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Cited by 16Electric Vehicles and InfrastructureVehicle emissions and performanceEnvironmental Impact and Sustainability

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Life Cycle Assessment to Study the Impact of the Regional Grid Mix and Temperature Differences on the GHG Emissions of Battery Electric and Conventional Vehicles

Aljohani, Tawfiq, Alzahrani, Ghurmallah·2019·2019 SoutheastCon
DOI 10.1109/southeastcon42311.2019.9020666
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Abstract

Electric Vehicles are one of the key factors for a sustainable energy future. Research has not conclusively verified whether or not transportation dependence on EVs achieves the suggested sustainability goals at the rate required by climate policy and science. In this study, we conduct a life cycle assessment to measure the impact of different regional energy mixes and climates temperatures, for both the electric and conventional vehicles. Specifically, we modeled the case of Los Angeles, California vs. Detroit, Michigan for current energy mixes for both cities in 2017, as well as the projected ones for 2020 and 2030 plans. Also, we compared the difference in GHG emissions produced in the two cities to value the impact of the variations in both the weather and generation portfolios. We used GREET, a full life cycle assessment model released by the Argonne National Laboratory of the Department of Energy. The results show that the geographical differences greatly influence the level of GHG emitted from the utilization of the EVs. Specifically, for the same level of driving (considering the same car model), the EVs in Los Angeles have lower GHG emissions than in Detroit. Furthermore, Detroit's lower ambient temperature for most of the year will result in higher levels in both GHG emission intensities and battery-charging inefficiencies. Additionally, the modeled energy grid mix for Michigan in all case scenarios (2017, 2020 and 2030) contains high percentage of coal as a fuel, compared to no coal at all in California's energy grid, making the EVs charging process in Detroit lead to more GHG emissions than in Los Angeles.

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Crossref · 10.1109/southeastcon42311.2019.9020666

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Citations

Cites

Show all 7 references

Research works printed but not linked: 21

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Metadata

Authors
Aljohani, T., & Alzahrani, G.
Affiliations
  • Florida International University (US)
  • King Fahd University of Petroleum and Minerals (SA)
ORCID
Aljohani, Tawfiq
Venue
2019 SoutheastCon
Publisher
IEEE
Published
2019-04
Pages
1-9
ISSN
1091-0050 (ISSN-L)
DOI
10.1109/southeastcon42311.2019.9020666
OpenAlex
W3012426919
Cited by
15
References
7
Citations by year
2025: 1 · 2024: 2 · 2023: 2 · 2022: 1 · 2021: 5 · 2020: 4
FWCI
3.98
Type
conference paper
Language
en
Open access
closed
Licence
Locations
IEEE SoutheastCon (publishedVersion)
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
  • Physical Sciences › Engineering › Automotive Engineering › Vehicle emissions and performance
  • Physical Sciences › Environmental Science › Environmental Engineering › Environmental Impact and Sustainability
Keywords
battery electric vehicles, internal combustion engine vehicles, greenhouse gas emissions, ambient temperature effects, battery charging efficiency, electricity generation portfolio, coal dependence, Los Angeles, Detroit, GREET model

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CoChat. (2026). Citation check: Life Cycle Assessment to Study the Impact of the Regional Grid Mix and Temperature Differences on the GHG Emissions of Battery Electric and Conventional Vehicles. https://app.cochat.ai/papers/10.1109/southeastcon42311.2019.9020666
The paper
Aljohani, T., & Alzahrani, G. (2019). Life Cycle Assessment to Study the Impact of the Regional Grid Mix and Temperature Differences on the GHG Emissions of Battery Electric and Conventional Vehicles. 2019 SoutheastCon. https://doi.org/10.1109/southeastcon42311.2019.9020666

Data sources

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.