Fuel cell hybrid electric vehicle sizing using ordinal optimization -

dc.contributor.authorDinnawi, Rafica Khaled
dc.contributor.departmentDepartment of Electrical and Computer Engineering
dc.contributor.facultyFaculty of Engineering and Architecture
dc.contributor.institutionAmerican University of Beirut
dc.date2014
dc.date.accessioned2015-02-03T10:35:03Z
dc.date.available2015-02-03T10:35:03Z
dc.date.issued2014
dc.date.submitted2014
dc.descriptionThesis. M.E. American University of Beirut. Department of Electrical and Computer Engineering, 2014. ET:6022
dc.descriptionAdvisor : Dr. Sami Karaki, Professor, Electrical and Computer Engineering ; Members of Committee: Dr. Riad Chedid, Professor, Electrical and Computer Engineering ; Dr. Rabih Jabr, Associate Professor, Electrical and Computer Engineering.
dc.descriptionIncludes bibliographical references (leaves 46-48)
dc.description.abstractThis thesis will develop an optimal design methodology for fuel cell hybrid electric vehicle (FCHEV) based on ordinal optimization (OO) technique and dynamic programming; the optimal design aims to determine the appropriate sizes of the different units – hydrogen tank, fuel cell, and battery – for the purpose of minimizing the investment and operational cost given some specification of the car range, the road type and its gradeability. The dynamic programming simulates the operation of the vehicle for a set of specified sizes on given driving cycles and provides the total vehicle cost per year. The OO method offers an efficient approach for simulation optimization by focusing on ranking and selecting a finite set of good alternatives through two models: the simple model and the accurate model. The OO program sets the sizes of the components to sample the search space using the simple but fast model. In the simple model the operation of components is simplified by taking small samples of the mixed driving cycles with appropriate scaling for the energy utilized. Moreover, the number of discrete states used in the dynamic programming is made relatively low. The OO theory is then applied to determine the numbers of top-S of selected design solutions. The method that is used to determine the best good enough solutions of this selected set S is the blind pick. Then, the top-S designs are examined using an “accurate model”, which is implemented by taking the whole mixed driving cycles and an increased number of states in dynamic programming. Five different test runs were carried out based on different situations. First, three tests were conducted: one based on gradeability with the variation of the fuel cell and hydrogen costs, another without gradeability, and a third without separate gradeability but with 5percent slope on the HWFET driving cycle. Another test run was performed to study the effect of road range. The fifth test run used an OO selection method other than the blind pick. The results
dc.format.extentxii, 48 leaves : illustrations ; 30 cm
dc.identifier.otherb18263021
dc.identifier.urihttp://hdl.handle.net/10938/10075
dc.language.isoen
dc.relation.ispartofTheses, Dissertations, and Projects
dc.subject.classificationET:006022 AUBNO
dc.subject.lcshRenewable energy sources.
dc.subject.lcshFuel cells.
dc.subject.lcshElectric vehicles -- Power supply.
dc.subject.lcshHybrid electric vehicles.
dc.subject.lcshFuel cell vehicles.
dc.subject.lcshMathematical optimization.
dc.titleFuel cell hybrid electric vehicle sizing using ordinal optimization -
dc.typeThesis

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