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Enabling predictive energy management in vehicles

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dc.contributor.authorAsher, Zachary D., author
dc.contributor.authorBradley, Thomas H., advisor
dc.contributor.authorChong, Edwin, committee member
dc.contributor.authorYoung, Peter, committee member
dc.contributor.authorZhao, Jianguo, committee member
dc.date.accessioned2018-06-12T16:14:04Z
dc.date.available2019-06-07T16:14:04Z
dc.date.issued2018
dc.description.abstractWidespread automobile usage provides economic and societal benefits but combustion engine powered automobiles have significant economic, environmental, and human health costs. Recent research has shown that these costs can be reduced by increasing fuel economy through optimal energy management. A globally optimal energy management strategy requires perfect prediction of an entire drive cycle but can improve fuel economy by up to 30\%. This dissertation focuses on bridging the gap between this important research finding and implementation of predictive energy management in modern vehicles. A primary research focus is to investigate the tradeoffs between information sensing, computation power requirements for prediction, and prediction effort when implementing predictive energy management in vehicles. These tradeoffs are specifically addressed by first exploring the resulting fuel economy from different types of prediction errors, then investigating the level of prediction fidelity, scope, and real-time computation that is required to realize a fuel economy improvement, and lastly investigating a large computational effort scenario using only modern technology to make predictions. All of these studies are implemented in simulation using high fidelity and physically validated vehicle models. Results show that fuel economy improvements using predictive optimal energy management are feasible despite prediction errors, in a low computational cost scenario, and with only modern technology to make predictions. It is anticipated that these research findings can inform new control strategies to improve vehicle fuel economy and alleviate the economic, environmental, and human health costs for the modern vehicle fleet.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierAsher_colostate_0053A_14727.pdf
dc.identifier.urihttps://hdl.handle.net/10217/189347
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectdynamic programming
dc.subjecthybrid electric vehicle
dc.subjectoptimal control
dc.subjectfuel economy
dc.subjectartificial neural network
dc.subjectmisprediction
dc.titleEnabling predictive energy management in vehicles
dc.typeText
dcterms.embargo.expires2019-06-07
dcterms.embargo.terms2019-06-07
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineMechanical Engineering
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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