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A Comparative Analysis of Route-Based Energy Management Systems for Phevs

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dc.contributor.authorTaghavipour, Amir
dc.contributor.authorVajedi, Mahyar
dc.contributor.authorAzad, Nasser L.
dc.contributor.authorMcPhee, John
dc.date.accessioned2018-06-20T14:40:21Z
dc.date.available2018-06-20T14:40:21Z
dc.date.issued2015-07-29
dc.descriptionThis is the peer reviewed version of the following article: Taghavipour, A., Vajedi, M., Azad, N. L., & McPhee, J. (2015). A Comparative Analysis of Route-Based Energy Management Systems for Phevs. Asian Journal of Control, 18(1), 29–39, which has been published in final form at https://dx.doi.org/10.1002/asjc.1191. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.en
dc.description.abstractPlug-in hybrid electric vehicle (PHEV) development seems to be essential step on the path to widespread deployment of electric vehicles (EVs) as the zero-emission solution for the future of transportation. Because of their larger battery pack in comparison to conventional hybrid electic vehicles (HEVs), they offer longer electric range which leads to a superior fuel economy performance. Advanced energy management systems (EMSs) use vehicle trip information to enhance a PHEV's performance. In this study, the performance of two optimal control approaches, model predictive control (MPC) and adaptive equivalent consumption minimization strategy (A-ECMS), for designing an EMS for different levels of trip information are compared. The resulting EMSs are fine-tuned for the Toyota Prius plug-in hybrid powertrain and their performances are evaluated by using a high-fidelity simulation model in the Autonomie software. The results of simulation show that both MPC and A-ECMS can approximately improve fuel economy up to 10% compared to the baseline Autonomie controller for EPA urban and highway drive cycles. Although both EMSs can be implemented in real time, A-ECMS is 15% faster than MPC. Moreover, it is shown that the engine operating points are more sensitive to the battery depletion pattern than to different driving schedules.en
dc.identifier.urihttps://dx.doi.org/10.1002/asjc.1191
dc.identifier.urihttp://hdl.handle.net/10012/13422
dc.language.isoenen
dc.publisherWileyen
dc.subjectadaptive equivalent consumption minimization strategyen
dc.subjectenergy management systemen
dc.subjectmodel predictive controlen
dc.subjectPlug-in hybrid electric vehicleen
dc.titleA Comparative Analysis of Route-Based Energy Management Systems for Phevsen
dc.typeArticleen
dcterms.bibliographicCitationTaghavipour, A., Vajedi, M., Azad, N. L., & McPhee, J. (2015). A Comparative Analysis of Route-Based Energy Management Systems for Phevs. Asian Journal of Control, 18(1), 29–39. doi:10.1002/asjc.1191en
uws.contributor.affiliation1Faculty of Engineeringen
uws.contributor.affiliation2Systems Design Engineeringen
uws.peerReviewStatusRevieweden
uws.scholarLevelFacultyen
uws.typeOfResourceTexten
uws.typeOfResourceTexten

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