Deterministic and Stochastic Total Cost of Ownership (TCO ... · Introduction Motivation Motivation...

Deterministic and Stochastic Total Cost of Ownership(TCO) Analysis for commercial vehicles in Germany

Master Thesis Presentation

Hiteshkumar Amipara

Renewable Energy and Energy Efficiency for the Middle East and North Africa Region [REMENA]University of Kassel

March 5, 2019

Hiteshkumar Amipara TCO March 5, 2019 1 / 61

Table of Contents

Table of Contents

1 Introduction

2 Methodology

3 Results

4 Conclusions and Future Work

5 References


Introduction

Table of Contents

1 IntroductionMotivationThesis Contribution

2 MethodologyTCO calculation structureVehicle drivetrains and segmentsDeterministic TCO analysisValidationStochastic TCO analysis

3 ResultsDeterministic TCOStochastic TCO


5 References


Introduction Motivation

Motivation

The transport sector is the third largest cause of 160 million tonnes of CO2

emission in 2015 [25].

Increasing road freight vehicles with Low-efficiency improvements and low fuelprices have led to increase emissions in 2016.

Government of Germany has decided the national goal to decrease greenhousegas (GHG) emissions 40 % by 2020, 50 % by 2030, and 80-95 % by 2050(compared to 1990) [1].

Electric vehicles fuelled by renewable energy sources are expected to reduce CO2

emissions in commercial vehicle areas.

Majority of existing Total cost ownership (TCO) studies: Deterministic inputparameters.


Introduction Thesis Contribution

Thesis Contribution

Provide a deterministic and stochastic TCO comparison among three drivetraintechnologies for commercial vehicles in Germany from 2020 to 2030.

Effect of additional storage weight (drivetrain characteristics, daily mileage) ontransport capacity.


Methodology TCO calculation structure

Table of Contents





5 References


Methodology TCO calculation structure

TCO calculation structure of each vehicle

Figure: TCO calculation structure [4]

TCO is spilt into CAPEX andOPEX.

CAPEX has the initial purchasingprice (IPP) and resale price.

OPEX includes fixed cost andvariable cost.


Methodology Vehicle drivetrains and segments

Vehicle drivetrains and segments

Three drivetrain technologies

Internal combustion engine vehicle(ICEV), Battery electric vehicle (BEV),Fuel cell electric vehicle (FCEV)

Figure: Vehicle drivetrains

Vehiclesegment

Vehicle segmentshare

Categories 1 Passenger car 26 %Categories 2 Local bus -

Categories 3LDV 75%MDV 7%HDV 9%

Categories 4MHV(Class-1) -MHV(Class-2) -MHV(Class-3) -

Table: Vehicle segments [2][3]

Four vehicle segments

Passenger car, Local bus

Road freight vehicles: Light-dutyvehicle (LDV), Medium duty vehicle(MDV), Heavy-duty vehicle (HDV)

Material handling vehicle (MHV)Go to


Methodology Deterministic TCO analysis

Deterministic TCO analysis

Figure: Workflow of the deterministic TCO analysis

TCO defined as T :

T =

(I −RP )C + 1N

N∑n=1

Aoc(1+i)n

A(1)

where,

T : total cost of ownership in e/kmor e/h

I: initial purchasing price (IPP) ine

A: could be AK (annual kilometerstravelled) in km or AH (annualworking hours) in h

Aoc: annual operating cost in e

R: resale price in e

C: capital recovery factor

P : present value factor

N : vehicle holding period of thefirst owner in years

i: interest rate in %



Input parameters: Investment cost of the vehicle components

Component Description Unit 2020 2025 2030Drivetrain Combustion engine e/kW 60 63.5 67

Electric motor e/kW 18 16 14PEM fuel cell system e/kW 180 65 35

Energystorage

Hydrogen storagetank

e/kWh 11.4 10.26 9.12

Li-ion battery pack e/kWh 155 105 90Miscellaneous Converter, Controller,

Invertere/kW 24 21 18

Table: Investment cost of different vehicle components [6][7][11][14]

Battery pack cost decreases over the years due to the rising production units, aswell as technology improvements.

Fuel cell system cost decreases rapidly.

Internal combustion engine costs are expected to increase over time.

Go to



Input parameters: Investment cost of different vehiclecomponents

Vehiclesegment

Body cost [e] Daily meanmileage [km]

Categories 1 Car 9468 100Categories 2 Bus 240000 243

Categories 3LDV 20000 88MDV 40000 225HDV 60000 430

Categories 4MHV(Class-1) 25000 -MHV(Class-2) 25000 -MHV(Class-3) 7800 -

Table: Input parameters [4][5][12]

Body cost is determined so that estimated values match real sale price.

ValueNumber of shifts per day 2Number of hours per shift 7.25Annual working hours 2000

Table: Vehicle usage parameters for MHV (Class-1,2,and 3) [16]



Initial purchasing price (IPP) and storage capacity:′′Bottom-up approach′′

Figure: Workflow of IPP (Bottom-up approach)

Figure: Vehicle drivetrains

IPP is calculated using the followingequation :

I = PcRp (2)

where,

I: initial purchasing price in e

Pc: production cost in e

Rp: retail price equivalent factor

The power of engine, electric motor and fuel cell system is based on referencevehicle.



Structure of Initial purchasing price

Figure: Structure of Initial purchasing price

IPP was calculated at a dailymileage of 300 km.

FCEV has the IPP of 54,995e with storage cost 1,491 e.

BEV has the IPP of 35,797 ewith storage cost 15,295 e.

ICEV has the IPP of 25,165e.



Resale price of the Vehicle

Resale price (R) in e for passenger car [17]:

R = eαe12β1LeAKβ2

12 Iβ3 (3)

where,

α: absolute term

β1,β2, β3:coefficient estimators

L: lifetime of vehicle in year

R in e for the local bus and road freight vehicle can be determined by followingequation [18]:

R = 0.10I (4)



Operating expenditures (OPEX) calculation

Figure: Structure of OPEX

Fuel 2020 2025 2030Energycarrierprice

Diesel(e/L)

1.37 1.635 1.9

Electricity(e/kWh)

0.217 0.234 0.250

Hydrogen(e/kg)

12.62 11.309 10.65

Table: Fuel price [7][19]

Go to

Energy cost is the largest share of the vehicle′s operating cost.

The energy consumption data of passenger car, local bus and MHV is based on areview of the literature sources.

For road freight vehicle, a linear regression analysis is used to calculate the fuelconsumption.



Additional weight effect of a battery electric versusconventional drivetrain

Figure: Battery pack density impacts on BEB weight andrange

Parameters ValueEnergy consumption for battery electricbus at the wheels (kWh/km)

1.5

Mean Mileage (km) 243Battery pack size (kWh) 455.62Energy density of battery pack (Wh/kg) 235Battery pack weight (kg) 1938.8Power of electric motor for bus (kW) 150Weight of electric motor (kg) 30Total weight of battery and motor (kg) 1968.8Energy consumption for diesel bus(kWh/km)

4

Power of conventional engine (kW) 260Weight of drivetrain and fluids in dieselbus (kg)

491

Net additional weight for battery electricbus (BEB) versus diesel bus (kg)

1477

Loss of passenger capacity for BEB versusdiesel bus (%)

35.1

Table: Input parameters [23]

Curb weight is 13,800 kg and transport weight is 4,200 kg.With 987 Wh/kg, BEB is successfully driven without any weight penalties.

Go to



Effect of additional storage weight on transport capacity

Figure: Workflow of the correction factor

The available mass of transport capacity has beencalculated in terms of correction factor:

C =Mcapacity

Mcapacity −Mstorage(5)

where,

C: correction factor

Mcapacity: total transport capacity in kg

Mstorage: total storage weight in kg


Methodology Validation

Validation of TCO analysis:Passenger Car

Figure: Validation [21]

Go to


Methodology Stochastic TCO analysis

Stochastic TCO analysis

Figure: Workflow of the stochastic analysis

Vehicles Unit Stochastic inputparameters

ICEV e/L Diesel price

BEVe/kWh Electricity pricee/kWh Battery pack cost

FCEVe/kW Fuel cell system coste/kWh Hydrogen storage coste/kg Hydrogen price

ICEV, BEV,and FCEV

km Daily range

Table: Stochastic parameters

Non-Stochastic inputparameters

Annual fixed cost Vehicle tax, Insurance costAnnual running cost Maintenance & repair, Tire

cost, Driver wage

Table: Non-Stochastic parameters

Go to


Results

Table of Contents





5 References


Results Deterministic TCO

Results:Deterministic TCO for Passenger car

Figure: Deterministic analysis for Passenger car



Results:Deterministic TCO for Local bus

Figure: Deterministic analysis for Local bus

Internal combustion engine bus (ICEB), Battery electric bus (BEB), Fuel cellelectric bus (FCEB)



Results:Deterministic TCO for Light-duty vehicle

Figure: Deterministic analysis for LDV

CLDV (Conventional LDV), BLDV (Battery electric LDV), FLDV (Fuel cellelectric LDV).



Results:Deterministic TCO for Medium duty vehicle andHeavy-duty vehicle

Figure: Deterministic analysis for MDV

CMDV (Conventional MDV), BMDV(Battery electric MDV), FMDV (Fuelcell electric MDV)

Figure: Deterministic analysis for HDV

CHDV (Conventional HDV), BHDV(Battery electric HDV), FHDV (Fuelcell electric HDV)



Results:Deterministic TCO for Material handling vehicle(Class-1 & 2)

Figure: Deterministic analysis for MHV (Class-1) Figure: Deterministic analysis for MHV (Class-2)

BMHV (Battery electric MHV), FMHV (Fuel cell electric MHV).



Results: Deterministic TCO for Material handling vehicle(Class-3)

Figure: Deterministic analysis for MHV [Class-3]

MHV (Class-3) has the lower TCO than MHV (Class-1 and 2), reflecting bothlower CAPEX for the lift truck and power pack.


Results Stochastic TCO

Results: Stochastic TCO for Passenger Car

Figure: Stochastic analysis for Passenger car



Results: Stochastic TCO for Local bus

Figure: Stochastic analysis for Local bus



Results: Stochastic TCO for Light-duty vehicle

Figure: Stochastic analysis for Light-duty vehicle



Results:Stochastic TCO for Medium duty vehicle andHeavy-duty vehicle

Figure: Stochastic analysis for Medium duty vehicle

FMDV and CMDV havealmost the same results by2030.

BHDV is likely to be thetechnology with the highestTCO.

Figure: Stochastic analysis for Heavy-duty vehicle



Results:Stochastic TCO for Material handling vehicle(Class-1 & 2)

Figure: Stochastic analysis for MHV (class-1)

Fuel cell MHV (Class-1,2) islikely to have a lower meanTCO.

Battery electric MHV has thelower CAPEX and fuel cost.

Figure: Stochastic analysis for MHV (class-2)



Results: Stochastic TCO for MHV (Class-3)

Figure: Stochastic analysis for MHV (Class-3)


Conclusions and Future Work

Table of Contents





5 References



Conclusions 1/3

For the Deterministic TCO analysis:

For passenger cars, the BEV shows the potential to become the cost-competitivetechnology after 2020.

For local buses, the BEB and FCEB are likely to remain as the cost inefficienttechnologies.

For road freight vehicles, the market potential for conventional vehicles in theLDV and HDV segment from 2020 to 2030 was identified. On the other hand,BMDV and CMDV have the almost same TCO for MDV segment by 2030.

In terms of equipment cost, FMHVs are more expensive than BMHVs butconsidering the multi-shift warehouse operations, FMHVs are comparable toBMHVs.



Conclusions 2/3

For the Stochastic TCO analysis:

For passenger cars, BEV is the least expensive drivetrain after 2020, while theFCEV is likely to become cost-competitive after 2025.

For local buses, LDVs, and MDVs, the conventional vehicle has the lowest meanTCO.

The HDV segment is likely to have FCEV as the technology with the lowestTCO indicated by the mean TCO.

For MHVs (Class-1,2, and 3), the FMHV has the lowest mean and minimumvalue in comparison to BMHV.



Conclusions 3/3

The cost-competitiveness of the BEV is strongly linked with the assumed fuelprices for electricity, assumed battery pack cost as well as the specific energydensity of battery pack.

The comparative cost efficiency of the FCEV is strongly connected with theassumed fuel prices for hydrogen as well as the assumed fuel cell system cost.

BEVs are expected to be more economical at lower ranges and lower utilizationrates, whereas FCEVs provide very promising economic performance in case ofhigh range and high utilization of the vehicle.



Future Work

Include external costs such as climate external costs (greenhouse gas emissions)and Health external costs (air quality and noise).

Include Government subsidies.



CO2 emission calculation for Passenger Car and Bus

Calculates annual CO2 emission asspecified in the below equation [6]:

Ec = Ee (6)

where

Ec: CO2 emission in g CO2/a

E: Energy consumption in kWh/a

e: emission factor in g CO2/kWh

Car Fuel consumption[kWh/a]

CO2 emissionfactor[g CO2/kWh]

ICEV 12,838 239BEV 5,636 -FCEV 7,616 -

Table: CO2 emission factor and fuel consumption forCar [6]

Bus Fuel consumption[kWh/a]

CO2 emissionfactor[g CO2/kWh]

ICEB 264,384 239BEB 99,144 -FCEB 185,068 -

Table: CO2 emission factor and fuel consumption forBus

Annual CO2 emission for a ICEV is around 3.068 t CO2/a (112.806 g CO2/km).

Annual CO2 emission for a ICEB is around 63 t CO2/a (956 g CO2/km).


References

Table of Contents





5 References


References

F Bergk, W Knorr, and U Lambrecht

Climate Protection in Transport–Need forAction in the Wake of the Paris Climate Agreement.

Climate Change Mitigationin Transport until 2050 (2017).

Letmathe, P. and M. Suares

A consumer-oriented total cost of ownership model for different vehicle types in Germany.

Transportation Research Part D: Transport and Environment, 2017.

Kleiner, F. and H.E. Friedrich

Scenario analyses for the techno-economical evaluation of the market diffusion of future commercial vehicleconcepts.

2017.

Wu, G., A. Inderbitzin, and C. Bening

Total cost of ownership of electric vehicles compared to conventional vehicles: A probabilistic analysis andprojection across market segments.

Energy Policy, 2015.

Den Boer, Eelco and Aarnink, S and Kleiner, F and Pagenkopf, J

Zero emissions trucks: An overview of state-of-the-art technologies and their potential.

2013.

Bubeck, Steffen and Tomaschek, Jan and Fahl, Ulrich

Perspectives of electric mobility: Total cost of ownership of electric vehicles in Germany.

Transport Policy,2016.

Moultak, Marissa and Lutsey, Nic and Hall, Dale

Transitioning to zero-emission heavy-duty freight vehicles.

2017.


References

Gnann, T and Wietschel, M and Kuhn, A and Thielmann, A and Sauer, A and Plotz, P and Moll, C and

Stutz, S and Schellert, M and Rudiger, D and others

Brennstoffzellen-Lkw: kritische Entwicklungshemmnisse, Forschungsbedarf und Marktpotential.

Wissenschaftliche Beratung des BMVI zur Mobilitats-und Kraftstoffstrategie. 2017.

James, Brian

2018 Cost Projections of PEM Fuel Cell Systems for Automobiles and Medium-Duty Vehicles Question andAnswer Motivation and Outline.

2018.

https://www.now-gmbh.de/en/national-innovation-programme/projektfinder/verkehr/

autostack-industrie.

Drive, US

Target explanation document: onboard hydrogen storage for light-duty fuel cell vehicles.

Department of Energy,2015.

Ramsden, Todd

Evaluation of the Total Cost of Ownership of Fuel Cell-Powered Material Handling Equipment.

2013.

Reid - Marketing Specialist, Catharine

FUEL CELL ELECTRIC BUSES: AN ATTRACTIVE VALUE PROPOSITION FOR ZERO-EMISSIONBUSES IN THE UNITED KINGDOM.

2016.

Stapelbroek, D.-I.M.

BT 2019 Stapelbroek FEV Battery trends.

2019.


https://www.now-gmbh.de/en/national-innovation-programme/projektfinder/verkehr/autostack-industrie

https://www.now-gmbh.de/en/national-innovation-programme/projektfinder/verkehr/autostack-industrie

References

Ammermann, Heiko and Ruf, Yvonne and Lange, Simon and Fundulea, Dragos and Martin, Andre

Fuel Cell Electric Buses–Potential for Sustainable Public Transport in Europe A Study for the Fuel Cells andHydrogen Joint Undertaking, Roland Berger GmbH, Munchen, Germany, 2015, 52 p.

2016.

R. Micheli M.Sc.,Lehrstuhl fml Dipl.-Ing. M. Hanke, Linde Material Handling AG

Einsatz einer wasserstoffberiebenen Flurforderzeugflotte unter Produktionsbedingungen: Herausforderungen,Wirtschaftlichkeit, Nachhaltigkeit.

2015.

Dexheimer, Verena

Hedonic methods of price measurement for used cars.

Statistisches Bundesamt (Destatis), zuletzt abgerufen am, 2003.

Mathieu, Lucien

Marketplace, economic, technology, environmental and policy perspectives for fully electric buses in the EU.

2018.

Goehlich, D and Spangenberg, F and Kunith, A

Stochastic total cost of ownership forecasting for innovative urban transport systems.

838–842,2013.

Cerniauskas, Simonas and Grube, Thomas and Robinius, Martin and Stolten, Detlef

Hydrogen Supply Chain Costs for Different Geographical Distribution and Technology Penetration Scenarios.

2018.

Propfe, Bernd and Redelbach, Martin and Santini, Danilo and Friedrich, Horst

Cost analysis of plug-in hybrid electric vehicles including maintenance & repair costs and resale values.

World Electric Vehicle Journal,2012.


References

Meeus, Marcel Meeus -Emiri, Marcel

Overview of Battery Cell Technologies Overview of battery technologies Bridging the Innovation Gap.

2018.

Karlsson, Elin

Charging infrastructure forelectric city buses: An analysis of grid impact and costs.

2016.

Loogen, F.

Nullemissions-nutzfahrzeuge Vom okologischen HoffnungstrA¤ger zur okonomischen Alternative.

2017.

CO2 emissions Target in Germany.

2017.

Plotz, Patrick and Gnann, Till and Wietschel, Martin

Total ownership cost projection for the German electric vehicle market with implications for its future powerand electricity demand.

2012.


References

Thank You!


References

Back-Up Slides


References

Vehicle usage and TCO calculation

Vehicle usage and input parameters

Car Bus LDV MDV HDVMean dailymileage [km]

100 243 88 225 430

Vehicle lifetime[years]

10 10 10 10 10

MHV (Class-1,2, and 3) ValueNumber of shifts per day 2Number of hours per shift(hours)

7.25

Annual working hours(hours)

2000

ValueDiscount rate (i) 8%Vehicle holding period(N)

10 years

Retail Price Equivalentfactor (RPE)

1.5

Working day 272


References

Input parameters

Input parameters

LDV MDV HDV Bus CarEngine power [kW] (only for ICEVs) 100 150 350 260 110Electric motor [kW] (for BEVs andFCEVs)

100 150 350 150 100

Fuel cell [kW] (only for FCEVs) 100 150 300 100 115HVAC [kW] (only for FCEB andBEB)

- - - 28 -

Table: Characteristics of the reference vehicle

MHV(class-1)

MHV(class-2)

MHV(class-3)

Fuel cell power [kW] 10 8 2Electric motor [kW](for FCEVs)

8 6 2

Electric motor [kW](for BEVs)

10 10 5

Table: Characteristics of the reference vehicle


References

Effect of storage weight on transport capacity

Figure: Workflow for the correction factor

Characteristics ValueICEV Power density of engine [W/kg] 650

Density of diesel fuel [kg/L] 0.83Tank weight [g/L] 200

BEVandFCEV

Energy density of battery pack [Wh/kg] 235

Power density of E-motor [kW/kg] 5Power density of fuel cell system [W/kg] 650Compressed 700 bar H2 storage [kg/100kg] 5.4

Table: Characteristics of drivetrains technologies [5][22]

The effect of storage weight on transport capacity is expressed by thefollowing equation (5):

C =Mcapacity

Mcapacity −Mstorage(7)

where,

C: correction factor

Mcapacity : Total transport capacity in kg

Mstorage: Storage weight in kg


References

Additional weight effect of a battery electric versus fuel cellelectric drivetrain

Weight consideration analysis

Figure: Weight consideration analysis

Parameters ValueEnergy consumption for fuel cell electricbus at the wheels (kWh/km)

2.72

Mean Mileage (km) 243Weight of hydrogen storage (kg) 367Power of fuel cell system (kW) 100Power of electric motor for bus (kW) 150Weight of electric motor (kg) 30Total storage weight of fuel cell electricbus (kg)

732.25

Total storage weight of BEB (kg) 1968.8Net additional weight for BEB versus fuelcell electric bus (FCEB) (kg)

1235.7

Loss of passenger capacity for BEB versusFCEB (%)

29.42

Table: Input parameters [15]

The required energy density of the battery pack is 650 Wh/kg.


References

Operating expenditures

Energy consumption (Linear regression analysis) for Road freight vehicle

Energy consumption


References


Energy consumption for Car, Local Bus, and MHV

Figure: Energy consumption for car and local bus

Figure: Energy consumption for MHV

Figure: Additional efficiency gain per year


References


Maintenance and repair cost

Figure: Maintenance and repair cost

Figure: Maintenance and repair cost for MHV


References


Other Operating costs

Figure: Fixed operating cost parameters

Figure: Key operating parameters


References

Validation for MDVsValidation

Input parameters Unit 2020Glider price for all threedrivetrains

e 40000

Hydrogen price e/kg 4.77Electricity price e/kWh 0.127Daily Range km 200Annual kilometretravelled

km 52000

Diesel price e/L 1.18Battery pack cost e/kWh 240Internal combustionengine cost

e/kW 60

Fuel cell system cost e/kW 190Hydrogen storage cost(700 bar)

e/kWh 18

Battery pack capacity kWh 230Holding period year 10Interest rate % 4

Table: Input parameters

Used different RPE

Bottom-up approach


References

Validation for HDVs

Input parameters Unit 2020Glider price for allthree drivetrains

e 60000

Hydrogen price e/kg 4.77Electricity price e/kWh 0.112Daily Range km 1000Annual kilometretravelled

km 141,960

Diesel price e/L 1.18Battery pack cost e/kWh 240Fuel cell systemcost

e/kW 190

hydrogen storagecapacity (700 bar)

kWh 2570

Hydrogen storagecost (700 bar)

e/kWh 18

Battery packcapacity

kWh 152

Holding period year 8Interest rate % 4


Used different RPE

Bottom-up approach


References

Validation for Bus

Input parameters Unit 2018Base vehicle pricefor electric bus

e 247863

Base vehicle pricefor diesel bus

e 213675

Electricity price e/kWh 0.112Daily Range km 250Annual kilometretravelled

km 91250

Diesel price e/L 1.175Battery pack cost e/kWh 335Battery packcapacity

kWh 451

Holding period year 8Interest rate % 4


Excluding external health cost

Bottom-up approach


References

Stochastic analysis: Stochastic cost variablesStochastic analysis: stochastic parameters

Figure: Stochastic analysis of Hydrogen storage cost

Figure: Stochastic analysis of Fuel cell system cost


References

Stochastic analysis: Battery pack cost

Cost will decrease over the years

SD will decrease

2020 2025 2030- 84 75291.118 165.35 151.67259.92 191.32 136.28240 200 161401.33 293.67 186404 332 260155 105 90163.02 139.08 -182.4 145.92 -217.74 136.8 91.2361.38 96.9 57209.76 151.62 -173.28 - -182.4 - -148.2 - -



References

Stochastic analysis: Electricity price

Input parameters yearElectricity price(e/kWh)

2020 0.163 0.213 0.216 0.24 0.292

2025 0.182 0.216 0.239 0.252 0.3122030 0.19 0.222 0.250 0.265 0.284



References

Stochastic analysis: Diesel price

Input parameters yearDiesel price (e/L) 2020 1.163 1.37 1.788 1.792 1.59

2025 1.346 1.635 1.863 1.979 1.682030 1.53 1.90 2.106 2.185 1.78



References

Stochastic analysis: Hydrogen price

Input parameters yearHydrogen price(e/kg)

2020 8.29 7.95 12.62 11.012 10.3323

2025 6.69 7.3 11.309 9.47 10.362030 5.09 6.65 10.65 7.9458 10.36



References

Stochastic analysis: Daily mean range

Vehicles Unit Bus Car LDV MDV HDVMinimum daily range (assumed 25 % of trip) km 206 24 22 80 251Mean daily range km 243 100 88 225 430Maximum daily range (assumed 95 % trip forcar and 90 % trip for remaining vehicles)

km 329 368 209 499 724

Average annual range km 66,096 27,200 23,936 61,200 116,960



References

Initial purchasing price (IPP) calculation structure forBattery electric powertrain

IPP structure for all three drivetrains

Powertrain

cost (€)

Chassis and

Body cost (€)

Production

cost (€)

Retail price

equivalent

factor

Initial

Purchasing

Price (€)

Energy consumption

(kWh/km), Battery

pack efficiency, DOD

Battery pack

storage cost (€)

Electric motor cost (€)

Daily mileage

(km)

Battery pack capacity

(kWh) X cost per kWh


References

Initial purchasing price (IPP) calculation structure for Fuelcell electric powertrain

Powertrain

cost (€)

Chassis and

Body cost (€)

Production

cost (€)

Retail price

equivalent

factor

Initial

Purchasing

Price (€)

Energy consumption

(kg/km), Fuel cell

system efficiency

Hydrogen

storage cost (€)

Electric motor cost (€),

Fuel cell system cost (€),

Battery pack cost (€)

Daily mileage

(km)

Hydrogen storage

capacity (kg) X cost per

kg


References

Initial purchasing price (IPP) calculation structure forConventional powertrain

Powertrain

cost (€)

Chassis and

Body cost (€)

Production

cost (€)

Retail price

equivalent

factor

Initial

Purchasing

Price (€)

Energy consumption

(L/km), Engine

efficiency

Storage cost

(€)

Engine cost (€)

Daily mileage

(km)


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