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Impact of Intersection Control on Battery Electric Vehicle Energy Consumption

Author

Listed:
  • Kyoungho Ahn

    (Virginia Tech Transportation Institute, 3500 Transportation Research Plaza, Blacksburg, VA 24061, USA)

  • Sangjun Park

    (Department of Civil Engineering, Chosun University, 309 Pilmun-Daero, Dong-Gu, Gwangju 61452, Korea)

  • Hesham A. Rakha

    (Department of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University, 3500 Transportation Research Plaza, Blacksburg, VA 24061, USA)

Abstract

Battery electric vehicle (BEV) sales have significantly increased in recent years. They have different energy consumption patterns compared to the fuel consumption patterns of internal combustion engine vehicles (ICEVs). This study quantified the impact of intersection control approaches—roundabout, traffic signal, and two-way stop controls—on BEVs’ energy consumption. The paper systematically investigates BEVs’ energy consumption patterns compared to the fuel consumption of ICEVs. The results indicate that BEVs’ energy consumption patterns are significantly different than ICEVs’ patterns. For example, for BEVs approaching a high-speed intersection, the roundabout was found to be the most energy-efficient intersection control, while the two-way stop sign was the least efficient. In contrast, for ICEVs, the two-way stop sign was the most fuel-efficient control, while the roundabout was the least efficient. Findings also indicate that the energy saving of traffic signal coordination was less significant for BEVs compared to the fuel consumption of ICEVs since more regenerative energy is produced when partial or poorly coordinated signal plans are implemented. The study confirms that BEV regenerative energy is a major factor in energy efficiency, and that BEVs recover different amounts of energy in different urban driving environments. The study suggests that new transportation facilities and control strategies should be designed to enhance BEVs’ energy efficiency, particularly in zero emission zones.

Suggested Citation

  • Kyoungho Ahn & Sangjun Park & Hesham A. Rakha, 2020. "Impact of Intersection Control on Battery Electric Vehicle Energy Consumption," Energies, MDPI, vol. 13(12), pages 1-11, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3190-:d:373749
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    References listed on IDEAS

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    1. Fiori, Chiara & Ahn, Kyoungho & Rakha, Hesham A., 2016. "Power-based electric vehicle energy consumption model: Model development and validation," Applied Energy, Elsevier, vol. 168(C), pages 257-268.
    2. De Gennaro, Michele & Paffumi, Elena & Scholz, Harald & Martini, Giorgio, 2014. "GIS-driven analysis of e-mobility in urban areas: An evaluation of the impact on the electric energy grid," Applied Energy, Elsevier, vol. 124(C), pages 94-116.
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    Cited by:

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    2. Andrea Di Martino & Seyed Mahdi Miraftabzadeh & Michela Longo, 2022. "Strategies for the Modelisation of Electric Vehicle Energy Consumption: A Review," Energies, MDPI, vol. 15(21), pages 1-20, October.
    3. Peter Tauš & Marcela Taušová & Peter Sivák & Mária Shejbalová Muchová & Eva Mihaliková, 2020. "Parameter Optimization Model Photovoltaic Battery System for Charging Electric Cars," Energies, MDPI, vol. 13(17), pages 1-17, September.
    4. Kalina Grzesiuk & Dorota Jegorow & Monika Wawer & Anna Głowacz, 2023. "Energy-Efficient City Transportation Solutions in the Context of Energy-Conserving and Mobility Behaviours of Generation Z," Energies, MDPI, vol. 16(15), pages 1-28, August.
    5. Nur Ayeesha Qisteena Muzir & Md. Hasanuzzaman & Jeyraj Selvaraj, 2023. "Modeling and Analyzing the Impact of Different Operating Conditions for Electric and Conventional Vehicles in Malaysia on Energy, Economic, and the Environment," Energies, MDPI, vol. 16(13), pages 1-31, June.

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