2026-09-20T00:17:26Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/918272026-06-06T00:55:43Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Leslie Pack Kaelbling.
Hollingsworth, Noel
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2014-11-24T18:37:46Z
2014-11-24T18:37:46Z
2014
2014
http://hdl.handle.net/1721.1/91827
894227292
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).
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.
by Noel Hollingsworth.
M.Eng.
75 pages
eng
Massachusetts Institute of Technology
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.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Optimizing a start-stop system to minimize fuel consumption using machine learning
Thesis
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<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>
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