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

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 t...

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Autores principales:	Yuche Chen, Ruixiao Sun, Xuanke Wu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	autonomous vehicles self-driving cars autonomy aerodynamic efficiency Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/2d7a3f45935e4cd4ab06d311ae9ca831
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id	oai:doaj.org-article:2d7a3f45935e4cd4ab06d311ae9ca831
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spelling	oai:doaj.org-article:2d7a3f45935e4cd4ab06d311ae9ca8312021-11-25T19:00:47ZEstimating Bounds of Aerodynamic, Mass, and Auxiliary Load Impacts on Autonomous Vehicles: A Powertrain Simulation Approach10.3390/su1322124052071-1050https://doaj.org/article/2d7a3f45935e4cd4ab06d311ae9ca8312021-11-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/22/12405https://doaj.org/toc/2071-1050Vehicle 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.Yuche ChenRuixiao SunXuanke WuMDPI AGarticleautonomous vehiclesself-driving carsautonomyaerodynamic efficiencyEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12405, p 12405 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	autonomous vehicles self-driving cars autonomy aerodynamic efficiency Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	autonomous vehicles self-driving cars autonomy aerodynamic efficiency Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Yuche Chen Ruixiao Sun Xuanke Wu Estimating Bounds of Aerodynamic, Mass, and Auxiliary Load Impacts on Autonomous Vehicles: A Powertrain Simulation Approach
description	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.
format	article
author	Yuche Chen Ruixiao Sun Xuanke Wu
author_facet	Yuche Chen Ruixiao Sun Xuanke Wu
author_sort	Yuche Chen
title	Estimating Bounds of Aerodynamic, Mass, and Auxiliary Load Impacts on Autonomous Vehicles: A Powertrain Simulation Approach
title_short	Estimating Bounds of Aerodynamic, Mass, and Auxiliary Load Impacts on Autonomous Vehicles: A Powertrain Simulation Approach
title_full	Estimating Bounds of Aerodynamic, Mass, and Auxiliary Load Impacts on Autonomous Vehicles: A Powertrain Simulation Approach
title_fullStr	Estimating Bounds of Aerodynamic, Mass, and Auxiliary Load Impacts on Autonomous Vehicles: A Powertrain Simulation Approach
title_full_unstemmed	Estimating Bounds of Aerodynamic, Mass, and Auxiliary Load Impacts on Autonomous Vehicles: A Powertrain Simulation Approach
title_sort	estimating bounds of aerodynamic, mass, and auxiliary load impacts on autonomous vehicles: a powertrain simulation approach
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/2d7a3f45935e4cd4ab06d311ae9ca831
work_keys_str_mv	AT yuchechen estimatingboundsofaerodynamicmassandauxiliaryloadimpactsonautonomousvehiclesapowertrainsimulationapproach AT ruixiaosun estimatingboundsofaerodynamicmassandauxiliaryloadimpactsonautonomousvehiclesapowertrainsimulationapproach AT xuankewu estimatingboundsofaerodynamicmassandauxiliaryloadimpactsonautonomousvehiclesapowertrainsimulationapproach
_version_	1718410393161826304

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

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