Electric and Conventional Vehicle Performance over Eco-Driving Cycles: Energy Benefits and Component Loss

Gao, Zhiming; Laclair, Tim

Title: Electric and Conventional Vehicle Performance over Eco-Driving Cycles: Energy Benefits and Component Loss

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Abstract

Battery electric vehicles (BEVs) are expected to be a pathway towards meeting greenhouse and criteria pollutant gas reductions in the current and future transportation sector. However, BEV technologies are still evolving, including efficiency optimization and enhancement. Emerging connected and automated vehicle (CAV) technologies provide an in-depth insight for developing innovative applications and systems to leverage BEV energy efficiency and substantially transform transportation systems. Therefore, we present simulation studies of various BEV types and compare the performance between measured, real-world drive cycles and highly optimized eco-driving cycles that are representative of trajectory modifications using advanced CAV technologies. The investigated vehicles include a compact car and a Class 7 delivery truck. The results demonstrate that eco-driving has a high potential to reduce energy consumption for the BEVs considered. As part of the study, a comprehensive EV powertrain model was developed to account for key EV components and powertrain configurations. The impact of eco-driving was further evaluated for conventional vehicles with characteristics that are comparable to the modeled BEVs.

Authors:

^[1];

^[1]

ORNL

Publication Date:: 2019-07-01

Research Org.:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE Advanced Research Projects Agency - Energy (ARPA-E)

OSTI Identifier:: 1559604

DOE Contract Number:: AC05-00OR22725

Resource Type:: Conference

Resource Relation:: Conference: 32nd Electric Vehicle Symposium (EVS32) - Lyon, , France - 5/19/2019 8:00:00 AM-5/22/2019 8:00:00 AM

Country of Publication:: United States

Language:: English

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                    Gao, Zhiming, and Laclair, Tim. Electric and Conventional Vehicle Performance over Eco-Driving Cycles: Energy Benefits and Component Loss.  United States: N. p., 2019. 
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                    Gao, Zhiming, & Laclair, Tim. Electric and Conventional Vehicle Performance over Eco-Driving Cycles: Energy Benefits and Component Loss.  United States.

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                    Gao, Zhiming, and Laclair, Tim. 2019.  
        "Electric and Conventional Vehicle Performance over Eco-Driving Cycles: Energy Benefits and Component Loss".  United States.  https://www.osti.gov/servlets/purl/1559604.

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@article{osti_1559604,

  title        = {Electric and Conventional Vehicle Performance over Eco-Driving Cycles: Energy Benefits and Component Loss},

  author       = {Gao, Zhiming and Laclair, Tim},

  abstractNote = {Battery electric vehicles (BEVs) are expected to be a pathway towards meeting greenhouse and criteria pollutant gas reductions in the current and future transportation sector. However, BEV technologies are still evolving, including efficiency optimization and enhancement. Emerging connected and automated vehicle (CAV) technologies provide an in-depth insight for developing innovative applications and systems to leverage BEV energy efficiency and substantially transform transportation systems. Therefore, we present simulation studies of various BEV types and compare the performance between measured, real-world drive cycles and highly optimized eco-driving cycles that are representative of trajectory modifications using advanced CAV technologies. The investigated vehicles include a compact car and a Class 7 delivery truck. The results demonstrate that eco-driving has a high potential to reduce energy consumption for the BEVs considered. As part of the study, a comprehensive EV powertrain model was developed to account for key EV components and powertrain configurations. The impact of eco-driving was further evaluated for conventional vehicles with characteristics that are comparable to the modeled BEVs.},

  doi          = {},

  url          = {https://www.osti.gov/biblio/1559604},
  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {2019},

  month        = {7}

}

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