50204.pdf
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 lif...
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InTechOpen
2021
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oapen-20.500.12657-491602021-11-23T14:04:30Z Chapter Genetic Algorithm Optimization of an Energy Storage System Design and Fuzzy Logic Supervision for Battery Electric Vehicles Breban, Stefan Genetic algorithm optimization, battery electric vehicle, fuzzy logic, ultracapacitor, electrochemical battery bic Book Industry Communication::P Mathematics & science::PB Mathematics::PBU Optimization 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%. 2021-06-02T10:08:05Z 2021-06-02T10:08:05Z 2016 chapter ONIX_20210602_10.5772/62587_274 https://library.oapen.org/handle/20.500.12657/49160 eng application/pdf n/a 50204.pdf InTechOpen 10.5772/62587 10.5772/62587 09f6769d-48ed-467d-b150-4cf2680656a1 H2020-TWINN-2015 692224 open access |
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English |
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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%. |
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