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Estimating Bounds of Aerodynamic, Mass, and Auxiliary Load Impacts on Autonomous Vehicles: A Powertrain Simulation Approach

Author

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  • Yuche Chen

    (Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC 29208, USA)

  • Ruixiao Sun

    (Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC 29208, USA)

  • Xuanke Wu

    (Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC 29208, USA)

Abstract

Vehicle automation requires new onboard sensors, communication equipment, and/or data processing units, and may encourage modifications to existing onboard components (such as the steering wheel). These changes impact the vehicle’s mass, auxiliary load, coefficient of drag, and frontal area, which then change vehicle performance. This paper uses the powertrain simulation model FASTSim to quantify the impact of autonomy-related design changes on a vehicle’s fuel consumption. Levels 0, 2, and 5 autonomous vehicles are modeled for two battery-electric vehicles (2017 Chevrolet Bolt and 2017 Nissan Leaf) and a gasoline powered vehicle (2017 Toyota Corolla). Additionally, a level 5 vehicle is divided into pessimistic and optimistic scenarios which assume different electronic equipment integration format. The results show that 4–8% reductions in energy economy can be achieved in a L5 optimistic scenario and an 10–15% increase in energy economy will be the result in a L5 pessimistic scenario. When looking at impacts on different power demand sources, inertial power is the major power demand in urban driving conditions and aerodynamic power demand is the major demand in highway driving conditions.

Suggested Citation

  • Yuche Chen & Ruixiao Sun & Xuanke Wu, 2021. "Estimating Bounds of Aerodynamic, Mass, and Auxiliary Load Impacts on Autonomous Vehicles: A Powertrain Simulation Approach," Sustainability, MDPI, vol. 13(22), pages 1-13, November.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:22:p:12405-:d:675840
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    References listed on IDEAS

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    1. Christopher R. Knittel, 2011. "Automobiles on Steroids: Product Attribute Trade-Offs and Technological Progress in the Automobile Sector," American Economic Review, American Economic Association, vol. 101(7), pages 3368-3399, December.
    2. Chen, Yuche & Zhang, Yunteng & Fan, Yueyue & Hu, Kejia & Zhao, Jianyou, 2017. "A dynamic programming approach for modeling low-carbon fuel technology adoption considering learning-by-doing effect," Applied Energy, Elsevier, vol. 185(P1), pages 825-835.
    3. Hu, Kejia & Chen, Yuche, 2016. "Technological growth of fuel efficiency in european automobile market 1975–2015," Energy Policy, Elsevier, vol. 98(C), pages 142-148.
    4. Wadud, Zia & MacKenzie, Don & Leiby, Paul, 2016. "Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 86(C), pages 1-18.
    5. Chen, Yuche & Meier, Alan, 2016. "Fuel consumption impacts of auto roof racks," Energy Policy, Elsevier, vol. 92(C), pages 325-333.
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