Transitioning toward efficient smart charging of electric vehicles: Challenges and perspectives

Abstract

Rapid diffusion of electric vehicles (EVs) is central to global decarbonization strategies. Yet, large-scale uncoordinated charging threatens the stability of the distribution grid, increases congestion costs, and can erode the environmental benefits of electrified mobility. Smart charging has therefore emerged as a critical paradigm for aligning mobility demand with power system constraints through adaptive, information-driven coordination. This commentary develops a comprehensive operations-management–oriented perspective on EV smart charging as a complex socio-techno-economic system shaped by interacting physical infrastructures, digital platforms, market mechanisms, and heterogeneous human behavior. We synthesize existing research into an integrated framework that distinguishes between a physical layer (vehicles, charging infrastructure, grids, and mobility needs) and a digital layer (data exchange, coordination, and service provision), and organize the landscape into five interdependent ecosystems: electricity, charging, mobility services, users, and regulation. Building on this framework, we articulate five key research opportunities: (i) coordination of heterogeneous stakeholders with conflicting objectives; (ii) scalable and flexible control strategies; (iii) strategic infrastructure and investment planning under uncertainty; (iv) effective governance and policy design, and (v) modeling EV user preferences and behavior. We highlight how addressing these opportunities can enhance system efficiency, reliability, sustainability, and user acceptance. By positioning smart charging at the intersection of energy and mobility systems and emphasizing the central role of digital-physical integration, this commentary provides a unifying lens and a forward-looking research agenda for scholars seeking to contribute to the design of smart, sustainable urban mobility and power systems.

Graphical abstract

Keywords

electric vehicles / smart charging / energy artificial intelligence / sustainability / transportation

References

[1] Ahadi R, Ketter W, Collins J, Daina N 2021. Siting and sizing of charging infrastructure for shared autonomous electric fleets. In: Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems London: IFAAMAS, 88–96

[2] Ahadi R, Ketter W, Collins J, Daina N, (2023). Cooperative learning for smart charging of shared autonomous vehicle fleets. Transportation Science, 57( 3): 613–630

[3] Anderson J E, Bergfeld M, Nguyen D M, Steck F, (2023). Real-world charging behavior and preferences of electric vehicles users in Germany. International Journal of Sustainable Transportation, 17( 9): 1032–1046

[4] Attig C, Schrills T, Gödker M, Wollstadt P, Wiebel-Herboth C, Calero Valdez A, Franke T, (2023). Enhancing trust in smart charging agents—the role of traceability for human-agent-cooperation. In: HCI International 2023— Late Breaking Papers. Copenhagen: Springer, 14059: 313–324

[5] Barua A, Mukherjee R, (2021). Multi-homing revisited: Level of adoption and competitive strategies. Management Information Systems Quarterly, 45( 2): 897–924

[6] Becker H, Balac M, Ciari F, Axhausen K W, (2020). Assessing the welfare impacts of shared mobility and mobility as a service (MaaS). Transportation Research Part A, Policy and Practice, 131: 228–243

[7] Beverungen D, Breidbach C F, Poeppelbuss J, Tuunainen V K, (2019). Smart service systems: An interdisciplinary perspective. Information Systems Journal, 29( 6): 1201–1206

[8] Bichler M, Gupta A, Ketter W, (2010). Research commentary—designing smart markets. Information Systems Research, 21( 4): 688–699

[9] Choi T M, Kumar S, Yue X, Chan H L, (2022). Disruptive technologies and operations management in the Industry 4.0 era and beyond. Production and Operations Management, 31( 1): 9–31

[10] de Palma A, Lindsey R, (2011). Traffic congestion pricing methodologies and technologies. Transportation Research Part C, Emerging Technologies, 19( 6): 1377–1399

[11] Dean M D, Kockelman K M, (2024). Americans’ opinions and interests in plug-in electric vehicle smart charging programs. Transportation Research Part D, Transport and Environment, 129: 104129

[12] DeForest N, MacDonald J S, Black D R, (2018). Day ahead optimization of an electric vehicle fleet providing ancillary services in the Los Angeles Air Force Base Vehicle-to-Grid Demonstration. Applied Energy, 210: 987–1001

[13] Dietvorst B J, Simmons J P, Massey C, (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology. General, 144( 1): 114–126

[14] Doying R, Goggin M, Sherman A, (2023). Transmission congestion costs rise again in U.S. RTOS..

[15] El Helou R, Sivaranjani S, Kalathil D, Schaper A, Xie L, (2022). The impact of heavy-duty vehicle electrification on large power grids: A synthetic Texas case study. Advances in Applied Energy, 6: 100093

[16] Englberger S, Abo Gamra K, Tepe B, Schreiber M, Jossen A, Hesse H, (2021). Electric vehicle multi-use: Optimizing multiple value streams using mobile storage systems in a vehicle-to-grid context. Applied Energy, 304: 117862

[17] European Commission (2024). Climate action: Leading the way: From plans to implementation for a green and competitive Europe: Progress report 2024. Luxembourg: European Commission Publications Office, doi:10.2834/8000893

[18] Fagnant D J, Kockelman K M, (2014). The travel and environmental implications of shared autonomous vehicles, using agent-based model scenarios. Transportation Research Part C, Emerging Technologies, 40: 1–13

[19] Fotouhi Z, Hashemi M R, Narimani H, Bayram I S, (2019). A general model for EV drivers’ charging behavior. IEEE Transactions on Vehicular Technology, 68( 8): 7368–7382

[20] Froger A, Jabali O, Mendoza J E, Laporte G, (2022). The electric vehicle routing problem with capacitated charging stations. Transportation Science, 56( 2): 460–482

[21] Greaker M, (2021). Optimal regulatory policies for charging of electric vehicles. Transportation Research Part D, Transport and Environment, 97: 102922

[22] Gschwendtner C, Knoeri C, Stephan A, (2025). The role of behavioral heterogeneity in incentivizing the spatial-temporal flexibility of electric vehicle charging.. Environmental Research: Energy,

[23] Gunapriya B, Kumar B S, Rajalakshmi B, Palanisamy K, (2025). Cost-efficient energy management between electric vehicle charging stations and distribution systems using intelligent scheduling. Energy, 339: 138687

[24] Guo X, Haupt A, Wang H, Qadri R, Zhao J, (2023). Understanding multi-homing and switching by platform drivers. Transportation Research Part C, Emerging Technologies, 154: 104233

[25] He L, Ma G, Qi W, Wang X, (2020). Charging an electric vehicle-sharing fleet.. Manufacturing & Service Operations Management,

[26] Henriksen I M, Throndsen W, Ryghaug M, Skjølsvold T M, (2021). Electric vehicle charging and end-user motivation for flexibility: A case study from Norway. Energy, Sustainability and Society, 11( 1): 44

[27] IEA (2025a). IEA Reports: Energy and AI. Available at the website of iea.org/reports/energy-and-ai

[28] IEA (2025b). IEA Reports: Global EV outlook 2025. Available at the website of iea.org/reports/global-ev-outlook-2025

[29] Kaddoura I, Bischoff J, Nagel K, (2020). Towards welfare optimal operation of innovative mobility concepts: External cost pricing in a world of shared autonomous vehicles. Transportation Research Part A, Policy and Practice, 136: 48–63

[30] Kahlen M, Schroer K, Ketter W, Gupta A, (2023). Smart markets for real-time allocation of multiproduct resources: The case of shared electric vehicles.. Information Systems Research,

[31] Kataoka R, Ogimoto K, Iwafune Y, Nishi T, (2024). Does BEV always help to reduce CO₂ emission? Impact of charging strategy. Transportation Research Part D, Transport and Environment, 129: 104124

[32] Ketter W, Collins J, Saar-Tsechansky M, Marom O, (2018). Information systems for a smart electricity grid: Emerging challenges and opportunities. ACM Transactions on Management Information Systems, 9( 3): 1–22

[33] Ketter W, Schroer K, Valogianni K, (2023). Information systems research for smart sustainable mobility: A framework and call for action. Information Systems Research, 34( 3): 1045–1065

[34] Kienscherf P A, Lu Y, He L, Ketter W, (2024). Spatial competition in fast-charging networks. SSRN Electronic Journal,

[35] Krzyzanowski M, Kuna-Dibbert B, Schneider J, eds (2005). Health Effects of Transport-Related Air Pollution. Copenhagen: World Health Organization Europe

[36] Kubli M, (2022). EV drivers’ willingness to accept smart charging: Measuring preferences of potential adopters. Transportation Research Part D, Transport and Environment, 109: 103396

[37] Kullman N D, Cousineau M, Goodson J C, Mendoza J E, (2022). Dynamic ride-hailing with electric vehicles. Transportation Science, 56( 3): 775–794

[38] Langbroek J H M, Franklin J P, Susilo Y O, (2016). The effect of policy incentives on electric vehicle adoption. Energy Policy, 94: 94–103

[39] Li P, Jiang M, Zhang Y, Zhang Y, (2023). Cooperative optimization of bus service and charging schedules for a fast-charging battery electric bus network. IEEE Transactions on Intelligent Transportation Systems, 24( 5): 5362–5375

[40] Li X, Jenn A, (2022). Energy, emissions, and cost impacts of charging price strategies for electric vehicles. Environmental Science & Technology, 56( 9): 5724–5733

[41] Li X, Wang Z, Zhang L, Sun F, Cui D, Hecht C, Figgener J, Sauer D U, (2023). Electric vehicle behavior modeling and applications in vehicle-grid integration: An overview. Energy, 268: 126647

[42] Li Y, Xie K, Wang L, Xiang Y, (2018). The impact of PHEVs charging and network topology optimization on bulk power system reliability. Electric Power Systems Research, 163: 85–97

[43] Liang Y, Ding Z, Ding T, Lee W J, (2021). Mobility-aware charging scheduling for shared on-demand electric vehicle fleet using deep reinforcement learning. IEEE Transactions on Smart Grid, 12( 2): 1380–1393

[44] Lu Y, Liang Y, Ding Z, Wu Q, Ding T, Lee W J, (2022). Deep reinforcement learning-based charging pricing for autonomous mobility-on-demand system. IEEE Transactions on Smart Grid, 13( 2): 1412–1426

[45] Madzharov D, Delarue E, D’haeseleer W, (2014). Integrating electric vehicles as flexible load in unit commitment modeling. Energy, 65: 285–294

[46] Mazzei M, Michelotti C, ((2025)). Smart data and decision support system for local optimization of electric charging stations. IEEE Access,

[47] McCarthy JGrayson NBrule JCottle TDinerman ADombrowski JLong JTran HQuigg KThompson MTownsend AUrlaub N (2023). Cybersecurity framework profile for electric vehicle extreme fast charging infrastructure. Gaithersburg: National Institute of Standards Technology (NIST)

[48] Metais M O, Jouini O, Perez Y, Berrada J, Suomalainen E, (2022). Too much or not enough? Planning electric vehicle charging infrastructure: A review of modeling options. Renewable & Sustainable Energy Reviews, 153: 111719

[49] Müller W, Leyer M, Degirmenci K, Barros A, (2025). Designing EV charging networks from a city perspective: A conceptual analysis. Energy Policy, 198: 114516

[50] Najafi A, Gao K, Parishwad O, Pourakbari-Kasmaei M, Precup R E, Roman R C, (2025). Integrated planning, operation and optimization of coupled transportation and energy systems.. Multimodal Transportation,

[51] Noel L, Brodie J F, Kempton W, Archer C L, Budischak C, (2017). Cost minimization of generation, storage, and new loads, comparing costs with and without externalities. Applied Energy, 189: 110–121

[52] Orfanoudakis S, Robu V, Salazar E M, Palensky P, Vergara P P, (2025). Scalable reinforcement learning for large-scale coordination of electric vehicles using graph neural networks. Communications Engineering, 4( 1): 118

[53] Pal A, Bhattacharya A, Chakraborty A K, (2023). Planning of EV charging station with distribution network expansion considering traffic congestion and uncertainties. IEEE Transactions on Industry Applications, 59( 3): 3810–3825

[54] Pantelidis T P, Li L, Ma T Y, Chow J Y J, Jabari S E G, (2022). A node-charge graph-based online carshare rebalancing policy with capacitated electric charging. Transportation Science, 56( 3): 654–676

[55] Powell W B, (2014). Energy and uncertainty: Models and algorithms for complex energy systems. AI Magazine, 35( 3): 8–21

[56] Qi W, Sha M, Li S, (2022). When shared autonomous electric vehicles meet microgrids: Citywide energy-mobility orchestration. Manufacturing & Service Operations Management, 24( 5): 2389–2406

[57] Roni M S, Yi Z, Smart J G, (2019). Optimal charging management and infrastructure planning for free-floating shared electric vehicles. Transportation Research Part D, Transport and Environment, 76: 155–175

[58] Rossi F, Zhang R, Hindy Y, Pavone M, (2018). Routing autonomous vehicles in congested transportation networks: Structural properties and coordination algorithms. Autonomous Robots, 42( 7): 1427–1442

[59] Saber H, Ehsan M, Moeini-Aghtaie M, Fotuhi-Firuzabad M, Lehtonen M, ((2020). Network-constrained transactive coordination for plug-in electric vehicles participation in real-time retail electricity markets. IEEE Transactions on Sustainable Energy,

[60] Saber H, Ehsan M, Moeini-Aghtaie M, Ranjbar H, Lehtonen M, (2022). A user-friendly transactive coordination model for residential prosumers considering voltage unbalance in distribution networks. IEEE Transactions on Industrial Informatics, 18( 9): 5748–5759

[61] Saber H, Ranjbar H, Fattaheian-Dehkordi S, Moeini-Aghtaie M, Ehsan M, Shahidehpour M, (2022). Transactive energy management of V2G-capable electric vehicles in residential buildings: An MILP approach. IEEE Transactions on Sustainable Energy, 13( 3): 1734–1743

[62] Saber H, Ranjbar H, Hajipour E, Shahidehpour M, (2024). Two-stage coordination scheme for multiple EV charging stations connected to an exclusive DC feeder considering grid-tie converter limitation. IEEE Transactions on Transportation Electrification, 10( 4): 8213–8223

[63] Saberi-Beglar K, Zare K, Seyedi H, Marzband M, Nojavan S, (2023). Risk-embedded scheduling of a CCHP integrated with electric vehicle parking lot in a residential energy hub considering flexible thermal and electrical loads. Applied Energy, 329: 120265

[64] Saner C B, Saha J, Srinivasan D, (2023). A charge curve and battery management system aware optimal charging scheduling framework for electric vehicle fast charging stations with heterogeneous customer mix. IEEE Transactions on Intelligent Transportation Systems, 24( 12): 14890–14902

[65] Schexnider T, (2023). Roadways for people: Rethinking transportation planning and engineering. Sustainability and Climate Change, 16( 6): 432–434

[66] Schroer K, Ahadi R, Ketter W, Lee T Y, (2025). Data-driven planning of large-scale electric vehicle charging hubs using deep reinforcement learning. Transportation Research Part C, Emerging Technologies, 177: 105126

[67] Schwartz B 2016. The Paradox of Choice: Why More is Less. New York: ECCO Press

[68] Shafie-Khah M, Siano P, Fitiwi D Z, Mahmoudi N, Catalao J P S, (2018). An innovative two-level model for electric vehicle parking lots in distribution systems with renewable energy. IEEE Transactions on Smart Grid, 9( 2): 1506–1520

[69] Shahriar S, Al-Ali A R, Osman A H, Dhou S, Nijim M, (2020). Machine learning approaches for ev charging behavior: A review. IEEE Access: Practical Innovations, Open Solutions, 8: 168980–168993

[70] Siddique C, Afifah F, Guo Z, Zhou Y, (2022). Data mining of plug-in electric vehicles charging behavior using supply-side data. Energy Policy, 161: 112710

[71] Simon H, (1972). Theories of bounded rationality. Decision and Organization, 1( 1): 161–176

[72] Sovacool B K, Noel L, Axsen J, Kempton W, (2018). The neglected social dimensions to a vehicle-to-grid (V2G) transition: A critical and systematic review. Environmental Research Letters, 13( 1): 013001

[73] Sund L, Muires J, Ahadi R, Valogianni K, Ketter W (2025). Coordinating competing electric vehicle fleets: An agent-based charging capacity market. In: Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems Detroit: IFAAMAS, 2759–2761

[74] Talari S, Shafie-Khah M, Siano P, Loia V, Tommasetti A, Catalão J P S, (2017). A review of smart cities based on the internet of things concept. Energies, 10( 4): 421

[75] Tao Y, Qiu J, Lai S, Sun X, Zhao J, (2022). Adaptive integrated planning of electricity networks and fast charging stations under electric vehicle diffusion. IEEE Transactions on Power Systems,

[76] Taufique K M R, Nielsen K S, Dietz T, Shwom R, Stern P C, Vandenbergh M P, (2022). Revisiting the promise of carbon labelling. Nature Climate Change, 12( 2): 132–140

[77] Ter-Gazarian A Electric Vehicles as Distributed Energy Sources and Storage (2020). In: Energy Storage for Power Systems. 3rd ed. London: Institution of Engineering and Technology, 277–286

[78] Tian J, Jia H, Wang G, Huang Q, Wu R, Gao H, Liu C, (2024). Integrated optimization of charging infrastructure, fleet size and vehicle operation in shared autonomous electric vehicle system considering vehicle-to-grid. Renewable Energy, 229: 120760

[79] Unterweger A, Knirsch F, Engel D, Musikhina D, Alyousef A, de Meer H, (2022). An analysis of privacy preservation in electric vehicle charging. Energy Informatics, 5( 1): 3

[80] Valogianni K, Ketter W, Collins J, Adomavicius G, (2025). Toward sustainable electricity markets: Capacity-based pricing for electric vehicle smart charging.. Information Systems Research,

[81] Valogianni K, Ketter W, Collins J, Zhdanov D, (2020). Sustainable electric vehicle charging using adaptive pricing. Production and Operations Management, 29( 6): 1550–1572

[82] Varma S MCastro FMaguluri S T (2025). Electric vehicle fleet and charging infrastructure planning. Preprint at arXiv. arXiv:2306.10178 [math.OC]

[83] Vendé P, Kergosien Y, Mendoza J E, (2025). Optimizing fast charger location for hybrid electric bus transit networks. Transportation Research Part C, Emerging Technologies, 178: 105175

[84] Verzijlbergh R A, Grond M O W, Lukszo Z, Slootweg J G, Ilic M D, (2012). Network impacts and cost savings of controlled EV charging. IEEE Transactions on Smart Grid, 3( 3): 1203–1212

[85] Wang Y, Yao E, Pan L, (2021). Electric vehicle drivers’ charging behavior analysis considering heterogeneity and satisfaction. Journal of Cleaner Production, 286: 124982

[86] Welzel F, Klinck C F, Pohlmann Y, Bednarczyk M, (2021). Grid and user-optimized planning of charging processes of an electric vehicle fleet using a quantitative optimization model. Applied Energy, 290: 116717

[87] Wolf S, Teitge T, Mielke J, Schütze F, Jaeger C, (2021). The European Green Deal—more than climate neutrality. Inter Economics, 56( 2): 99–107

[88] Wong S D, Shaheen S A, Martin E, Uyeki R, (2023). Do incentives make a difference? Understanding smart charging program adoption for electric vehicles. Transportation Research Part C, Emerging Technologies, 151: 104123

[89] Wu O Q, Yücel Ş, Zhou Y, ((2021)). Smart charging of electric vehicles: An innovative business model for utility firms.. Manufacturing & Service Operations Management,

[90] Wu T, Wei X, Zhang X, Wang G, Qiu J, Xia S, (2022). Carbon-oriented expansion planning of integrated electricity-natural gas systems with ev fast-charging stations. IEEE Transactions on Transportation Electrification, 8( 2): 2797–2809

[91] Wu X, Gong J, Greenwood B, Song Y, (2019). No longer riding dirty: The effect of electric vehicle subsidies on automobile markets.. SSRN Electronic Journal,

[92] Yang J, Levin M W, Hu L, Li H, Jiang Y, (2023). Fleet sizing and charging infrastructure design for electric autonomous mobility-on-demand systems with endogenous congestion and limited link space. Transportation Research Part C, Emerging Technologies, 152: 104172

[93] Ye Q, Bansal P, Adey B T, (2025). Strategic planning of charging infrastructure for shared electric vehicles: A multi-phase stochastic approach using reinforcement learning. Sustainable Cities and Society, 133: 106850

[94] Yoo Y, Henfridsson O, Lyytinen K, (2010). Research commentary—the new organizing logic of digital innovation: An agenda for information systems research. Information Systems Research, 21( 4): 724–735

[95] Zavvos E, Gerding E H, Brede M, (2022). A comprehensive game-theoretic model for electric vehicle charging station competition. IEEE Transactions on Intelligent Transportation Systems, 23( 8): 12239–12250

[96] Zhao A P, Li S, Li Z, Wang Z, Fei X, Hu Z, Alhazmi M, Yan X, Wu C, Lu S, Xiang Y, Xie D, ((2024)). Electric vehicle charging planning: A complex systems perspective.. IEEE Transactions on Smart Grid,

[97] Zhao T, Li Y, Pan X, Wang P, Zhang J, (2018). Real-time optimal energy and reserve management of electric vehicle fast charging station: Hierarchical game approach. IEEE Transactions on Smart Grid, 9( 5): 5357–5370

[98] Zhou S, Zhuang Y, Wu Z, Gu W, Yu P, Du J, Luo X, (2020). Planning and real-time pricing of EV charging stations considering social welfare and profitability balance.. CSEE Journal of Power and Energy Systems,

[99] Zhu J, Nacmanson W J, Ochoa L F, Hellyer B, (2022). Assessing the EV hosting capacity of Australian urban and rural MV-LV networks. Electric Power Systems Research, 212: 108399

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