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Quantifying a cellular automata simulation of electric vehicles

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  • Hill, Graeme
  • Bell, Margaret
  • Blythe, Phil

Abstract

Within this work the Nagel–Schreckenberg (NS) cellular automata is used to simulate a basic cyclic road network. Results from SwitchEV, a real world Electric Vehicle trial which has collected more than two years of detailed electric vehicle data, are used to quantify the results of the NS automata, demonstrating similar power consumption behavior to that observed in the experimental results. In particular the efficiency of the electric vehicles reduces as the vehicle density increases, due in part to the reduced efficiency of EVs at low speeds, but also due to the energy consumption inherent in changing speeds. Further work shows the results from introducing spatially restricted speed restriction. In general it can be seen that induced congestion from spatially transient events propagates back through the road network and alters the energy and efficiency profile of the simulated vehicles, both before and after the speed restriction. Vehicles upstream from the restriction show a reduced energy usage and an increased efficiency, and vehicles downstream show an initial large increase in energy usage as they accelerate away from the speed restriction.

Suggested Citation

  • Hill, Graeme & Bell, Margaret & Blythe, Phil, 2014. "Quantifying a cellular automata simulation of electric vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 421-429.
    • Handle: RePEc:eee:phsmap:v:416:y:2014:i:c:p:421-429
    DOI: 10.1016/j.physa.2014.08.007
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    References listed on IDEAS

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    1. Druckman, Angela & Jackson, Tim, 2009. "The carbon footprint of UK households 1990-2004: A socio-economically disaggregated, quasi-multi-regional input-output model," Ecological Economics, Elsevier, vol. 68(7), pages 2066-2077, May.
    2. Zhang, Wei & Zhang, Wei & Yang, Xian-qing, 2008. "Energy dissipation in the deterministic and nondeterministic Nagel–Schreckenberg models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(18), pages 4657-4664.
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    Cited by:

    1. Zhang, Jiahe & Qian, Yongsheng & Zeng, Junwei & Wei, Xuting & Li, Haijun, 2023. "Hybrid characteristics of heterogeneous traffic flow mixed with electric vehicles considering the amplitude of acceleration and deceleration," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 614(C).
    2. He, Jia & Huang, Hai-Jun & Yang, Hai & Tang, Tie-Qiao, 2017. "An electric vehicle driving behavior model in the traffic system with a wireless charging lane," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 481(C), pages 119-126.
    3. Yu, Bin & Zhou, Huixin & Wang, Lin & Wang, Zirui & Cui, Shaohua, 2021. "An extended two-lane car-following model considering the influence of heterogeneous speed information on drivers with different characteristics under honk environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    4. Luo, Qingsong & Zhou, Yimin & Hou, Weicheng & Peng, Lei, 2022. "A hierarchical blockchain architecture based V2G market trading system," Applied Energy, Elsevier, vol. 307(C).
    5. 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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