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Emerging Technologies for Electric and Hybrid Vehicles
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Energies 2016,9, 997 Figure19.Optimizationmodel for theHEVenergymanagementkeyparameters. Table11.KeyparametersofDIRECTalgorithm. KeyParameters Value Maximumnumberof iterations 20 Maximumnumberof functioncalculation 1000 Maximumdivisiontimepersideofhyper-rectangle 100 Global/localweightingcoefficient 0.0001 Relativeerror 0.01% x1, x2, x3, x4, x5, x6, x7 are the parameters that need to be optimized. Themeaning of these parametersaredescribed inTable3. The initialvalue, range,andoptimizedvalueof theseparameters areshowninTable12. Table12.OptimizedresultofkeyparametersbasedonDIRECTalgorithm. Parameters InitialValue LowerLimit UpperLimit OptimizedValue x1 0.4 0.2 0.6 0.3889 x2 0.3 0.1 0.5 0.3556 x3 70 50 70 53.333 x4 40 30 50 47.963 x5 20 15 25 22.222 x6 50 40 60 50.667 x7 15 10 20 18.333 Changes to the parameters to be optimized have a big impact on the fuel economy. In the optimizationprocessofDIRECTalgorithm, theequivalent fuel consumptionper100kmfordifferent iterationfunctionevaluations is showninFigures20and21, respectively. 301
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Emerging Technologies for Electric and Hybrid Vehicles
Titel
Emerging Technologies for Electric and Hybrid Vehicles
Herausgeber
MDPI
Ort
Basel
Datum
2017
Sprache
englisch
Lizenz
CC BY-NC-ND 4.0
ISBN
978-3-03897-191-7
Abmessungen
17.0 x 24.4 cm
Seiten
376
Schlagwörter
electric vehicle, plug-in hybrid electric vehicle (PHEV), energy sources, energy management strategy, energy-storage system, charging technologies, control algorithms, battery, operating scenario, wireless power transfer (WPT)
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Technik
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Emerging Technologies for Electric and Hybrid Vehicles