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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.
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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