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Optimised Centralised Charging of Electric Vehicles Along Motorways

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  • Ekaterina Dudkina

    (Department of Energy, Systems, Territory and Construction Engineering, University of Pisa, 56122 Pisa, Italy)

  • Claudio Scarpelli

    (Department of Energy, Systems, Territory and Construction Engineering, University of Pisa, 56122 Pisa, Italy)

  • Valerio Apicella

    (Research & Development and Innovation, Movyon SpA—Gruppo Autostrade per l’Italia, 50123 Firenze, Italy)

  • Massimo Ceraolo

    (Department of Energy, Systems, Territory and Construction Engineering, University of Pisa, 56122 Pisa, Italy)

  • Emanuele Crisostomi

    (Department of Energy, Systems, Territory and Construction Engineering, University of Pisa, 56122 Pisa, Italy)

Abstract

Nowadays, when battery-powered electric vehicles (EVs) travel along motorways, their drivers decide where to recharge their cars’ batteries with no or scarce information on the occupancy status of the next charging stations. While this may still be acceptable in most countries, due to the limited number of EVs on motorways, long queues may build-up in the coming years with increased electric mobility, unless smart allocation strategies are designed and implemented. For instance, as we shall investigate in this manuscript, a centralised coordination of the charging strategies of individual EVs has the potential to significantly reduce the queuing time at charging stations. In particular, in this paper we explain how the charging problem on motorways can be modelled as an optimisation problem, we propose some strategies based on dynamic optimisation to solve it, and we explain how this may be implemented in practice using a centralised charge manager that exchanges information with the EVs and solves the optimisation problems. Finally, we compare in a realistic scenario the current decentralised recharging strategies with a centralised one, and we show that, under simplifying assumptions, queueing times can be reduced by more than 50%. Such a significant reduction allows one to greatly improve vehicular flows and general journey durations without requiring building new infrastructure. Reducing queuing times has a positive impact on traffic congestion and emissions, and the more geographically balanced energy demand of the proposed methodology mitigates energy consumption peaks.

Suggested Citation

  • Ekaterina Dudkina & Claudio Scarpelli & Valerio Apicella & Massimo Ceraolo & Emanuele Crisostomi, 2025. "Optimised Centralised Charging of Electric Vehicles Along Motorways," Sustainability, MDPI, vol. 17(12), pages 1-15, June.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:12:p:5668-:d:1683158
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    References listed on IDEAS

    as
    1. Zhou, Jianshu & Xiang, Yue & Zhang, Xin & Sun, Zhou & Liu, Xuefei & Liu, Junyong, 2025. "Optimal self-consumption scheduling of highway electric vehicle charging station based on multi-agent deep reinforcement learning," Renewable Energy, Elsevier, vol. 238(C).
    2. Wager, Guido & Whale, Jonathan & Braunl, Thomas, 2016. "Driving electric vehicles at highway speeds: The effect of higher driving speeds on energy consumption and driving range for electric vehicles in Australia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 63(C), pages 158-165.
    3. Zhou, Kaile & Cheng, Lexin & Wen, Lulu & Lu, Xinhui & Ding, Tao, 2020. "A coordinated charging scheduling method for electric vehicles considering different charging demands," Energy, Elsevier, vol. 213(C).
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