10.23958/ijsei/vol11-i03/282
[41]	Mandal B, Mondal S (2025) Harnessing GIS-based hybrid MCDM techniques for optimal electric vehicle charging sites selection: Bridging the urban-rural divide in a metropolitan region of the Global South. Smart Constr Sustain Cities 3: 26. https://doi.org/10.1007/s44268-025-00074-6 doi: 10.1007/s44268-025-00074-6
[42]	Bogdanov D, Breyer C (2024) Role of smart charging of electric vehicles and vehicle-to-grid in integrated renewables-based energy systems on country level. Energy 301: 131635. https://doi.org/10.1016/j.energy.2024.131635 doi: 10.1016/j.energy.2024.131635
[43]	Zhan W, Liao Y, Deng J, et al. (2025) Large-scale empirical study of electric vehicle usage patterns and charging infrastructure needs. npj Sustain Mobil Transp 2: 9. https://doi.org/10.1038/s44333-024-00023-3 doi: 10.1038/s44333-024-00023-3
[44]	Sambasivam B, Sundararaman M (2023) Evaluating the impact of passenger electric vehicle adoption on high renewable resources electricity grid. Resour Conserv Recycl Adv 20: 200193. https://doi.org/10.1016/j.rcradv.2023.200193 doi: 10.1016/j.rcradv.2023.200193
[45]	Hussain S, Sharma SK, Lal S (2024) Feasible synergy between hybrid solar PV and wind system for energy supply of a green building in Kota (India): A case study using iHOGA. Energy Convers Manag 315: 118783. https://doi.org/10.1016/j.enconman.2024.118783 doi: 10.1016/j.enconman.2024.118783
[46]	Damianakis N, Mouli GRC, Bauer P, et al. (2023) Assessing the grid impact of electric vehicles, heat pumps and PV generation in Dutch LV distribution grids. Appl Energy 352: 121878. https://doi.org/10.1016/j.apenergy.2023.121878 doi: 10.1016/j.apenergy.2023.121878

Reader Comments

Your name:*

Email:*
© 2025 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)