Energy Cost Analysis and Operational Range Prediction Based on Medium- and Heavy-Duty Electric Vehicle Real-World Deployments across the United States
While the market for medium- and heavy-duty battery-electric vehicles (MHD EVs) is still nascent, a growing number of these vehicles are being deployed across the U.S. This study used over 2.3 million miles of operational data from multiple types of MHD EVs across various regions and operating condi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | World Electric Vehicle Journal |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2032-6653/14/12/330 |
| _version_ | 1797379117812809728 |
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| author | Yin Qiu Cristina Dobbelaere Shuhan Song |
| author_facet | Yin Qiu Cristina Dobbelaere Shuhan Song |
| author_sort | Yin Qiu |
| collection | DOAJ |
| description | While the market for medium- and heavy-duty battery-electric vehicles (MHD EVs) is still nascent, a growing number of these vehicles are being deployed across the U.S. This study used over 2.3 million miles of operational data from multiple types of MHD EVs across various regions and operating conditions to address knowledge gaps in total cost of ownership and operational range. First, real-world energy cost savings were determined: MHD fleets should experience energy cost savings each year from 2021 to 2035, regardless of vehicle platform, with the greatest savings seen in transit buses (up to USD 4459 annually) and HD trucks (up to USD 3284 annually). Second, to help fleets across various geographies throughout the U.S. assess the suitability of EVs for their year-round operating needs, operational range was modeled using the XGBoost algorithm (<i>R</i><sup>2</sup>: 70%) given 22 input features relevant to vehicle efficiency. Finally, this paper recommends (1) that MHD fleets apply energy-saving practices to minimize the impacts of cold temperatures and high congestion levels on vehicle efficiency and range, and (2) that local hauling fleets select trucks with a nominal range nearly double the expected maximum daily range to account for range losses under local, urban driving conditions. |
| first_indexed | 2024-03-08T20:16:29Z |
| format | Article |
| id | doaj.art-da0d6723ae2e48dd95d6233c9aedd2ce |
| institution | Directory Open Access Journal |
| issn | 2032-6653 |
| language | English |
| last_indexed | 2024-03-08T20:16:29Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj.art-da0d6723ae2e48dd95d6233c9aedd2ce2023-12-22T14:50:05ZengMDPI AGWorld Electric Vehicle Journal2032-66532023-11-01141233010.3390/wevj14120330Energy Cost Analysis and Operational Range Prediction Based on Medium- and Heavy-Duty Electric Vehicle Real-World Deployments across the United StatesYin Qiu0Cristina Dobbelaere1Shuhan Song2CALSTART, Pasadena, CA 91106, USACALSTART, Pasadena, CA 91106, USACALSTART, Pasadena, CA 91106, USAWhile the market for medium- and heavy-duty battery-electric vehicles (MHD EVs) is still nascent, a growing number of these vehicles are being deployed across the U.S. This study used over 2.3 million miles of operational data from multiple types of MHD EVs across various regions and operating conditions to address knowledge gaps in total cost of ownership and operational range. First, real-world energy cost savings were determined: MHD fleets should experience energy cost savings each year from 2021 to 2035, regardless of vehicle platform, with the greatest savings seen in transit buses (up to USD 4459 annually) and HD trucks (up to USD 3284 annually). Second, to help fleets across various geographies throughout the U.S. assess the suitability of EVs for their year-round operating needs, operational range was modeled using the XGBoost algorithm (<i>R</i><sup>2</sup>: 70%) given 22 input features relevant to vehicle efficiency. Finally, this paper recommends (1) that MHD fleets apply energy-saving practices to minimize the impacts of cold temperatures and high congestion levels on vehicle efficiency and range, and (2) that local hauling fleets select trucks with a nominal range nearly double the expected maximum daily range to account for range losses under local, urban driving conditions.https://www.mdpi.com/2032-6653/14/12/330BEV (battery electric vehicle)heavy-dutymedium-dutycostrangeenergy efficiency |
| spellingShingle | Yin Qiu Cristina Dobbelaere Shuhan Song Energy Cost Analysis and Operational Range Prediction Based on Medium- and Heavy-Duty Electric Vehicle Real-World Deployments across the United States World Electric Vehicle Journal BEV (battery electric vehicle) heavy-duty medium-duty cost range energy efficiency |
| title | Energy Cost Analysis and Operational Range Prediction Based on Medium- and Heavy-Duty Electric Vehicle Real-World Deployments across the United States |
| title_full | Energy Cost Analysis and Operational Range Prediction Based on Medium- and Heavy-Duty Electric Vehicle Real-World Deployments across the United States |
| title_fullStr | Energy Cost Analysis and Operational Range Prediction Based on Medium- and Heavy-Duty Electric Vehicle Real-World Deployments across the United States |
| title_full_unstemmed | Energy Cost Analysis and Operational Range Prediction Based on Medium- and Heavy-Duty Electric Vehicle Real-World Deployments across the United States |
| title_short | Energy Cost Analysis and Operational Range Prediction Based on Medium- and Heavy-Duty Electric Vehicle Real-World Deployments across the United States |
| title_sort | energy cost analysis and operational range prediction based on medium and heavy duty electric vehicle real world deployments across the united states |
| topic | BEV (battery electric vehicle) heavy-duty medium-duty cost range energy efficiency |
| url | https://www.mdpi.com/2032-6653/14/12/330 |
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