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Metaheuristics for online drive train efficiency optimization in electric vehicles

Apitzsch, Tilman; Klöffer, Christian; Jochem, Patrick; Doppelbauer, Martin; Fichtner, Wolf ORCID iD icon


Utilization of electric vehicles provides a solution to several challenges in today’s individual mobility. However, ensuring maximum efficient operation of electric vehicles is required in order to overcome their greatest weakness: the limited range. Even though the overall efficiency is already high, incorporating DC/DC converter into the electric drivetrain improves the efficiency level further. This inclusion enables the dynamic optimization of the intermediate voltage level subject to the current driving demand (operating point) of the drivetrain. Moreover, the overall drivetrain efficiency depends on the setup of other drivetrain components’ electric parameters. Solving this complex problem for different drivetrain parameter setups subject to the current driving demand needs considerable computing time for conventional solvers and cannot be delivered in real-time. Therefore, basic metaheuristics are identified and applied in order to assure the optimization process during driving. In order to compare the performance of metaheuristics for this task, we adjust and compare the performance of different basic metaheuristics (i.e. Monte-Carlo, Evolutionary Algorithms, Simulated Annealing and Particle Swarm Optimization). ... mehr

Volltext §
DOI: 10.5445/IR/1000063608
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Industriebetriebslehre und Industrielle Produktion (IIP)
Publikationstyp Forschungsbericht/Preprint
Publikationsjahr 2016
Sprache Englisch
Identifikator ISSN: 2196-7296
KITopen-ID: 1000063608
Verlag Karlsruher Institut für Technologie (KIT)
Umfang 36 S.
Serie Working Paper Series in Production and Energy ; 17
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