energy management system optimization for battery-ultracapacitor powered electric vehicle

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2017
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Abstract
Energy usage and environment pollution in the transportation are major problems of today’s world. Although electric vehicles are promising solutions to these problems, their energy management methods are complicated and need to be improved for the extensive usage. In this work, the heuristic optimization methods; Differential Evolution Algorithm, Genetic Algorithm and Particle Swarm Optimization, are used to provide an optimal energy management system for a battery/ultracapacitor powered electric vehicle without prior knowledge of the drive cycle. The proposed scheme has been simulated in Matlab and applied on the ECE driving cycle. The differences between optimization methods are compared with reproducible and measurable error criteria. Results and the comparisons show the effectiveness and the practicality of the applied methods for the energy management problem of the multi-source electric vehicles.
Reference Key
koroglu2017journalenergy Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Selim Koroglu;Akif Demircali;Selami Kesler;Peter Sergeant;Erkan Ozturk;Mustafa Tumbek
Journal آب و فاضلاب
Year 2017
DOI
DOI not found
URL
http://journal.esrgroups.org/jes/papers/13_1_2.pdf
Keywords
battery energy management optimization genetic algorithm particle swarm optimization electric vehicle electronics nuclear engineering

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