Show simple item record

dc.contributor.authorMarefat, Hoda
dc.date.accessioned2019-05-23 15:46:06 (GMT)
dc.date.available2019-05-23 15:46:06 (GMT)
dc.date.issued2019-05-23
dc.date.submitted2019-05-15
dc.identifier.urihttp://hdl.handle.net/10012/14685
dc.description.abstractRecently, the Battery Electric Vehicle (BEV) has been considered to be a proper candidate to terminate the problems associated with fuel-based vehicles. Therefore, the development and enhancement of the BEVs have lately formed an attractive field of study. One of the significant challenges to commercialize BEVs is to overcome the battery drawbacks that limit the BEV’s performance. One promising solution is to hybridize the BEV with a supercapacitor (SC) so that the battery is the primary source of energy meanwhile the SC handles sudden fluctuations in power demand. Obviously, to exploit the most benefits from this hybrid system, an intelligent Energy Management System (EMS) is required. In this thesis, different EMSs are developed: first, the Nonlinear Model Predictive Controller (NMPC) based on Newton Generalized Minimum Residual (Newton/GMRES) method. The NMPC effectively optimizes the power distribution between the battery and supercapacitor as a result of NMPC ability to handle multi-input, multi-output problems and utilize past information to predict future power demand. However, real-time application of the NMPC is challenging due to its huge computational cost. Therefore, Newton/GMRES, which is a fast real-time optimizer, is implemented in the heart of the NMPC. Simulation results demonstrate that the Newton/GMRES NMPC successfully protects the battery during high power peaks and nadirs. On the other hand, future power demand is inherently probabilistic. Consequently, Stochastic Dynamic Programming (SDP) is employed to maximize the battery lifespan while considering the uncertain nature of power demand. The next power demand is predicted by a Markov chain. The SDP approach determines the optimal policy using the policy iteration algorithm. Implementation of the SDP is quite free-to-launch since it does not require any additional equipment. Furthermore, the SDP is an offline approach, thus, computational cost is not an issue. Simulation results are considerable compared to those of other rival approaches. Recent success stories of applying bio-inspired techniques such as Particle Swarm Optimization (PSO) to control area have motivated the author to investigate the potential of this algorithm to solve the problem at hand. The PSO is a population-based method that effectively seeks the best answer in the solution space with no need to solve complex equations. Simulation results indicate that PSO is successful in terms of optimality, but it shows some difficulties for real-time application.en
dc.language.isoenen
dc.publisherUniversity of Waterlooen
dc.subjectenergy management systemen
dc.subjectsupercapacitoren
dc.subjectbattery lifespanen
dc.subjecthybrid electric vehiclesen
dc.subjectbattery electric vehiclesen
dc.subjectbattery electric vehicles hybridized with supercapacitoren
dc.titleNew Energy Management Systems for Battery Electric Vehicles with Supercapacitoren
dc.typeMaster Thesisen
dc.pendingfalse
uws-etd.degree.departmentSystems Design Engineeringen
uws-etd.degree.disciplineSystem Design Engineeringen
uws-etd.degree.grantorUniversity of Waterlooen
uws-etd.degreeMaster of Applied Scienceen
uws.contributor.advisorL. Azad, Nasser
uws.contributor.affiliation1Faculty of Engineeringen
uws.published.cityWaterlooen
uws.published.countryCanadaen
uws.published.provinceOntarioen
uws.typeOfResourceTexten
uws.peerReviewStatusUnrevieweden
uws.scholarLevelGraduateen


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record


UWSpace

University of Waterloo Library
200 University Avenue West
Waterloo, Ontario, Canada N2L 3G1
519 888 4883

All items in UWSpace are protected by copyright, with all rights reserved.

DSpace software

Service outages