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A Study on Price-Based Charging Strategy for Electric Vehicles on Expressways

Author

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  • Lixing Chen

    (School of Electrical Engineering, Southeast University, No. 2 Sipailou, Nanjing 210096, China)

  • Zhong Chen

    (School of Electrical Engineering, Southeast University, No. 2 Sipailou, Nanjing 210096, China)

  • Xueliang Huang

    (School of Electrical Engineering, Southeast University, No. 2 Sipailou, Nanjing 210096, China)

  • Long Jin

    (School of Electrical Engineering, Southeast University, No. 2 Sipailou, Nanjing 210096, China)

Abstract

With the large-scale adoption of electric vehicles (EVs) on expressways, the exploration of a guiding-based charging method to effectively adjust interactions between EVs and the fast charging stations (CSs) is urgently needed. This paper proposes a status-of-use (SOU) price-based charging strategy that can motivate users to charge in advance. A queuing model for a CS cluster was established to verify the effectiveness of the strategy, and then a simulation of traveling and charging conditions of 12,000 pure EVs on the road network from 0:00 to 24:00 was performed according to the related data and using the Monte Carlo method, the Floyd-Warshall algorithm, and the queuing algorithm proposed in this paper. Compared to unordered charging (UC), SOU price-based charging can not only reduce the charging cost and waiting time for users, but also increase the utilization ratio of charging facilities in a CS cluster and thus lower their influence on the power grid and expressway traffic. SOU price-based charging can effectively adjust interactions between EVs and CSs.

Suggested Citation

  • Lixing Chen & Zhong Chen & Xueliang Huang & Long Jin, 2016. "A Study on Price-Based Charging Strategy for Electric Vehicles on Expressways," Energies, MDPI, vol. 9(5), pages 1-18, May.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:5:p:385-:d:70403
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    References listed on IDEAS

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    1. Yuqing Yang & Weige Zhang & Liyong Niu & Jiuchun Jiang, 2015. "Coordinated Charging Strategy for Electric Taxis in Temporal and Spatial Scale," Energies, MDPI, vol. 8(2), pages 1-17, February.
    2. Aziz, Muhammad & Oda, Takuya & Ito, Masakazu, 2016. "Battery-assisted charging system for simultaneous charging of electric vehicles," Energy, Elsevier, vol. 100(C), pages 82-90.
    3. Muhammad Aziz & Takuya Oda & Takashi Mitani & Yoko Watanabe & Takao Kashiwagi, 2015. "Utilization of Electric Vehicles and Their Used Batteries for Peak-Load Shifting," Energies, MDPI, vol. 8(5), pages 1-19, April.
    4. Zhu, Zhi-Hong & Gao, Zi-You & Zheng, Jian-Feng & Du, Hao-Ming, 2016. "Charging station location problem of plug-in electric vehicles," Journal of Transport Geography, Elsevier, vol. 52(C), pages 11-22.
    5. Shuo Zhang & Chengning Zhang & Rui Xiong & Wei Zhou, 2014. "Study on the Optimal Charging Strategy for Lithium-Ion Batteries Used in Electric Vehicles," Energies, MDPI, vol. 7(10), pages 1-15, October.
    6. Mingchao Xia & Qingying Lai & Yajiao Zhong & Canbing Li & Hsiao-Dong Chiang, 2016. "Aggregator-Based Interactive Charging Management System for Electric Vehicle Charging," Energies, MDPI, vol. 9(3), pages 1-14, March.
    7. Maigha & Mariesa L. Crow, 2014. "Economic Scheduling of Residential Plug-In (Hybrid) Electric Vehicle (PHEV) Charging," Energies, MDPI, vol. 7(4), pages 1-23, March.
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    Citations

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    Cited by:

    1. Lixing Chen & Xueliang Huang & Hong Zhang, 2020. "Modeling the Charging Behaviors for Electric Vehicles Based on Ternary Symmetric Kernel Density Estimation," Energies, MDPI, vol. 13(7), pages 1-17, March.
    2. Jun Yang & Wanmeng Hao & Lei Chen & Jiejun Chen & Jing Jin & Feng Wang, 2016. "Risk Assessment of Distribution Networks Considering the Charging-Discharging Behaviors of Electric Vehicles," Energies, MDPI, vol. 9(7), pages 1-20, July.
    3. Bong-Gi Choi & Byeong-Chan Oh & Sungyun Choi & Sung-Yul Kim, 2020. "Selecting Locations of Electric Vehicle Charging Stations Based on the Traffic Load Eliminating Method," Energies, MDPI, vol. 13(7), pages 1-20, April.
    4. Lixing Chen & Xueliang Huang & Zhong Chen & Long Jin, 2016. "Study of a New Quick-Charging Strategy for Electric Vehicles in Highway Charging Stations," Energies, MDPI, vol. 9(9), pages 1-20, September.
    5. Lixing Chen & Xueliang Huang & Hong Zhang & Yinsheng Luo, 2018. "A Study on Coordinated Optimization of Electric Vehicle Charging and Charging Pile Selection," Energies, MDPI, vol. 11(6), pages 1-16, May.
    6. Ivana Semanjski & Sidharta Gautama, 2016. "Forecasting the State of Health of Electric Vehicle Batteries to Evaluate the Viability of Car Sharing Practices," Energies, MDPI, vol. 9(12), pages 1-17, December.
    7. Zhenxing Li & Yang Gong & Lu Wang & Hong Tan & Prominent Lovet Kativu & Pengfei Wang, 2018. "A Regional Protection Partition Strategy Considering Communication Constraints and Its Implementation Techniques," Energies, MDPI, vol. 11(10), pages 1-15, September.
    8. Li, Yanbin & Wang, Jiani & Wang, Weiye & Liu, Chang & Li, Yun, 2023. "Dynamic pricing based electric vehicle charging station location strategy using reinforcement learning," Energy, Elsevier, vol. 281(C).
    9. Su Su & Hao Li & David Wenzhong Gao, 2017. "Optimal Planning of Charging for Plug-In Electric Vehicles Focusing on Users’ Benefits," Energies, MDPI, vol. 10(7), pages 1-15, July.

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