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Environmental evaluation of introducing electric vehicles using a dynamic traffic-flow model

Author

Listed:
  • Kudoh, Yuki
  • Ishitani, Hisashi
  • Matsuhashi, Ryuji
  • Yoshida, Yoshikuni
  • Morita, Kouji
  • Katsuki, Shinichi
  • Kobayashi, Osamu

Abstract

A dynamic traffic-flow model (DTFM) is used in this study to evaluate the effectiveness of introducing electric vehicles (EVs) into the total traffic system as one of the alternative fuel vehicles. This model simulates congested and non-congested traffic flow caused by changes in the traffic demand. An environmental evaluation is carried out on the basis that all vehicles are substituted for EVs. Calculated results indicate that by introducing EVs, the NOx emissions and the CO2 emissions can be reduced by approximately 25.7 and 14.4% respectively. If battery performance of EVs is improved further, emissions can be further reduced by 39.6% (NOx) and 27.8% (CO2). Since emissions from heavy-duty vehicles are greater than other vehicles, the following measures have to be taken for these vehicles to significantly improve their impact upon the overall environment: (1) improvement in fuel efficiency and reductions of NOx in exhaust gas, (2) traffic demand management, such as modal shift.

Suggested Citation

  • Kudoh, Yuki & Ishitani, Hisashi & Matsuhashi, Ryuji & Yoshida, Yoshikuni & Morita, Kouji & Katsuki, Shinichi & Kobayashi, Osamu, 2001. "Environmental evaluation of introducing electric vehicles using a dynamic traffic-flow model," Applied Energy, Elsevier, vol. 69(2), pages 145-159, June.
    • Handle: RePEc:eee:appene:v:69:y:2001:i:2:p:145-159
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    Citations

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    Cited by:

    1. Ferrero, Enrico & Alessandrini, Stefano & Balanzino, Alessia, 2016. "Impact of the electric vehicles on the air pollution from a highway," Applied Energy, Elsevier, vol. 169(C), pages 450-459.
    2. Requia, Weeberb J. & Adams, Matthew D. & Arain, Altaf & Koutrakis, Petros & Ferguson, Mark, 2017. "Carbon dioxide emissions of plug-in hybrid electric vehicles: A life-cycle analysis in eight Canadian cities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 1390-1396.
    3. Kristoffersen, Trine Krogh & Capion, Karsten & Meibom, Peter, 2011. "Optimal charging of electric drive vehicles in a market environment," Applied Energy, Elsevier, vol. 88(5), pages 1940-1948, May.
    4. Hu, Xiaosong & Murgovski, Nikolce & Johannesson, Lars & Egardt, Bo, 2013. "Energy efficiency analysis of a series plug-in hybrid electric bus with different energy management strategies and battery sizes," Applied Energy, Elsevier, vol. 111(C), pages 1001-1009.
    5. Mu, Yunfei & Wu, Jianzhong & Jenkins, Nick & Jia, Hongjie & Wang, Chengshan, 2014. "A Spatial–Temporal model for grid impact analysis of plug-in electric vehicles," Applied Energy, Elsevier, vol. 114(C), pages 456-465.
    6. Jiang, C.X. & Jing, Z.X. & Cui, X.R. & Ji, T.Y. & Wu, Q.H., 2018. "Multiple agents and reinforcement learning for modelling charging loads of electric taxis," Applied Energy, Elsevier, vol. 222(C), pages 158-168.
    7. Saxena, Samveg & Gopal, Anand & Phadke, Amol, 2014. "Electrical consumption of two-, three- and four-wheel light-duty electric vehicles in India," Applied Energy, Elsevier, vol. 115(C), pages 582-590.
    8. Kudoh, Yuki & Kondo, Yoshinori & Matsuhashi, Keisuke & Kobayashi, Shinji & Moriguchi, Yuichi, 2004. "Current status of actual fuel-consumptions of petrol-fuelled passenger vehicles in Japan," Applied Energy, Elsevier, vol. 79(3), pages 291-308, November.
    9. Zhang, Hao & Cai, Guixin, 2020. "Subsidy strategy on new-energy vehicle based on incomplete information: A Case in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).

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