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Emerging Technologies for Electric and Hybrid Vehicles
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Energies 2017,10, 1314 3.5. Process-BasedCostModeling Thissectionwill focusoncostandsalespricescalculationsandpredictionsbyusingprocess-based costmodelingofseveralbatterychemistries. Themethodologyofprocess-basedcostmodeling is to calculatecostsbasedondetailedprocessdescriptions,whicharewelldefinedforbatteries [52,53]. This methodology isbeingused in several applicationfields includingbatterycost calculations [54–58]. Batteryproduction for automotive cells are still in anearlyphase andnotyet inmassproduction. Thismeansoptimizations in theproductionprocessarepossiblebyupscalingandbybuildingmore production plants. Process-basedmodeling can be combinedwith learning or dynamic curves, which are dependent ongrowth of the production capacity as demonstrated in other application fields [59]. Thisstudy[59]hasperformedathoroughanalysisabout thepriceevaluationofchemical productsduringadecadeandlinkingthe impactof increasingproductioncapacities to theevolution ofprices. Foranalysisof thepriceevolutionsupto2030process-basedcostmodelingextendedwith learningcurveswill beused. Themaindrawbacksof this approachare that theexactprocess and compositionof thebatterychemistryhas tobeknown.Thiscanbeovercomebycombiningrecently published patents, which describe the processes in detail, aswell as by performing an extended literaturereview. The cost of two different cell chemistries will be analyzed, namely battery I (NMC(6:2:2) + Graphite)andbattery II (NMC(6:2:2)+siliconalloy).NMCbasedbatteriescanbeseenas thecurrent stateof theartbatteriesandsiliconbasedonesas stateof theartbatteries in10–15yearsas shown by the roadmap inFigure4. Process-basedcostmodeling isused inorder to calculate thedetailed material cost indollar/kWhfor eachbattery type. Themethodology, including thekeyequations, arerepresented inFigure5. In thefirst stepthemanufacturingprocedure is split into logical substeps ofwhichthematerialandenergyinandoutflowsareanalyzed,which isdone inSection3.4. In this stepalso theexcessmaterial,usedduringthemanufacturing, shouldbe taken intoaccount. Thenext stepwill calculate the cost of goods sold,which entails thematerial, energy, labor andoverhead costs. Twoadditional assumptionsaremade regarding the laborandoverheadcost,whichcanbe approximatedby respectively 15%and8%asdemonstrated in [60]. When comparingwith other calculationsdone in literature [54,56,61] similarassumptionswereused. Thesalesprice includingthe manufacturer’sandretailer’sprofitaretakenintoaccount.Aprofitmarginof35%forthemanufacturer isused,which ishighbut it entailsnovelproducts requiringahighprofitmargin [60].Whenthese batteriesarenotdirectlysoldbythemanufacturerandadditionalprofitmarginof15%bytheretailer isadded. This leads to thefinal salesprice. Thepredictionof thepriceswillbedetailed inSection3.6. Throughout this paper several assumptions aremade. Firstly all priceswill be expressed in dollar/kWh in2015. All priceswill be expressed inprice in2015 tomake it easier to compare the evolutions. Thiswill imply that thepriceofabatterywillbehigher in2030 thanpredicteddueto the inflation,whichwilloccurbetween2015and2030. Figure5.Overviewof theusedmethodology including learningcurves. 115
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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)
Kategorie
Technik
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Emerging Technologies for Electric and Hybrid Vehicles