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Shanghai as a Model: Research on the Journey of Transportation Electrification and Charging Infrastructure Development

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  • Cong Zhang

    (School of Automotive Engineering, Harbin Institute of Technology, Weihai 264209, China)

  • Jingchao Lian

    (School of Automotive Engineering, Harbin Institute of Technology, Weihai 264209, China)

  • Haitao Min

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China)

  • Ming Li

    (Shandong Inspur Database Technology Co., Ltd., Jinan 250101, China)

Abstract

As the world pivots to a greener paradigm, Shanghai emerges as an archetype in the sustainable urban transit narrative, particularly through the aggressive expansion and refinement of its electric vehicle (EV) charging infrastructure. This scholarly article provides a comprehensive examination of the current state of charging infrastructure in Shanghai, highlighting the challenges that the existing infrastructure may face in light of the burgeoning electric vehicle market. This paper delves into the strategic development approaches adopted by Shanghai to address these challenges, particularly emphasizing the expansion of high-power charging infrastructure to meet the anticipated increase in future electric vehicle charging demands. It also discusses the implementation of co-construction and sharing models, the enhancement of interconnectivity and standardized management of charging facilities, and the continuous improvement and strengthening of infrastructure construction and operations. Furthermore, this article explores the implementation of time-of-use electricity pricing policies and the ongoing conduct of demand response activities, which are instrumental in creating conditions for vehicle-to-grid interaction. The aim of our presentation is to foster a keen understanding among policymakers, industry stakeholders, and urban planners of the mechanisms necessary to effectively navigate the emerging electric vehicle market, thereby encouraging harmonious development between metropolises and transportation systems. Future research endeavors should delve into the realms of fast-charging technologies, intelligent operation and maintenance of charging infrastructure, and vehicle-to-grid interaction technologies. These areas of study are pivotal in fostering the harmonious development of electric vehicles (EVs) and their charging infrastructure, thereby aligning with the dual objectives of advancing urban transportation systems and sustainable green city development. The findings presented herein offer valuable insights for policymakers, urban planners, and industry leaders, guiding them in crafting informed strategies that not only address the immediate needs of the EV market but also lay the groundwork for a scalable and resilient charging infrastructure, poised to support the long-term vision of sustainable urban mobility.

Suggested Citation

  • Cong Zhang & Jingchao Lian & Haitao Min & Ming Li, 2024. "Shanghai as a Model: Research on the Journey of Transportation Electrification and Charging Infrastructure Development," Sustainability, MDPI, vol. 17(1), pages 1-22, December.
  • Handle: RePEc:gam:jsusta:v:17:y:2024:i:1:p:91-:d:1553981
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    References listed on IDEAS

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    1. Elżbieta Macioszek & Maria Cieśla & Anna Granà, 2023. "Future Development of an Energy-Efficient Electric Scooter Sharing System Based on a Stakeholder Analysis Method," Energies, MDPI, vol. 16(1), pages 1-24, January.
    2. Crozier, Constance & Morstyn, Thomas & McCulloch, Malcolm, 2020. "The opportunity for smart charging to mitigate the impact of electric vehicles on transmission and distribution systems," Applied Energy, Elsevier, vol. 268(C).
    3. Heinisch, Verena & Göransson, Lisa & Erlandsson, Rasmus & Hodel, Henrik & Johnsson, Filip & Odenberger, Mikael, 2021. "Smart electric vehicle charging strategies for sectoral coupling in a city energy system," Applied Energy, Elsevier, vol. 288(C).
    4. Heredia, Willy Bernal & Chaudhari, Kalpesh & Meintz, Andrew & Jun, Myungsoo & Pless, Shanti, 2020. "Evaluation of smart charging for electric vehicle-to-building integration: A case study," Applied Energy, Elsevier, vol. 266(C).
    5. Oluwagbenga Apata & Pitshou N. Bokoro & Gulshan Sharma, 2023. "The Risks and Challenges of Electric Vehicle Integration into Smart Cities," Energies, MDPI, vol. 16(14), pages 1-25, July.
    6. Tuchnitz, Felix & Ebell, Niklas & Schlund, Jonas & Pruckner, Marco, 2021. "Development and Evaluation of a Smart Charging Strategy for an Electric Vehicle Fleet Based on Reinforcement Learning," Applied Energy, Elsevier, vol. 285(C).
    7. Trinko, David & Horesh, Noah & Porter, Emily & Dunckley, Jamie & Miller, Erika & Bradley, Thomas, 2023. "Transportation and electricity systems integration via electric vehicle charging-as-a-service: A review of techno-economic and societal benefits," Renewable and Sustainable Energy Reviews, Elsevier, vol. 175(C).
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