Chapter Genetic Algorithm Optimization of an Energy Storage System Design and Fuzzy Logic Supervision for Battery Electric Vehicles
| dc.contributor.author | Breban, Stefan | |
| dc.date.accessioned | 2021-06-02T10:08:05Z | |
| dc.date.available | 2021-06-02T10:08:05Z | |
| dc.date.issued | 2016 | |
| dc.identifier | ONIX_20210602_10.5772/62587_274 | |
| dc.identifier.uri | https://library.oapen.org/handle/20.500.12657/49160 | |
| dc.description.abstract | This 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.language | English | |
| dc.subject.classification | bic Book Industry Communication::P Mathematics & science::PB Mathematics::PBU Optimization | |
| dc.subject.other | Genetic algorithm optimization, battery electric vehicle, fuzzy logic, ultracapacitor, electrochemical battery | |
| dc.title | Chapter Genetic Algorithm Optimization of an Energy Storage System Design and Fuzzy Logic Supervision for Battery Electric Vehicles | |
| dc.type | chapter | |
| oapen.identifier.doi | 10.5772/62587 | |
| oapen.relation.isPublishedBy | 09f6769d-48ed-467d-b150-4cf2680656a1 | |
| oapen.relation.isFundedBy | H2020-TWINN-2015 | |
| oapen.grant.number | 692224 | |
| oapen.grant.acronym | ESPESA |
