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Chapter Genetic Algorithm Optimization of an Energy Storage System Design and Fuzzy Logic Supervision for Battery Electric Vehicles

dc.contributor.authorBreban, Stefan
dc.date.accessioned2025-03-07T14:03:38Z
dc.date.available2025-03-07T14:03:38Z
dc.date.issued2016
dc.date.submitted2021-06-02T10:08:05Z
dc.identifierONIX_20210602_10.5772/62587_274
dc.identifierhttps://library.oapen.org/handle/20.500.12657/49160
dc.identifier.urihttps://doab-dev.siscern.org/handle/20.500.12854/153548
dc.description.abstractThis chapter presents a methodology to optimize the capacity and power of the ultracapacitor (UC) energy storage device and also the fuzzy logic supervision strategy for a battery electric vehicle (BEV) equipped with electrochemical battery (EB). The aim of the optimization was to prolong the EB life and consequently to permit financial economies for the end-user of the BEV. Eight variables were used in the optimization process: two variables that control the energy storage capacity and power of the UC device and six variables that change the membership functions of the fuzzy logic supervisor. The results of the optimization, using a genetic algorithm from MATLAB®, are showing an increase of the financial economy of 16%.
dc.languageEnglish
dc.rightsopen access
dc.subject.classificationbic Book Industry Communication::P Mathematics & science::PB Mathematics::PBU Optimization
dc.subject.otherGenetic algorithm optimization, battery electric vehicle, fuzzy logic, ultracapacitor, electrochemical battery
dc.titleChapter Genetic Algorithm Optimization of an Energy Storage System Design and Fuzzy Logic Supervision for Battery Electric Vehicles
dc.typechapter
oapen.identifier.doi10.5772/62587
oapen.relation.isPublishedBy035ecc65-6737-43cf-a13a-6bdf67ce01f4
dc.relationisFundedByH2020-TWINN-2015


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