Repository logo
 

Predictive energy management strategies for hybrid electric vehicles applied during acceleration events

Simple item page
dc.contributor.authorTrinko, David A., author
dc.contributor.authorBradley, Thomas H., advisor
dc.contributor.authorQuinn, Jason C., committee member
dc.contributor.authorAnderson, Charles W., committee member
dc.date.accessioned2019-06-14T17:06:12Z
dc.date.available2020-06-10T17:06:09Z
dc.date.issued2019
dc.description.abstractThe emergence and widespread adoption of vehicles with hybrid powertrains and onboard computing capabilities have improved the feasibility of utilizing predictions of vehicle state to enable optimal energy management strategies (EMS) to improve fuel economy. Real-world implementation of optimal EMS remains challenging in part because of limits on prediction accuracy and computation speed. However, if a finite set of EMS can be pre-derived offline, instead of onboard the vehicle in real time, fuel economy improvements may be possible using hardware that is common in current production vehicles. Acceleration events (AE) are attractive targets for this kind of EMS application due to their high energy cost, probability of recurrence, and limited variability. This research aims to understand how a finite set of EMS might be derived and applied to AEs based on predictions of basic AE attributes to achieve reliable fuel economy improvements. Models of the 2010 Toyota Prius are used to simulate fuel economy for a variety of control strategies, including baseline control, optimal EMS control derived via dynamic programming, and pre-derived control applied with approximate prediction to AEs. Statistical methods are used to identify correlations between AE attributes, optimal powertrain control, and fuel economy results. Then, key AE attributes are used to define AE categorization schemes of various resolutions, in which one pre-derived EMS is applied to every AE in a category. Last, the control strategies are simulated during a variety of drive cycles to predict real-world fuel economy results. By simulating fuel economy improvement for AEs both in isolation and in the context of drive cycles, it was concluded that applying pre-derived EMS to AEs based on predictions of initial and final velocity is likely to enable reliable fuel economy benefits in low-aggression driving.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierTrinko_colostate_0053N_15369.pdf
dc.identifier.urihttps://hdl.handle.net/10217/195328
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.titlePredictive energy management strategies for hybrid electric vehicles applied during acceleration events
dc.typeText
dcterms.embargo.expires2020-06-10
dcterms.embargo.terms2020-06-10
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.levelMasters
thesis.degree.nameMaster of Science (M.S.)

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Trinko_colostate_0053N_15369.pdf
Size:
7.69 MB
Format:
Adobe Portable Document Format
Download

Collections

2000-2019
Theses and Dissertations