{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/91827"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/91827","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Optimizing a start-stop system to minimize fuel consumption using machine learning","abstract":"Many people are working on improving the efficiency of car's engines. One approach to maximizing efficiency has been to create start-stop systems. These systems shut the car's engine off when the car comes to a stop, saving fuel that would be used to keep the engine running. However, these systems introduce additional energy costs, which are associated with the engine restarting. These energy costs must be balanced by the system. In this thesis I describe my work with Ford to improve the performance of their start-stop controller. In this thesis I discuss optimizing a controller for both the general population as well as for individual drivers. I use reinforcement-learning techniques in both cases to find the best performing controller. I find a 27% improvement on Ford's current controller when optimizing for the general population, and then find an additional 1.6% improvement on the improved controller when optimizing for an individual.","abstract_html":"Many people are working on improving the efficiency of car's engines. One approach to maximizing efficiency has been to create start-stop systems. These systems shut the car's engine off when the car comes to a stop, saving fuel that would be used to keep the engine running. However, these systems introduce additional energy costs, which are associated with the engine restarting. These energy costs must be balanced by the system. In this thesis I describe my work with Ford to improve the performance of their start-stop controller. In this thesis I discuss optimizing a controller for both the general population as well as for individual drivers. I use reinforcement-learning techniques in both cases to find the best performing controller. I find a 27% improvement on Ford's current controller when optimizing for the general population, and then find an additional 1.6% improvement on the improved controller when optimizing for an individual.","abstract_has_math":false,"creators":["Hollingsworth, Noel"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Leslie Pack Kaelbling."],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014","date_published":"2014","updated_at":"2026-07-22T22:22:06Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1721.1/91827","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Leslie Pack Kaelbling."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. 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They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1721.1/91827"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.","Cataloged from PDF version of thesis.","Includes bibliographical references (pages 74-75)."]},{"key":"dc:description.abstract","label":"Abstract","values":["Many people are working on improving the efficiency of car's engines. One approach to maximizing efficiency has been to create start-stop systems. These systems shut the car's engine off when the car comes to a stop, saving fuel that would be used to keep the engine running. However, these systems introduce additional energy costs, which are associated with the engine restarting. These energy costs must be balanced by the system. In this thesis I describe my work with Ford to improve the performance of their start-stop controller. In this thesis I discuss optimizing a controller for both the general population as well as for individual drivers. I use reinforcement-learning techniques in both cases to find the best performing controller. I find a 27% improvement on Ford's current controller when optimizing for the general population, and then find an additional 1.6% improvement on the improved controller when optimizing for an individual."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M. Eng."]},{"key":"dc:title","label":"Title","values":["Optimizing a start-stop system to minimize fuel consumption using machine learning"]}]}],"canonical_facts":{"dc:contributor.advisor":["Leslie Pack Kaelbling."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:creator":["Hollingsworth, Noel"],"dc:date.accessioned":["2014-11-24T18:37:46Z"],"dc:date.available":["2014-11-24T18:37:46Z"],"dc:date.issued":["2014"],"dc:description":["Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.","Cataloged from PDF version of thesis.","Includes bibliographical references (pages 74-75)."],"dc:description.abstract":["Many people are working on improving the efficiency of car's engines. One approach to maximizing efficiency has been to create start-stop systems. These systems shut the car's engine off when the car comes to a stop, saving fuel that would be used to keep the engine running. However, these systems introduce additional energy costs, which are associated with the engine restarting. These energy costs must be balanced by the system. In this thesis I describe my work with Ford to improve the performance of their start-stop controller. In this thesis I discuss optimizing a controller for both the general population as well as for individual drivers. I use reinforcement-learning techniques in both cases to find the best performing controller. I find a 27% improvement on Ford's current controller when optimizing for the general population, and then find an additional 1.6% improvement on the improved controller when optimizing for an individual."],"dc:description.degree":["M. 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