dc.contributor.advisor | Jacquillat, Alexandre | |
dc.contributor.author | Alam, Muhammad Ashhad | |
dc.date.accessioned | 2024-10-09T18:27:14Z | |
dc.date.available | 2024-10-09T18:27:14Z | |
dc.date.issued | 2024-09 | |
dc.date.submitted | 2024-10-07T14:34:40.736Z | |
dc.identifier.uri | https://hdl.handle.net/1721.1/157190 | |
dc.description.abstract | The transportation sector in the US contributes to about a third of all greenhouse gas emissions, about a quarter of which stems from road freight. A major driver of this environmental footprint remains a heavy reliance on trucking—the least fuel-efficient mode of transportation. A key pathway toward freight decarbonization, therefore, involves shifting from internal combustion engines (ICE) to electric powertrains in truck fleets. This work develops analytics-based solutions to support and assess the electrification of long-haul logistics operations, by applying the methods to PepsiCo’s operations in Texas. | |
dc.publisher | Massachusetts Institute of Technology | |
dc.rights | In Copyright - Educational Use Permitted | |
dc.rights | Copyright retained by author(s) | |
dc.rights.uri | https://rightsstatements.org/page/InC-EDU/1.0/ | |
dc.title | Building a Scalable Electrification Infrastructure in
Logistics | |
dc.type | Thesis | |
dc.description.degree | M.Eng. | |
dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | |
mit.thesis.degree | Master | |
thesis.degree.name | Master of Engineering in Electrical Engineering and Computer Science | |