Seite - 61 - in Emerging Technologies for Electric and Hybrid Vehicles

Energies 2017,10, 1217 efficientwayor turningsomeof theseoff can increase therangeofavehicle. LEDscanbeusedfor lightingbecauseof theirhighefficiency [169]. Table 30 showsdifferentmethodsof recovering the energy lostduringbraking. Table30.Differentmethodsof recoveringenergyduringbraking[169]. StorageSystem EnergyConverter RecoveredEnergy Application Electric storage Electricmotor/generator ~50% BEV,HEV Compressedgasstorage Hydraulicmotor >70% Heavy-dutyvehicles Flywheel Rotationalkineticenergy >70% FormulaOne(F1) racing Gravitationalenergystorage Springstoragesystem - Train Aerodynamic techniquesareusedinvehicles toreducethedragcoefficient,whichreduces the requiredpower. Powerneededtoovercomethedragforce is: Pd= 1 2 ρv3ACd (7) HereCd is thedragcoefficient, thepower toovercomethedrag increases if thedragcoefficient’s value increases. TheToyotaPriusclaimsadragcoefficientof0.24 for the2017model, thesameas the TeslaModelS.The2012NissanLeafSLhadthisvaluesetat0.28 [171]. To ensure efficient use of the available energy, different energymanagement schemes canbe employed[6]. Presenteddifferentcontrol strategies forenergymanagementwhich includedsystems usingfuzzylogic,deterministic ruleandoptimizationbasedschemes.Gengetal.,workedonaplug-in serieshybridFCV.Theobjectiveof their control systemwas to consume theminimumamountof hydrogenwhilepreservingthehealthof theprotonexchangemembranefuel cell (PEMFC)[172]. The control systemwascomprisedof twostages; thefirst stagedeterminedtheSOCandcontrol references, whereas the second stage determined the PEMFChealth parameters. Thismethodproved to be capableof reducingthehydrogenconsumptionwhile increasing the life-timeto the fuel cell.Another intelligentmanagementsystemisexaminedin [173]byMurpheyetal.,whichusedmachine learning combinedwithdynamicprogrammingtodetermineenergyoptimizationstrategies for roadwayand traffic-congestionscenarios for real-timeenergyflowcontrolofahybridEV.Their systemissimulated usingaFordEscapeHybridmodel; it revealed the systemwaseffective infindingout congestion level, optimal battery power and optimal speed. Geng et al., proposed a controlmechanism for energymanagement for a PHEVemploying batteries and amicro turbine in [174]. In thiswork, they introducedanewparameter, named the“energy ratio”, toproduce theequivalent factor (EF) whichwasused in thepopularEquivalentConsumptionMinimizationStrategy(ECMS) todeduce the minimumdrivingcostbyapplyingPontryagin’sminimumprinciple. Thismethodclaimedtoreduce thecostby7.7–21.6%. In [175],Mouraetal., exploredefficientways tosplitpowerdemandamong differentpowersourcesofmid-sizedsedanPHEVs. Theyusedanumberofdrivecycles, rather thana singleone,assessedthepotentialofdepletingcharge inacontrolledmanner,andconsideredrelative pricingof fuelandelectricity foroptimalpowermanagementof thevehicle. 11.ControlAlgorithms Control systemsarecrucial forproper functioningofEVsandassociatedsystems. Sophisticated controlmechanismsarerequiredforprovidingasmoothandsatisfactoryridequality, forproviding the enoughpowerwhen required, estimating the energyavailable fromtheon-board sources and usingthemproperly tocover themaximumdistance, charging inasatisfactory timewithoutcausing burdenonthegrid,andassociatedtasks.Differentalgorithmsareusedin theseareas,andas theEV culture isbecomingmoremainstream,needforbetteralgorithmsareontherise. Drivingcontrolsystemsarerequiredtoassistthedriverinkeepingthevehicleincontrol,especially athighspeedsandinadverseconditionssuchasslipperysurfacescausedbyrainorsnow.Driving 61