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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
Emerging Technologies for Electric and Hybrid Vehicles
- Title
- Emerging Technologies for Electric and Hybrid Vehicles
- Editor
- MDPI
- Location
- Basel
- Date
- 2017
- Language
- English
- License
- CC BY-NC-ND 4.0
- ISBN
- 978-3-03897-191-7
- Size
- 17.0 x 24.4 cm
- Pages
- 376
- Keywords
- 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)
- Category
- Technik