Impact of powertrain electrification, vehicle size reduction and lightweight materials substitution on energy use, CO2 emissions and cost of a passenger light-duty vehicle fleet

Energy 93 (2015) 1489e1504

Contents lists available at ScienceDirect

Energy journal homepage: www.elsevier.com/locate/energy

Impact of powertrain electrification, vehicle size reduction and lightweight materials substitution on energy use, CO2 emissions and cost of a passenger light-duty vehicle fleet
lez Palencia*, Tsukasa Sakamaki, Mikiya Araki, Seiichi Shiga Juan C. Gonza Division of Mechanical Science and Technology, Graduate School of Science and Technology, Gunma University, 1-5-1 Tenjincho, Kiryu, Gunma 376-8515, Japan

a r t i c l e i n f o

a b s t r a c t

Article history: Received 24 April 2015 Received in revised form 30 September 2015 Accepted 7 October 2015 Available online 19 November 2015

Electric-drive and lightweight vehicles can reduce CO2 emissions in road passenger transportation. However, maximum reductions are limited by the extent of their diffusion. A vehicle fleet stock turnover model was developed to study the impact of powertrain electrification, vehicle size reduction and lightweight materials substitution on light-duty vehicle fleet energy consumption, CO2 emissions and cost; and used in the case of Japan. Vehicle types included internal combustion engine vehicles, hybrid electric vehicles, battery electric vehicles, and fuel cell hybrid electric vehicles; using two glider types, conventional and lightweight; available in three vehicle size classes, normal, compact and mini-sized vehicles. Diffusion of mini-sized lightweight battery electric vehicles has the largest potential for tankto-wheel energy consumption and CO2 emissions reductions, 70.6 and 92.2%, compared to the 2050 baseline values; with a net cash flow larger than zero until 2045. In contrast, diffusion of mini-sized lightweight fuel cell hybrid electric vehicles has the lowest net cash flow by 2050, with negative values from 2033 and potential tank-to-wheel energy consumption and CO2 emissions reductions of 55.4 and 82.9% compared to the 2050 baseline values. Lightweighting reduces significantly the capital cost of battery electric vehicles and fuel cell hybrid electric vehicles, favoring their deployment. © 2015 Elsevier Ltd. All rights reserved.

Keywords: Light-duty vehicle fleet Car stock model Downsizing Lightweight materials Electric-drive vehicles

1. Introduction During 2010, transport sector accounted for 27% of global final energy use, produced 6.7 Gt-CO2 direct emissions and represented 14% of GHG (global greenhouse gas) emissions, 7.0 Gt-CO2-eq [1]; with road transport dominating overall transport sector CO2 emissions [2]. Vehicle ownership (i.e. number of vehicles per inhabitant of a region) for road vehicles is high in developed countries, and tends to increase in the medium term in developing countries [3]. For instance, 2010 values for four-wheeler ownership were 657 vehicles/thousand people in North America and 426 vehicles/thousand people in the OECD (Organisation for Economic Co-operation and Development) Pacific countries; compared to

* Corresponding author. Tel./fax: þ81 277 30 1516.
lez Palencia), t14802034@ E-mail addresses: [email protected] (J.C. Gonza gunma-u.ac.jp (T. Sakamaki), [email protected] (M. Araki), shiga@ gunma-u.ac.jp (S. Shiga). http://dx.doi.org/10.1016/j.energy.2015.10.017 0360-5442/© 2015 Elsevier Ltd. All rights reserved.

140, 33 and 23 vehicles/thousand people in Latin America, Asia and Africa [4]. LDVs (Light-duty vehicles) consumed around 50% of world transport energy in 2010 [5]. LDVs are majorly fossil fuel-powered ICEVs (internal combustion engine vehicles), built with extensive use of iron and steel. Nevertheless, concerns about GHG emissions, oil-dependency and oil price volatility have encouraged the search for alternatives to reduce fossil fuel use and GHG emissions in road passenger transportation. Alternatives include more efficient powertrains, lower GHG emitting alternative fuels and powertrains, reduction of vehicle weight and drag, moderation of vehicle performance expectations, implementation of eco-driving behavior, increment of vehicle occupancy, modal shift and infrastructure management [6]. Furthermore, the IPCC (Intergovernmental Panel on Climate Change) concluded that TTW (tank-to-wheel) GHG emissions from passenger transport can be reduced using fuels with lower carbon intensities, lowering vehicle energy intensity, modal shift and avoiding trips when possible [2]. This research has focused on powertrain electrification, considering that aggressive and sustained mitigation policies are

1490


lez Palencia et al. / Energy 93 (2015) 1489e1504 J.C. Gonza

necessary to curb future CO2 emissions increments in transport sector [2], and significant reductions of GHG emissions can only be achieved using technologies that reduce drastically energy consumption [7]. Furthermore, electrifying transport sector can decrease oil dependency and CO2 emissions [8]; using low-carbon energy sources for electricity and hydrogen production to avoid shifting CO2 emissions to fuel production. Since every 10% glider mass reduction can lower fuel consumption by 7.0, 6.3, 5.0 and 3.3% for ICEVs, HEVs (hybrid electric vehicles), FCVs (fuel cell vehicles) and FCHEVs (fuel cell hybrid electric vehicles) [9], lightweighting through lightweight materials substitution and vehicle size reduction has also been considered. Nevertheless, replacing conventional fossil fuel-powered ICEVs with lighter and smaller vehicles using electric powertrains will change LDV fleet energy use, CO2 emissions and cost. Therefore, it is necessary to use quantitative methods to assess the impact of powertrain electrification, lightweight materials substitution and vehicle size reduction on energy use, CO2 emissions and costs of passenger LDV fleets, considering the dynamics of the vehicle stock turnover. This can contribute to quantify the mitigation potentials for electricity and hydrogen, considered very uncertain by the IPCC [2]. Previous research regarding powertrain electrification and lightweighting is extensive. The most relevant studies considering vehicle stock turnover are described below. In the context of developing countries, Du et al. [10] studied the impact of aluminum intensive vehicle diffusion on the Chinese LDV fleet energy and material use and GHG emissions between 2010 and 2020, without assessing the vehicle fleet cost. Also for China, Hao et al. [11] studied the effect of constraining vehicle registration, reducing vehicle travel, strengthening fuel consumption limits, vehicle downsizing and BEV (battery electric vehicle) penetration on passenger vehicle fleet energy consumption and GHG emissions between 2010 and 2050, without assessing the vehicle fleet cost. For
lez Palencia et al. [12] studied the effect of powColombia, Gonza ertrain electrification and lightweight materials use on passenger car fleet energy use, materials use, CO2 emissions and cost, without considering downsizing. In the context of developed countries, Bandivadekar et al. [13] assessed the potential of powertrain electrification, lightweight materials substitution and vehicle size reduction for energy consumption and CO2 emissions reduction in the LDV fleet of the United States, without estimating the LDV fleet cost. Cheah [14] studied the impact of lightweighting on energy and materials use for the United States LDV fleet, without estimating the LDV fleet cost. Wang [15] studied the penetration of PHEVs (plug-in hybrid electric vehicles), FCVs and BEVs in California between 2010 and 2030; considering the impact on energy use, CO2 emissions and fleet cost, without considering lightweighting. In the case of Europe, Kloess and Müller [16] studied the evolution of the passenger car fleet in Austria between 2010 and 2050, considering ICEVs, HEVs, PHEVs and BEVs without lightweighting. Pasaoglu et al. [17] evaluated the deployment of HEVs, PHEVs, BEVs and FCVs in Europe between 2010 and 2050, estimating WTW (well-towheel) energy consumption, CO2 emissions and costs. For South Korea, Lee et al. [7] assessed HEV, BEV and FCV deployment, estimating energy consumption and CO2 emissions between 2010 and 2050, without considering lightweighting. In the case of Japan, Nishimura [18] assessed energy use and GHG emissions in the LDV fleet until 2050, considering powertrain electrification without lightweighting; and without estimating LDV fleet cost. Yabe et al. [19] estimated the CO2 emissions reduction potential of BEVs and PHEVs in Japan, without considering lightweighting. In contrast, this research considers the effect of powertrain electrification, lightweight materials substitution and size

class reduction on passenger LDV fleet energy use, CO2 emissions and cost, taking into account vehicle stock turnover; something not found in the literature reviewed. The objective of this paper is to estimate the impact of powertrain electrification, vehicle size reduction and lightweight materials substitution on passenger LDV fleet energy consumption, CO2 emissions and cost, in the context of developed countries. Considering the acceptance of mini-sized vehicles, HEVs and BEVs, Japan was chosen as case of study. The rest of the paper is organized as follows: methods used for passenger LDV fleet modeling are presented in Section 2; results are presented and discussed in Section 3; and conclusions are presented in Section 4.

2. Methods 2.1. Passenger light-duty vehicle stock model formulation The considered energy system is shown in Fig. 1. 24 vehicle types were considered: four powertrain types, ICEV, HEV, BEV and FCHEV; two glider configurations, conventional and lightweight; and three vehicle size classes, normal vehicle, compact vehicle and mini-sized vehicle. The system was modeled using a dynamic bottom-up accounting energy-economic model developed in LEAP (Long-range Alternatives Planning System), based on the methods described in Ref. [12]. The mathematical formulation is presented below and the flowchart of the model is shown in Fig. 2. The model represents the dynamics of the passenger LDV stock turnover, calculating the stock of vehicles type t of a vintage u existing in a calendar year y using Eq. (1):

Nt;y;u ¼ St;v $FY¼yu

(1)

where S is the new vehicle sales, Y is the age of the vehicle and F is the survival fraction, estimated using Eq. (2) [18]:

1  FY ¼
a þ e½bðYY0 Þ

(2)

where a and b are model parameters and Y0 is the median vehicle service life. Total passenger LDV stock in a calendar year y is obtained using Eq. (3):

Ny ¼

T X U X

Nt;y;u

(3)

t¼0 u¼0

where T is the total number of vehicle types and U is the total number of vintage years considered. TTW energy consumption of vehicles type t of a vintage u in a calendar year y is given by Eq. (4):

ETTW;t;y;u ¼ Nt;y;u Mt;y;u Rt;y;u

(4)

where M is the annual traveled distance and R is the fuel consumption. TTW energy consumption for the fuel z is estimated using Eq. (5):

ETTW;y;z ¼

Tz X U X

ETTW;t;y;u

(5)

t¼0 u¼0

where Tz is the number of vehicle types that consume the fuel z. Passenger LDV fleet TTW energy consumption is estimated adding the energy consumption for all the fuel types Z using Eq. (6):


lez Palencia et al. / Energy 93 (2015) 1489e1504 J.C. Gonza

Fig. 1. Energy flows in the system.

1491


lez Palencia et al. / Energy 93 (2015) 1489e1504 J.C. Gonza

1492

Fig. 2. Flowchart of the model.

ETTW;y ¼

Z X

ETTW;y;z

(6)

z¼0

For each fuel, TTW and WTW CO2 emissions are estimated as the product of the energy consumption and the TTW and WTW CO2 emission factors. Total cost of ownership for the fleet is estimated using Eq. (7):

TCOfleet;y ¼

T X

y X

St;u ccap;t;u CRF þ

t¼0 u¼yY0

þ

Z X

T X U X

compact vehicles and 31.8% mini-sized vehicles [20]. Stock distribution for normal and compact vehicles is shown in Fig. 3; which was also used for mini-sized vehicles due to lack of data. There are two features that make Japan an interesting case of study: 1) significant penetration of mini-sized vehicles and 2) increasing acceptance of EDVs. For instance, normal HEV and compact HEV

Nt;y;u ACOM;t;y;u

t¼0 u¼0

ETTW;y;z cenergy;y;z

z¼0

(7) where TCOfleet is the total cost of ownership for the fleet, ccap is the initial capital cost of the vehicle, CRF is the capital recovery factor, ACOM is the annual operating and maintenance (O&M) cost of the vehicle, and cenergy is the energy price. 2.2. Characteristics of the Japanese passenger light-duty vehicle fleet The Japanese passenger LDV stock in 2012 was made of 54.5 million vehicles, distributed as 29.1% normal vehicles, 39.1%

Fig. 3. Distribution of normal and compact vehicle stock, using data from Ref. [27].


lez Palencia et al. / Energy 93 (2015) 1489e1504 J.C. Gonza Table 1 Passenger light-duty vehicle stock in the base year. Category

2012 stock [million vehicles]

Table 2 Passenger light-duty vehicle sales in the base year.

Powertrain share [%] ICEVc

Normal Compact Mini-sized a b c d e

a,b

17.0 22.9a,b 18.6a,b

1493

b,d

88.3 97.9b,d 99.9b,d

HEVe b,d

11.6 2.1b,d 0b,d

Category

BEV b,d

0.1 0b,d 0.1b,d

2012 sales [million vehicles]

b,d

0 0b,d 0b,d

New vehicle sales for 2012 are excluded. Estimated using values from AIRIA [20]. Corresponding only to gasoline-fueled ICEVs. Estimated using values from NeV [21]. Includes HEVs and PHEVs.

Normal Compact Mini-sized a b c d e f

stock in 2012 reached 1.95 and 0.88 million vehicles [21], BEV sales in 2012 reached 14,023 vehicles [22], and FCV sales will start in 2015 [23]. The three largest shares of the global electric vehicle1 stock in 2012 corresponded to the United States, Japan and France with 38, 24 and 11% [24]. Additionally, Japan targets that HEVs, BEVs/PHEVs and FCVs will account for 40, 30 and 3% of the new vehicle sales by 2030 [25]. Since diesel-fueled ICEVs accounted for 0.74 million vehicles in 2012, compared to the 55.5 million gasoline-fueled ICEVs [26], and the objective of this research is to assess the impact of powertrain electrification on energy consumption and CO2 emissions reductions, only gasoline-fueled ICEVs were considered. Distribution of passenger LDV stock in Japan is shown in Table 1, passenger LDV sales are presented in Table 2, and survival profile for passenger LDVs is shown in Fig. 4. Vehicle service lives were considered 13 years for normal and compact vehicles and 15 years for mini-sized vehicles [18]. Annual traveled distance for normal and compact vehicles is 9120 km/year and 7475 km/year for mini-sized vehicles [28]; for a total driven distance of 118,560 km for normal and compact vehicles and 112,125 km for mini-sized vehicles. Vehicle service life increased from 7 years in 1975 to 9 years during the 1990s and started growing rapidly after the vehicle inspection reform in 1996, in which the inspection intervals for passenger LDVs of age ten years or older were increased from every year to every two years; which might have incentivized vehicle owners to stay with their vehicles for longer time [18]. Despite the increase in vehicle service life, values in Japan are reasonable compared to other developed countries; such as 16.9 years in the United States [5] and 15 years in Europe [8]. Values in the United Kingdom are significantly smaller, with an average vehicle service life of 7.9 years [29]. It should be noted that vehicle service life influences significantly LDV fleet stock turnover rate and cost. Vehicle capital cost is annualized over the vehicle service life using a 5% discount rate, corresponding to the social discount rate used in Ref. [30].

Powertrain share [%] ICEVd

FCHEV a

1.41 1.60b 1.56c

a,e

68.3 72.9b,e 99.9c,e

HEVf a,e

30.9 27.1b,e 0c,e

BEV a,e

0.8 0b,e 0.1c,e

FCHEV 0a,e 0b,e 0c,e

Estimated using values from JADA [31]. Estimated using values from JADA [32]. Estimated using values from JADA [33]. Corresponding only to gasoline-fueled ICEVs. Estimated using values from NeV [22]. Includes HEVs and PHEVs.

the average road load power for the trip is obtained using Eq. (9) [34].

PRoad ¼

1 rC Av3 þ CRR mTot gv þ km mTot av þ mTot gGv 2 D

(8)

P Road ¼

1 rC Av3 þ CRR mTot gvavg 2 D rmc

(9)

where PRoad is the road power, r is the air density, CD is the drag coefficient, A is the vehicle frontal area, v is the vehicle velocity, CRR is the rolling resistance coefficient, mTot is the total vehicle mass, g is the gravitational acceleration, km is a factor to account for the

2.3. Passenger light-duty vehicle sizing The 24 vehicle types considered were modeled combining PAMVEC (Parametric Model of Vehicle Energy Consumption), a lumped-parameter model of vehicle energy consumption [34]; and a vehicle cost model, developed following the methods used in Ref. [35], to estimate the vehicle capital cost. A brief description of PAMVEC based on the documentation [34] is provided below. PAMVEC assumes that kinetic and potential energy terms in the road load equation, given by Eq. (8), integrate to zero when the vehicle returns to the starting point at the end of a trip. Therefore,

1 The Electric Vehicle Initiative definition of electric vehicles includes PHEVs, BEVs and FCVs [24].

Fig. 4. Survival profile for passenger light-duty vehicles in Japan, using data from Ref. [18]. (a) Normal and compact vehicles, (b) Mini-sized vehicles.


lez Palencia et al. / Energy 93 (2015) 1489e1504 J.C. Gonza

1494

Table 3 Main specifications and performance targets for each vehicle size class.

Specifications Drag coefficient [e] Frontal area [m2] Accessory load [W] Performance targets Time 0e100 km/h [s] 5.5% grade speed [km/h] Top speed [km/h] Range [km]

Table 4 Main assumptions used in passenger light-duty vehicle modeling.

Normal

Compact

Mini

0.33a 2.15b,c 700d

0.29i 2.07c,j 700d

0.32l 1.94c,m 300d

7.7e 88f 187e 600b,g,h

10.5k 88f 170k 500g,h,j

20.0l 88f 130l 400g,h,m

a

Reported value for Nissan Maxima by CarsDirect [37]. Based on catalogue data for Nissan Teana from Nissan [38]. Frontal area was estimated using a profile factor of 0.8 as in NREL's FASTSim [39]. d Fixed load corresponding to essential accessories such as the power steering, brake system and water pump. Values assumed based on data from NREL's FASTSim [39]. e Reported value for Nissan Altima in Car and Driver [40]. f Gradeability used for mid-size car by Pesaran et al. [41]. g Range estimated using catalogue data for gasoline tank capacity and vehicle fuel economy, using a correction factor of 0.785 [36] and adjusted to guarantee differences lower than 20% between catalogue data and modeling results for curb mass and fuel economy. h Range for BEVs was assumed to be 322 km (200 mi) as in Bandivadekar et al. [13]. i Reported value for Toyota Yaris in CarsDirect [42]. j Based on catalogue data for Toyota Vitz from Toyota [43]. k Reported value for Toyota Yaris in Car and Driver [44]. l Authors' assumption. m Based on catalogue data for Suzuki Wagon R from Suzuki [45]. b

Component

Capital cost [USD/kW]

Specific power [W/kg]

2012 2050 2012 a

ICE (ICEV) 31 ICE (HEV) 31a Fuel cell stack 285b Motor/generator (HEV) 23c Motor/generator (BEV, FCHEV) 23c Controller/inverter (HEV) 19a Controller/inverter (BEV, FCHEV) 19a Transmission (ICEV, HEV) 19a Transmission (BEV, FCHEV) 11d

a

26 26a 50e 6a 6a 6a 6a 11a 7d

f

642 642f 375f 1400f 1027f 1400f 1027f 1300f 1625f

Average efficiency [%]

2050

2012

g

j

770 770g 650h 1400i 1027i 1400i 1027i 1300i 1625i

26.0 30.5f 55.6f 70.0f 86.0f 70.0f 86.0f 89.0f 89.0f

2050 27.4k 32.1k 60.0l 70.0i 95.0m 70.0i 95.0m 94.0m 94.0m

c

rotational inertia of the powertrain, a is the vehicle acceleration, G is the road gradient, vrmc is the root-mean-cubed velocity during a trip and vavg is the average velocity during a trip. Component size estimations from PAMVEC were used along with the component specific costs to estimate the capital costs for all the components; which were added to obtain the vehicle capital cost for the vehicle using the Eq. (10).

cCap ¼

I X

Ki ci

(10)

i¼0

where Ki is the size of the ith component, ci is the specific cost for the ith component, and I is the total number of components. Main specifications and performance targets used for vehicle modeling are presented in Table 3. Vehicles were modeled using the JC08 driving cycle. A correction factor between JC08 mode driving cycle and real driving fuel consumption of 1.274 was used, based on the correction factor for fuel economy of 0.785 reported in Ref. [36]. Main assumptions for LDV modeling are presented in Table 4 and data for batteries are shown in Table 5. 2012 conventional glider mass was estimated to be 1164, 917 and 712 kg for normal, compact and mini-sized vehicles using catalogue data for curb mass and engine power for normal vehicles [38], compact vehicles [43], and mini-sized vehicles [45]; assuming a specific power for the ICE and the transmission of 642 and 1300 W/kg [34]. Conventional glider in 2012 has an iron and steel content of 74.8%, corresponding to the conventional glider from Ref. [46]. The same material breakdown was used for vintages previous to 2012. Compared to 2012, a conservative 5% reduction in the conventional glider mass was assumed in 2050, considering the tendency for the increase of lightweight materials use, as well as the increment of vehicle mass due to improvement of attributes such as comfort and safety [14]. Conventional glider mass reduction is achieved replacing iron and steel with HSS (high-strength steel); which can provide a 17% mass reduction compared to conventional

a Value reported in Contestabile et al. [35]. 2050 values correspond to 2030 optimistic values, assuming that specific capital cost is asymptotic with the number of produced units and will not decrease after 2030, similar to the behavior in the US EPA's MARKAL Database [48]. b Value reported by the US DOE [49]. c Estimated with data from van Vliet et al. [50], using an exchange rate of USD 1.3261 per V for 2012 according to data from the ECB [51]. d Values for production volume of 20,000 units/year in 2012 and 200,000 units/ year in 2050 from Delucchi et al. [52]. e Minimum capital cost for fuel cells in order to compete with ICE reported by the IEA [53]. f Value reported in Simpson, A. G. [34]. g Specific power for ICE in 2050 was assumed 20% higher than the 2012 value, achieved only through engine design improvement without changing materials. h Value for 2030 from Kromer and Heywood [54]. i Assumed identical to the 2012 values. j Value assumed based on average ICE efficiency reported by Saxena [55] and adjusted to guarantee differences lower than 20% between catalogue data and modeling results for curb mass and fuel economy. k Estimated considering a 5.3% increment, corresponding to the ICE peak efficiency increment between 2010 and 2045 in the average case from Moawad et al. [56]. l Value used after 2030 in Moawad et al. [56]. m Value for 2030 from Bandivadekar et al. [13].

steel without cost increment [47]. 2050 lightweight glider has the same material breakdown from the lightweight glider in Ref. [46]; achieving a 45% mass reduction compared to the 2012 conventional glider, due to increased use of aluminum and carbon FRP (fiber reinforced polymer) to replace conventional steel. Values for 2050 conventional glider mass for normal, compact and mini-size vehicles are 1106, 871 and 676 kg; while values for 2050 lightweight glider mass for normal, compact and mini-size vehicles are 640, 504 and 391 kg. Capital cost for the conventional glider for compact vehicles is 16,290 USD [35]; which was used to estimate the capital cost of the

Table 5 Main characteristics of the batteries.

Li-ion battery (HEV/FCHEV) Li-ion battery (BEV) a

Capital cost [USD/kWh]

Specific power [W/kg]

Specific energy [Wh/kg]

2012

2050

2012

2050

2012

2050

1326a

600c

1200d

3000f

90d

110f

743b

200c

420e

840g

150e

300h

Estimated with data from van Vliet et al. [50], using an exchange rate of USD 1.3261 per V [51]. b Value reported by the IEA [24]. c Optimistic value for 2030 from Kromer and Heywood [57], assuming that specific capital cost is asymptotic with the number of produced units and will not decrease after 2030, similar to the behavior in the US EPA's MARKAL Database [48]. d Value for the Valence UEV-18XP battery from van Vliet et al. [50]. e Value used by Simpson, A. G. [34]. f Value for the A123 Systems 26,650 (Cell) battery from van Vliet et al. [50]. g Estimated assuming that power/energy ratio in 2050 equal to the value in 2012. h Value for 2030 from Kromer and Heywood [54].


lez Palencia et al. / Energy 93 (2015) 1489e1504 J.C. Gonza

1495

The base scenario represents the continuation of current tendencies. New vehicle sales growth rates of 0.05%/year between 2012 and 2020 and 1.00%/year after 2020 were used, based on new vehicle sales projections by the MOE (Ministry of Environment) [58] and passenger vehicle stock calculations from the IEA (International Energy Agency's) ETP (Energy Technology Perspectives) [59]. New passenger LDV sales are identical for all the scenarios considered. Evolution of new vehicle sales market share by vehicle size class in the base scenario was estimated using data from the MOE [58], obtaining 24.4% for normal vehicles, 31.9% for compact vehicles and 43.7% for mini-sized vehicles in 2050. Powertrain market shares in the base scenario were defined in terms of targets for new vehicle sales market shares for EDVs, based on new vehicle sales in the reference scenario from Yabe et al. [19]. BEV market shares for all vehicle size classes are 5, 6, 15 and 25% in 2020, 2030, 2040 and 2050, respectively; while HEV market shares for normal and compact cars are 35, 40, 33 and 23% in 2020, 2030, 2040 and 2050, respectively. HEVs do not take off in mini-sized vehicle size class, similar to the realistic scenario from Nishimura [18]. Three alternative scenarios without lightweighting were considered, HEV, BEV and FCHEV scenarios; where successful diffusion of conventional HEVs, conventional BEVs and conventional FCHEVs takes place. Three alternative scenarios with lightweight materials substitution and vehicle size class reduction were considered, mini light HEV, mini light BEV and mini light FCHEV scenarios; where successful diffusion of HEVs, BEVs and FCHEVs, together with a shift to lightweight and mini-sized vehicles is achieved. Technology diffusion for EDVs and lightweight vehicles was modeled using a logistic curve defined by Eqs. (11) and (12) [60]:

conventional glider for normal and mini-sized vehicles, considering that capital cost is proportional to the mass, resulting in 21,290 and 12,246 USD, respectively. These values were kept constant between 2012 and 2050. Incremental cost of lightweight materials substitution in the lightweight glider was estimated considering a value of 8.82 USD/kg-saved (4 USD/lb-saved) [47]; resulting in capital costs for the lightweight normal, compact and mini-sized vehicle gliders of 26,050, 20,036 and 15,111 USD by 2050, respectively. Capital costs for the gasoline tank and emission control electronics were assumed to be 125 and 300 USD [35]; while the BEV's battery charger was estimated to cost 637 USD [30]. Previous data was used to estimate the vehicle fuel consumption, component sizes and mass. The difference between 2012 vehicle fuel consumption and curb mass for ICEVs and catalogue data for normal [38], compact [43] and mini-sized ICEVs [45] are lower than 20%. Even though the difference is large due to the lack of data, values are representative of each vehicle size class. The main specifications for vehicles modeled are presented in Table 6. For matter of simplicity, specifications for vintages between 2012 and 2050 were linearly interpolated. 2.4. Scenario characterization Seven scenarios were considered: the base scenario and six alternative scenarios considering diffusion of advanced vehicles. A ‘silver bullet’ approach was used in the construction of the alternative scenarios, considering only one dominant vehicle type in 2050 new vehicle sales. Even though the rationale for new vehicle sales in the future is a combination of vehicle types, the ‘silver bullet’ approach allows estimating the maximum ‘technically realizable’ energy consumption and CO2 emissions reductions achievable with powertrain electrification, lightweight materials use and vehicle size class reduction, considering the dynamics of the LDV fleet stock turnover. All the scenarios are defined in terms of new vehicle sales, according to the characteristics presented in Table 7.

MSt;y ¼

1 1 þ ebðyy0 Þ

(11)

Table 6 Main specifications for vehicle types considered. 2012 conventional

Normal vehicle Fuel consumption [MJ/km] Capital cost [USD] Vehicle curb mass [kg] ICE power [kW] Motor/generator power [kW] Fuel cell stack power [kW] Li-ion battery capacity [kWh] H2 fuel storage [kg-H2] Compact vehicle Fuel consumption [MJ/km] Capital cost [USD] Vehicle curb mass [kg] ICE power [kW] Motor/generator power [kW] Fuel cell stack power [kW] Li-ion battery capacity [kWh] H2 fuel storage [kg-H2] Mini-sized vehicle Fuel consumption [MJ/km] Capital cost [USD] Vehicle curb mass [kg] ICE power [kW] Motor/generator power [kW] Fuel cell stack power [kW] Li-ion battery capacity [kWh] H2 fuel storage [kg-H2]

2050 conventional

2050 lightweight

ICEV

HEV

BEV

FCHEV

ICEV

HEV

BEV

FCHEV

ICEV

HEV

BEV

FCHEV

2.72 28,439 1521 136 e e e e

2.15 33,284 1489 80 38 e 4.0 e

0.91 65,915 1993 e 145 e 75.2 e

1.41 65,264 1723 e 131 89 4.8 7.0

2.19 26,000 1385 118 e e e e

1.68 25,878 1342 74 28 e 1.5 e

0.58 33,320 1475 e 104 e 47.7 e

0.90 28,530 1436 e 105 73 1.4 4.5

1.65 29,320 823 77 e e e e

1.27 29,830 828 72 13 e 0.7 e

0.45 35,435 907 e 69 e 37.3 e

0.71 32,324 906 e 71 71 1.1 3.6

2.18 20,471 1115 75 e e e e

1.70 22,233 1097 52 15 e 1.6 e

0.75 51,046 1536 e 83 e 61.7 e

1.10 42,355 1241 e 71 57 2.0 4.6

1.79 19,159 1026 66 e e e e

1.36 19,057 1007 47 11 e 0.6 e

0.49 26,077 1133 e 61 e 40.1 e

0.73 20,792 1068 e 59 47 0.7 3.1

1.40 22,255 619 48 e e e e

1.09 22,616 625 46 8 e 0.4 e

0.39 27,928 705 e 43 e 32.3 e

0.60 24,051 668 e 43 46 0.7 2.5

1.58 14,247 795 32 e e e e

1.22 14,832 790 25 5 e 0.5 e

0.55 36,909 1124 e 35 e 45.7 e

0.78 24,798 859 e 30 28 0.9 2.6

1.30 13,712 741 28 e e e e

0.98 13,713 738 22 4 e 0.2 e

0.35 19,251 836 e 26 e 29.3 e

0.52 14,375 765 e 25 22 0.4 1.7

1.01 16,375 446 23 e e e e

0.79 16,544 448 22 4 e 0.2 e

0.29 20,836 518 e 20 e 23.6 e

0.42 17,035 467 e 20 21 0.3 1.4


lez Palencia et al. / Energy 93 (2015) 1489e1504 J.C. Gonza

1496 Table 7 New vehicle sales targeted in the considered scenarios. Scenario

Base HEV BEV FCHEV Mini light HEV Mini light BEV Mini light FCHEV

Dy ¼

Powertrain ICEV

HEV

✓

✓ ✓

Lightweighting BEV

FCHEV

Lightweight material

Size reduction

✓ ✓ ✓

✓ ✓ ✓

✓ ✓ ✓ ✓ ✓

1 ln 81 b

(12)

where MSt,y is the market share of the vehicle type t in the calendar year y; b is a parameter related to the growth rate; y0 is the inflection point, y is the calendar year and Dy is the diffusion rate. In scenarios considering vehicle size class reduction, mini-sized vehicle diffusion was modeled considering that mini-sized vehicle market share is identical to the base scenario until 2019, and follows Eq. (13) from 2020. Eq. (13) considers the mini-sized vehicle market share in 2019 MSt0 as the baseline level to ensure that minisized vehicle shift occurs without abrupt changes from the historical values.

MSt;y ¼ MSt0 þ

ð1  MSt0 Þ 1 þ ebðyy0 Þ

(13)

Technology diffusion curves in the alternative scenarios were built following three considerations. 1) There is only one dominant vehicle type in each alternative scenario, which reaches 99% of the new vehicle sales market share during the time horizon. 2) Technology diffusion follows the behavior previously observed for other technologies in the transport sector, corresponding to the logistic curves defined in the Eqs. (11) and (12). 3) There are no abrupt changes from the historical values, corresponding to the adjustment done for the logistic curves in Eq. (13) for mini-sized vehicle diffusion. Since there is only one vehicle type that reaches 99% market share in each alternative scenario, values estimated correspond to the maximum ‘technically realizable’ energy consumption and CO2 emissions reductions achievable with the targeted vehicle type. It should be noted that the logistic curves are asymptotic with 100%. It was assumed that advanced vehicle diffusion is symmetrical and has a diffusion rate of 15 years. This value corresponds to the time required for the new vehicle sales market share to grow from 10 to 90%. It also denotes the time required for the new vehicle sales market share to grow from 1 to 50%; and since the advanced vehicle diffusion was assumed symmetrical, it also denotes the time required for the new vehicle sales market share to grow from 50 to 99%. Technology diffusion spans 2Dy, 30 years in this case. The assumption used here for the diffusion rate is similar to the value of 12 years reported by Grübler [60] for the replacement of horses and carriages by cars at the beginning of the 1900s; and the diffusion of catalytic converters in the United States LDV fleet in the 1970s and 1980s. Similar values are reported by Hollinshead et al. [61] for the diffusion of motive power in transportation. It should be noted that technology diffusion curves used here are appropriate only under the ‘silver bullet’ approach used. Technology diffusion curves used are presented in Fig. 5. Alternative scenarios were built using the technology diffusion curves as shown in Table 8. Diffusion of vehicles using electric

Fig. 5. Technology diffusion curves.

powertrains, lightweight materials and mini-sized gliders is considered exogenously; and does not depend on the total cost of ownership. This approach ensures the successful diffusion of the targeted vehicle type in each alternative scenario and allows estimating the maximum ‘technically realizable’ energy consumption and CO2 emissions reductions. 2.5. Main assumptions and limitations Passenger LDV fleet is composed of cars and light trucks (i.e. pickups, SUVs and vans) with a large variety of vehicle types. In this research, the passenger LDV fleet was represented by normal, compact and mini-sized cars, excluding light trucks. It is suggested in the future to include light trucks to improve the representation of options available in the passenger LDV fleet. It was assumed that service life and annual traveled distance is only determined by the vehicle size class; in that sense, in scenarios considering vehicle size class reduction, the way of using passenger LDVs is also changed. O&M costs for ICEVs and HEVs were assumed equal to 0.056 USD/km (0.042 V/km) and 0.057 USD/km (0.043 V/km) for FCHEVs [50]; while O&M cost for BEVs was assumed equal to 0.057 USD/km (0.043 V/km) [30]. These values were assumed identical across vehicle size classes and vintages. Furthermore, fuel cell stacks and battery packs were considered to last for the whole service life of FCHEVs and BEVs. Since fuel cycle is not within the scope of the current research, CO2 emission factors and fuel prices were inputted exogenously to the model. TTW and WTW CO2 emission factors for gasoline were assumed equal to 71 and 92 g-CO2/MJ in 2012 [13] and kept constant until 2050. Gasoline price was considered to evolve from 48.9 USD/GJ in 2012 [26] to 56.9 USD/GJ in 2050, considering the ratio between gasoline price and crude oil price is constant and using crude oil price from projections for the 4DS2 scenario from the IEA's ETP [59]. Electricity WTW CO2 emission factor in 2012 was assumed equal to 138 g-CO2/MJ [62]; and considered to reach 55 g-CO2/MJ in 2050, based on the Nuclear Phase Out scenario from Berraho [63], corresponding to an electricity mix made of 50% renewable energy and 50% fossil fuels. Electricity price was 74.2 USD/GJ [26] in 2012; and reaches 80.9 USD/GJ in 2050,

2 The IEA's ETP considers the 2DS, 4DS and 6DS scenarios that limit the average global temperature increase to 2, 4 and 6  C in the long-term [59].


lez Palencia et al. / Energy 93 (2015) 1489e1504 J.C. Gonza

1497

Table 8 Technology diffusion curves used to build the alternative scenarios. Vehicle type

Alternative scenarios HEV

BEV

FCHEV

Mini light HEV

Mini light BEV

Mini light FCHEV

Normal HEV Compact HEV Mini-sized HEV Normal BEV Compact BEV Mini-sized BEV Normal FCHEV Compact FCHEV Mini sized FCHEV

HEV dif HEV dif Not started e e e e e e

e e e Normal BEV dif Not started Mini BEV dif e e e

e e e e e e About to start Not started Not started

HEV dif HEV dif Not started e e e e e e

e e e Normal BEV dif Not started Mini BEV dif e e e

e e e e e e About to start Not started Not started

Lightweight vehicle

e

e

e

Not started

Not started

Not started

Mini-sized vehicle

e

e

e

Mini dif

Mini dif

Mini dif

considering that household electricity price is composed by the electricity generation cost [63] and other costs [64]. In 2012, all the hydrogen is produced using decentralized SMR (steam methane gas reforming); with a WTW CO2 emission factor of 132 g-CO2/MJ [13]. By 2050, WTW CO2 emission factor for hydrogen decreases to 66 g-CO2/MJ, corresponding to 50% SMR and 25% electrolysis using wind power and 25% electrolysis using PV solar power. Hydrogen price was estimated to vary from 58.4 USD/GJ in 2012 to 43.0 USD/GJ in 2050, using delivered hydrogen costs from Delucchi et al. [65]. 3. Results and discussion 3.1. Advanced vehicle diffusion Evolution of passenger LDV stock in Japan for the seven scenarios considered is presented in Fig. 6, along with the passenger LDV stock estimations from the IEA's ETP [59]. Passenger LDV stock in Japan peaks around 63 million vehicles in 2027 for all scenarios; and decreases in the long term, reaching 52.4 and 54.6 million vehicles in 2050 for scenarios with and without size class reduction, respectively. Vehicle stock is larger in scenarios considering vehicle size reduction, since mini-sized vehicles have longer vehicle service lives than normal and compact vehicles, and new vehicle sales are identical for all the scenarios.

In the base scenario, a moderate participation in the stock share is achieved by HEVs and BEVs by 2050; with HEVs accounting for 28.1% of the normal and compact vehicles, with no mini-sized HEVs; while BEVs represent 19.7% of the normal and compact vehicles, and 18.8% of the mini-sized vehicles. The majority of the LDVs in the base scenario in 2050 are ICEVs, 52.2% of normal and compact vehicles, and 81.2% of mini-sized vehicles. HEVs achieve faster penetration in the HEV scenario than targeted technologies in other scenarios, since normal and compact HEVs already represent a significant share of new vehicle sales. By 2050, HEV stock share in the HEV scenario reaches 99.9% for normal and compact vehicles, and 79.7% for mini-sized vehicles. In the BEV scenario, the 2050 stock share of BEVs reaches 93.9% for normal and compact vehicles, and 91.9% of the mini-sized vehicles. In the FCHEV scenario, the 2050 FCHEV stock share reaches 93.9% for normal vehicles, 83.5% for compact vehicles and 79.7% for minisized vehicles. By 2050 in the mini light HEV scenario, lightweight mini-sized HEVs and conventional mini-sized HEVs are the dominant vehicle types, representing 66.4% and 9.4% of the passenger LDV fleet stock. A similar tendency is followed by lightweight mini-sized BEVs and conventional mini-sized BEVs in the mini light BEV scenario, where they represent 72.8% and 12.8% of the passenger LDV stock by 2050. In the mini light FCHEV scenario diffusion is slower, with 2050 lightweight mini-sized FCHEVs and conventional mini-sized FCHEVs stock shares reaching 66.4% and 9.4%. Passenger vehicle stock share is presented in Fig. 7 for the mini light BEV and mini light FCHEV scenarios. Compared to new vehicle sales, stock share of targeted technologies is smaller due to the time lag effect of vehicle service life. 3.2. Energy consumption reduction potential

Fig. 6. Passenger light-duty vehicle stock in Japan.

Passenger LDV fleet energy consumption is presented in Fig. 8. Base year energy consumption was estimated in 1554 PJ/year, 16.8% lower than the 2012 LDV fleet energy consumption in Japan [26]. One of the main reasons for such difference is that the degradation of fuel consumption with vehicle age was not considered due to lack of information. Compared to the 2012 baseline value, LDV fleet energy consumption in the base scenario is reduced 48.7% by 2050, due to vehicle stock decrement, fuel consumption improvement and deployment of HEVs and BEVs. The tendency is the same observed in the roadway energy consumption in the 2DS and 6DS scenarios form the IEA's ETP [59]. Nevertheless, base scenario energy consumption is smaller, since it is limited to passenger LDVs, which was not available from ETP data. Compared with results from Nishimura [18], all scenarios

1498


lez Palencia et al. / Energy 93 (2015) 1489e1504 J.C. Gonza

Fig. 7. Passenger vehicle stock share. (a) Normal vehicles e mini light BEV scenario, (b) Compact vehicles e mini light BEV scenario, (c) Mini-sized vehicles e mini light BEV scenario, (d) Normal vehicles e mini light FCHEV scenario, (e) Compact vehicles e mini light FCHEV scenario, (f) Mini-sized vehicles e mini light FCHEV scenario.

considered here have larger energy consumption in the mediumterm; while in the long term, energy consumption for the CeA3 scenario is similar to results for base and HEV scenarios; while values for the OeB4 scenario are similar to the values in the BEV and mini light HEV scenarios. Compared to the 2050 baseline value, HEV deployment can reduce 2050 passenger LDV fleet energy consumption only by 4.2%,

3 CeA scenario considers conservative improvements in fuel consumption. 2030 market shares for normal BEVs, normal PHEVs and mini-sized BEVs reach 20, 10% and 25%; which remain unchanged until 2050 [18]. 4 OeB scenario considers optimistic improvements in fuel consumption. 2030 market shares for normal BEVs, normal PHEVs and mini-sized BEVs reach 20, 10% and 25%; while 2050 market shares are 30, 20 and 50%, respectively [18].

due to the significant deployment of HEVs and BEVs in the base scenario. Diffusion of lightweight mini-sized HEVs and conventional FCHEVs produce similar results, with the mini light HEV and FCHEV scenarios achieving 33.9 and 38.3% reductions compared to the 2050 baseline value. In the mini light FCHEV and BEV scenarios, 2050 fleet energy consumption can be reduced 55.4 and 61.5% compared to the 2050 baseline value. The largest fleet energy consumption reduction is achieved in the mini light BEV scenario, 70.6% compared to the 2050 baseline scenario. Compared to the 2050 baseline value, gasoline consumption can be reduced up to 92.2% in the mini light BEV scenario. Nevertheless, gasoline remains an important energy carrier for the Japanese passenger LDV fleet, representing at least 24.8% of energy consumption in all scenarios by 2050. By 2050, electricity consumption


lez Palencia et al. / Energy 93 (2015) 1489e1504 J.C. Gonza

1499

Fig. 8. Tank-to-wheel fleet energy consumption.

Fig. 9. Tank-to wheel fleet CO2 emissions.

increases up to 240 PJ/year, corresponding to 78.4% the energy consumption in the BEV scenario. In contrast, electricity consumption in the mini light BEV scenario reaches a maximum of 176 PJ/year in 2050, showing the benefit of lightweight materials use and vehicle size class reduction. Similarly, hydrogen consumption increases up to 356 PJ/year, corresponding to 72.4% of the 2050 energy consumption in the FCHEV scenario; while hydrogen consumption in the mini light FCHEV scenario reaches 226 PJ/year. Lightweight materials use and vehicle size class reduction can lower the requirements for new infrastructure for power generation and hydrogen production, favoring the earlier stages of BEV and FCHEV diffusion.

value. TTW CO2 emissions reductions for FCHEV and mini light FCHEV scenarios in 2050 are very similar, 82.1 and 82.9%, compared to the baseline value. The lowest TTW CO2 emissions in 2050 are achieved using BEVs, with TTW CO2 emissions reductions reaching 91.1% in the BEV scenario and 92.2% in the mini light BEV scenario. WTW CO2 emissions are shown in Fig. 10. 2050 WTW CO2 emissions in the HEV and mini light HEV scenarios are reduced 1.8 and 32.3% compared to the 2050 baseline values. In contrast, 2050 WTW CO2 emissions reductions in the FCHEV and mini light FCHEV scenarios reach 49.8 and 62.5%. The largest WTW CO2 emissions reductions were obtained in the BEV and mini light BEV scenarios, 73.0 and 78.9% compared to the 2050 baseline values. Compared to TTW CO2 emissions, the effect of lightweighting on WTW CO2 emissions becomes more important for FCHEV and BEV deployment. Values estimated here using the ‘silver bullet’ approach correspond to the ‘technically realizable’ potential of powertrain electrification, lightweight materials use and vehicle size class reduction to decrease energy consumption and CO2 emissions. It should be noted that vehicle diffusion was considered independent of the total cost of ownership. Not all of this ‘technically realizable’ potential will be achieved because there are other constraints that limit diffusion of vehicles using electric powertrains, lightweight materials and mini-sized gliders, such as vehicle affordability and

3.3. CO2 emissions reduction potential Even though the focus of this research is on passenger LDV use, CO2 emissions were estimated for each scenario on a TTW and WTW basis. TTW CO2 emissions are presented in Fig. 9. 2012 TTW CO2 emissions were estimated in 110 Mt-CO2/year; 2.9% lower than the 2012 value [66]. 2050 TTW CO2 emissions in all scenarios considered here are lower than in the CeA scenario from Nishimura [18]; while TTW CO2 emissions in the OeB scenario from the same author have similar values to the base and HEV scenarios considered here. In contrast, TTW CO2 emissions in the case 0 from Yabe et al. [19] are lower than values estimated here for all the scenarios in the medium-term and higher in the long-term. Compared to the base year value, TTW CO2 emissions are reduced 51.9% by 2050 in the base scenario; corresponding to a reduction of 37.4% compared to 1990 CO2 emissions level. In the ‘Next-Generation Vehicle Plan 2010’, Japan set the target of 25% CO2 emissions reductions compared to the 1990 level by 2020 [25]. This target is not accomplished in any of the scenarios considered. The long-term CO2 emissions reduction target in Japan of 80% CO2 emissions reduction compared to the 1990 level [67] is attained in scenarios with successful deployment of BEVs and FCHEVs. In that sense, the long term CO2 emissions reduction target can be achieved with the deployment of BEVs and FCHEVs; while the medium-term CO2 emissions reduction target cannot be accomplished and other measures such as modal shift, demand management and vehicle occupancy increase are necessary. Compared to the 2050 baseline value, TTW CO2 emissions in the HEV scenario are 2.0% higher, due to the significant deployment of BEVs in the base scenario. In the mini light HEV scenario, 2050 TTW CO2 emissions can be reduced 29.7% compared to the 2050 baseline

Fig. 10. Well-to-tank fleet CO2 emissions.

1500


lez Palencia et al. / Energy 93 (2015) 1489e1504 J.C. Gonza

public acceptance. More realistic estimates of energy consumption and CO2 emissions reductions can be obtained by endogenizing the calculation of the new vehicle sales market shares considering these constraints. This is suggested as a direction for future research. 3.4. Cost of powertrain electrification, lightweight materials substitution and size reduction The annual cash flows for the six alternative scenarios compared to the base scenario are presented in Fig. 11. HEV scenario has large energy savings, with very small increments in capital and O&M costs until 2023 and reductions thereafter. The cash flow is negative over the whole time horizon, with energy savings having the major contribution until 2037 and capital and O&M reductions from thereafter. In the BEV and FCHEV scenarios, switch from ICEs to

electric powertrains increases capital and O&M costs and reduces energy cost. The net cash flow is positive for the BEV scenario during the whole time horizon; while in the FCHEV scenario the net cash flow becomes negative after 2044. The net cash flow for the FCHEV scenario peaks in 2037 at 7.35 billion USD/year, and reaches 12.6 billion USD/year by 2050; while net cash flow for the BEV scenario peaks in 2041 at 32.7 billion USD/year, and reaches 16.3 billion USD/year by 2050. BEV and FCHEV diffusion can be favored by vehicle size class reduction and lightweight materials use, as the net cash flow can be significantly lowered; reaching zero around 2045 for the mini light BEV scenario and around 2033 for the mini light FCHEV scenario. By 2050, the lowest net cash flow was found for the mini light FCHEV scenario, reaching 40.0 billion USD/year. In that sense, lightweight mini-sized FCHEVs represent the best alternative for the LDV fleet from the economic perspective in the long-term. It is

Fig. 11. Annual cash flow. (a) HEV scenario, (b) BEV scenario, (c) FCHEV scenario, (d) Mini light HEV scenario, (e) Mini light BEV scenario, and (f) Mini light FCHEV scenario.


lez Palencia et al. / Energy 93 (2015) 1489e1504 J.C. Gonza

e e e e e e e ;20%c

c

Parameters used in the alternative scenarios are presented in Section 2. 20% increment compared to the values used in the alternative scenarios. 20% reduction compared to the values used in the alternative scenarios. a

b

Low High

e e e e e e e :20%b e e e e e e ;20%c e

Low High

e e e e e e :20%b e e e e e e ;20%c e e

Low High

e e e e e :20%b e e e e e e ;20%c e e e

Short Long

e e e e :20%b e e e e e e ;20%c e e e e

Short Long

e e e :20%b e e e e e e ;20%c e e e e e

Low High

e e :20%b e e e e e e ;20%c e e e e e e ;20%c e e e e e e e :20%b e e e e e e e e e e e e e e e 2050 fuel consumption Tech. diffusion span Sales growth rate Vehicle service life Traveled distance Fuel price Discount rate 2050 capital cost

e :20%b e e e e e e

Long Low High

New vehicle sales Diffusion span Fuel consumption Alternative scenariosa Parameter

Table 9 Variation of the considered parameters in the sensitivity analysis compared to the alternative scenarios.

In order to assess the robustness of the model, the sensitivity of the results to variations in vehicle fuel consumption, technology diffusion span, new vehicle sales growth rate, vehicle service life, annual traveled distance, fuel price, discount rate and vehicle capital cost was evaluated. The sensitivity analysis was performed only for the mini light FCHEV scenario, which has the lowest net cash flow by 2050. Compared to the values used in the alternative scenarios, a ±20% variation was considered for these parameters. Vehicle fuel consumption and capital cost variations encompass the uncertainties in the assumptions used for vehicle modeling. The ±20% variation was applied to 2050 values and the other values were linearly interpolated. Changes in the technology diffusion span affect the stock of advanced vehicles available in the LDV fleet; while changes in the new vehicle sales growth rate affect all the LDV fleet stock. Uncertainty regarding vehicle use was considered via changes in the annual traveled distance and the vehicle survival profile. Additionally, the effect of changes in the fuel price and the discount rate were considered. The variation of the considered parameters in the sensitivity analysis compared to the alternative scenarios is presented in Table 9. Sensitivity of TTW CO2 emissions to variations in the considered parameters is presented in Fig. 12. Sensitivity of TTW energy consumption and TTW CO2 emissions to variations in the considered parameters exhibit a similar behavior. Vehicle service life, annual traveled distance and technology diffusion span have the largest impact on TTW energy consumption and CO2 emissions. Vehicle service life causes the largest impact on CO2 emissions because it influences the diffusion rate for advanced vehicles by determining the total stock of LDVs in the fleet. Compared to TTW energy consumption, the impact of technology diffusion span variations on TTW CO2 emissions is larger, since differences in CO2 emissions between EDVs and ICEVs are larger than differences in fuel consumption. Since vehicle diffusion follows a ‘silver bullet’ approach and was set up exogenously, variations in the discount rate, fuel price and vehicle capital cost do not have any effect on the LDV fleet composition, energy consumption or CO2 emissions. This is a limitation of the model, since the diffusion of vehicles using electric powertrains and lightweight materials was considered independent of the vehicle total cost of ownership. Sensitivity of net cash flow to variations in the considered parameters is presented in Fig. 13. Vehicle service life, vehicle capital cost and annual traveled distance have the largest impact on the net cash flow; while changes in vehicle fuel consumption cause the smallest impact. Changes in vehicle service life affect the

Service life

3.5. Sensitivity analysis

Short

Annual traveled distance

Fuel price

Discount rate

Capital cost

important to note that the TTW and WTW CO2 emissions reductions potentials in the very long-term of FCHEVs and BEVs will be the same, once they reach the same level of diffusion in the LDV stock. In order to evaluate the impact of powertrain electrification, lightweight materials use and vehicle size class reduction on LDV fleet energy consumption and CO2 emissions, it was necessary to develop scenarios where only the targeted vehicle type dominates the market, independent of the total cost of ownership. The total cost of ownership was calculated to assess the associated cost of the energy consumption and CO2 emissions reductions of that vehicle type. Compared to the ‘silver bullet’ approach used, diversification of the new vehicle sales is likely in the future. This implicates that the saturation value for the new vehicle sales market share of the advanced vehicles will be lower than in the alternative scenarios. Using the total cost of ownership to model the competition between the different vehicle types available in the automotive market is suggested in the future.

1501

1502


lez Palencia et al. / Energy 93 (2015) 1489e1504 J.C. Gonza

LDV fleet was developed and used to study the simultaneous effect of powertrain electrification, lightweight materials substitution and vehicle size class reduction on energy consumption, CO2 emissions and cost for the Japanese passenger LDV fleet. Main conclusions are presented below:

Fig. 12. Sensitivity of TTW CO2 emissions to variations in vehicle fuel consumption, technology diffusion span, new vehicle sales growth rate, vehicle service life, annual traveled distance, fuel price, discount rate and vehicle capital cost for the mini light FCHEV scenario.

LDV fleet energy cost and O&M cost, that depend on the vehicle stock; as well as the LDV fleet capital cost, since vehicles are depreciated over their service lives. 4. Conclusions The use of alternative powertrains, lightweight materials substitution and vehicle size class reduction can lower LDV fleet energy consumption and CO2 emissions. In this research, a dynamic bottom-up accounting energy-economic model of the passenger

 In the base scenario, TTW CO2 emissions are reduced by 51.9% compared to the 2012 baseline value by 2050. However, the reduction is not enough to achieve the target of 50% CO2 emissions reduction by 2050 compared to the 1990 level. This target is achievable only with the diffusion of BEVs and FCHEVs. In contrast, the medium-term target of 25% CO2 emissions reductions compared to the 1990 level by 2020 is not achievable in any of the scenarios. In that sense, in the short- and mediumterm other measures such as modal shift, demand management and vehicle occupancy increase are necessary. The combined assessment of these measures is recommended for future research.  BEV diffusion achieves the largest potential for energy consumption and CO2 emissions reduction. Compared to the 2050 baseline values, reductions of 61.5 and 91.1% for TTW energy and CO2 emissions are achieved in the BEV scenario; while the corresponding values in the mini light BEV scenario are 70.6 and 92.2%. However, the net cash flow for the passenger LDV fleet is significantly increased due to the incremental capital cost for BEVs. In contrast, the mini light FCHEV scenario has a lower net cash flow, representing the best alternative for the Japanese passenger LDV fleet from the economic perspective.  Lightweight materials substitution and vehicle size reduction causes a significant reduction of the net cash flow, due to the decrease of the capital cost, caused by vehicle size class reduction, and the decrease of the energy cost, caused by fuel consumption improvement. In that sense, BEV and FCHEV diffusion can be favored by vehicle size reduction and lightweight materials substitution.  By 2050, electricity consumption increases up to 240 and 176 PJ/ year in the BEV and mini light BEV scenarios; while hydrogen consumption increases up to 356 and 226 PJ/year in the FCHEV and mini light FCHEV scenarios. Lightweight materials use and vehicle size class reduction can lower the requirements for new infrastructure for power generation and hydrogen production, favoring the earlier stages of BEV and FCHEV diffusion.

References

Fig. 13. Sensitivity of net cash flow to variations in vehicle fuel consumption, technology diffusion span, new vehicle sales growth rate, vehicle service life, annual traveled distance, fuel price, discount rate and capital cost for the mini light FCHEV scenario.

[1] IPCC. Summary for policymakers. In: Edenhofer O, Pichs-Madruga R, Sokona Y, Farahani E, Kadner S, Seyboth K, et al., editors. Clim. Chang. 2014 Mitig. Clim. Chang. Contrib. Work. Gr. III to Fifth Assess. Rep. Intergov. Panel Clim. Chang. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press; 2014. [2] Edenhofer O, Pichs-Madruga R, Sokona Y, Kadner S, Minx JC, Brunner S, et al. Technical summary. In: Edenhofer O, Pichs-Madruga R, Sokona Y, Farahani E, Kadner S, Seyboth K, et al., editors. Clim. Chang. 2014 Mitig. Clim. Chang. Contrib. Work. Gr. III to Fifth Assess. Rep. Intergov. Panel Clim. Chang. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press; 2014. [3] Dargay J, Gately D, Sommer M. Vehicle ownership and income growth, Worldwide : 1960-2030. Energy J 2007;28:143e70. [4] OECD/ITF. ITF transport outlook 2015. Paris: Organisation for Economic Cooperation and Development and International Transport Forum; 2015. http://dx.doi.org/10.1787/9789282107782-en. ~ ez X, D'Agosto M, Dimitriu D, et al. [5] Sims R, Schaeffer R, Creutzig F, Cruz-Nún Transport. In: Edenhofer O, Pichs-Madruga R, Sokona Y, Farahani E, Kadner S, Seyboth K, et al., editors. Clim. Chang. 2014 Mitig. Clim. Chang. Contrib. Work. Gr. III to Fifth Assess. Rep. Intergov. Panel Clim. Chang. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press; 2014. [6] Heywood JB. Assessing the fuel consumption and GHG of future in-use vehicles. In: PEA-AIT Int. Conf. Energy Sustain. Dev. Issues Strateg. (ESD 2010). Chiang Mai, Thailand: Institute of Electrical and Electronics Engineers (IEEE); 2010. p. 1e12.


lez Palencia et al. / Energy 93 (2015) 1489e1504 J.C. Gonza [7] Lee DH, Park SY, Hong JC, Choi SJ, Kim JW. Analysis of the energy and environmental effects of green car deployment by an integrating energy system model with a forecasting model. Appl Energy 2013;103:306e16. http:// dx.doi.org/10.1016/j.apenergy.2012.09.046. [8] Salah F, Ilg JP, Flath CM, Basse H, Dinther C Van. Impact of electric vehicles on distribution substations: a Swiss case study. Appl Energy 2015;137:88e96. http://dx.doi.org/10.1016/j.apenergy.2014.09.091. [9] Pagerit S, Sharer P, Rousseau A. Fuel economy sensitivity to vehicle mass for advanced vehicle powertrains. SAE World Congr. Exhib. SAE Pap. 2006-010665. Detroit, MI: Society of Automotive Engineers; 2006. [10] Du JD, Han WJ, Peng YH, Gu CC. Potential for reducing GHG emissions and energy consumption from implementing the aluminum intensive vehicle fleet in China. Energy 2010;35:4671e8. http://dx.doi.org/10.1016/ j.energy.2010.09.037. [11] Hao H, Wang H, Ouyang M. Fuel conservation and GHG (Greenhouse gas) emissions mitigation scenarios for China's passenger vehicle fleet. Energy 2011;36:6520e8. http://dx.doi.org/10.1016/j.energy.2011.09.014.
lez Palencia JC, Furubayashi T, Nakata T. Techno-economic assessment [12] Gonza of lightweight and zero emission vehicles deployment in the passenger car fleet of developing countries. Appl Energy 2014;123:129e42. http:// dx.doi.org/10.1016/j.apenergy.2014.02.059. [13] Bandivadekar A, Bodek K, Cheah L, Evans C, Groode T, Heywood J, et al. On the road in 2035: reducing transportation's petroleum consumption and GHG emissions. Cambridge, Mass.: Massachusetts Institute of Technology; 2008. [14] Cheah L. Cars on a diet: the material and energy impacts of passenger vehicle weight reduction in the U.S. Doctor of Philosophy in Engineering Thesis, Engineering Systems Division. Massachusetts Institute of Technology, 2010. [15] Wang G. Advanced vehicles: costs, energy use, and macroeconomic impacts. J Power Sources 2011;196:530e40. http://dx.doi.org/10.1016/ j.jpowsour.2010.07.009. [16] Kloess M, Müller A. Simulating the impact of policy, energy prices and technological progress on the passenger car fleet in Austriada model based analysis 2010e2050. Energy Policy 2011;39:5045e62. http://dx.doi.org/ 10.1016/j.enpol.2011.06.008. [17] Pasaoglu G, Honselaar M, Thiel C. Potential vehicle fleet CO2 reductions and cost implications for various vehicle technology deployment scenarios. Energy Policy 2012;40:404e21. [18] Nishimura E. Assessing the fuel use and greenhouse gas emissions of future light-duty vehicles in Japan. Master of Science in Technology and Policy Thesis, Engineering System Division. Massachusetts Institute of Technology; 2011. [19] Yabe K, Shinoda Y, Seki T, Tanaka H, Akisawa A. Market penetration speed and effects on CO2 reduction of electric vehicles and plug-in hybrid electric vehicles in Japan. Energy Policy 2012;45:529e40. http://dx.doi.org/10.1016/ j.enpol.2012.02.068. [20] AIRIA. 2012 Vehicle stock (in Japanese). Automobile Inspection & Registration Association. [online] Available at: http://www.airia.or.jp/publish/file/ e49tph00000004t7-att/e49tph00000004te.pdf [last accessed in November 2014]. [21] NeV. Electric vehicle stock statistics (estimate) (in Japanese). Next Generation Vehicle Promotion Center. [online] Available at: http://www.cev-pc.or.jp/ tokei/hanbai.html [last accessed in November 2014]. [22] NeV. Electric vehicle sales statistics (in Japanese). Next Generation Vehicle Promotion Center. [online] Available at: http://www.cev-pc.or.jp/tokei/ hanbai3.html [last accessed in November 2014]. [23] Air Liquide, Toyota Tsusho for hydrogen refueling in Japan. Fuel Cells Bull 2013;2013:8e9. http://dx.doi.org/10.1016/S1464-2859(13)70383-3. [24] IEA/OECD, EVI. Global EV Outlook; Understanding the electric vehicle landscape to 2020. Paris: International Energy Agency/Organization for Economic Cooperation and Development and Electric Vehicles Initiative of the Clean Energy Ministerial; 2013. [25] METI. Next-Generation Vehicle Plan 2010 (Outline) Structural change in the auto market. vol. 2010. Ministry of Economy, Trade and Industry. [online] Available at: http://www.meti.go.jp/english/press/data/pdf/N-G-V2.pdf [last accessed on February 2015]. [26] EDMC, IEEJ. EDMC Handbook of energy & economics statistics 2014 (in Japanese). The Energy Data and Modelling Center, The Institute of Energy Economic, Japan; The Energy Conservation Center; 2014. [27] AIRIA. New vehicle registration by year; light-duty vehicle fleet (in Japanese). Automobile Inspection & Registration Association; 2012. [28] MLIT. Automobile transportation statistical yearbook, vol. 47, No 13 Fiscal Year 2009 (in Japanese). Ministry of Land, Infrastructure, Transport and Tourism. [online] Available at: http://www.mlit.go.jp/k-toukei/search/pdf/06/ 06200900a00000.pdf [last accessed on February 2014]. [29] DFT. Licensed cars by years since first registration. Great Britain, annually: 1994 to 2014; Vehicle statistics. Department for Transport. [online] Available at: https://www.gov.uk/government/statistical-data-sets/veh02-licensed-cars [last accessed in September 2015]. [30] Van Vliet O, Brouwer AS, Kuramochi T, van den Broek M, Faaij A. Energy use, cost and CO2 emissions of electric cars. J Power Sources 2011;196:2298e310. http://dx.doi.org/10.1016/j.jpowsour.2010.09.119. [31] JADA. Yearly new vehicle sales; normal automobiles (in Japanese). Japanese Automobile Dealers Association. [online] Available at: http://www.jada.or.jp/ contents/data/type/index02.html [last accessed in November 2014].

1503

[32] JADA. Yearly new vehicle sales; compact automobiles (in Japanese). Japanese Automobile Dealers Association. [online] Available at: http://www.jada.or.jp/ contents/data/type/index03.html [last accessed in November 2014]. [33] JADA. Yearly new vehicle sales; mini-sized automobiles (in Japanese). Japanese Automobile Dealers Association. [online] Available at: http://www.jada. or.jp/contents/data/index10.html [last accessed in November 2014]. [34] Simpson AG. Parametric modelling of energy consumption. In: Road vehicles. School of Information Technology and Electrical Engineering. The University of Queensland; 2005. Doctor of Philosophy in Engineering Thesis. [35] Contestabile M, Offer GJ, Slade R, Jaeger F, Thoennes M. Battery electric vehicles, hydrogen fuel cells and biofuels. Which will be the winner? Energy Environ Sci 2011;4:3754e72. http://dx.doi.org/10.1039/c1ee01804c. [36] Next-generation Vehicle Diffusion Strategy Review Panel. Next-generation vehicle diffusion strategy (in Japanese). Ministry of Environment. [online] Available at: http://www.env.go.jp/air/report/h21-01/ [last accessed in February 2015]. [37] CarsDirect. Nissan Maxima Specs, warranty & reliability. [online] Available at: http://www.carsdirect.com/2014/nissan/maxima/specs [last accessed in December 2013]. [38] Nissan. Nissan Teana (in Japanese). [online] Available at: http://www2.nissan. co.jp/TEANA/index.html [last accessed in December 2013]. [39] NREL, FASTSim, National Renewable Energy Laboratory. [online] Available at: http://www.nrel.gov/transportation/fastsim.html [last accessed in November 2014]. [40] Car and Driver. 2013 Nissan Altima 2.5 SL. [online] Available at: http://www. caranddriver.com/reviews/2013-nissan-altima-25-sl-test-review-impressingthe-joneses-page-2 [last accessed in February 2014]. [41] Pesaran A, Markel T, Zolot M, Sprik S, Tataria H, Duong T. Energy Storage Fuel Cell Vehicle Analysis. 21st Worlwide Batter. Hybrid Fuel Cell Electr. Veh. Symp. Exhib. - EVS21 Monaco, 2e5 April 2005. [42] CarsDirect. Toyota Yaris Specs & Safety. [online] Available at: http://www. carsdirect.com/2014/toyota/yaris/specs [last accessed in December 2013]. [43] Toyota. Toyota Vitz Main specifications (in Japanese). [online] Available at: http://toyota.jp/vitz/003_p_007/spec/spec/4wd/index.html [last accessed in December 2013]. [44] Car and Driver. 2012 Toyota Yaris; Yep, it’s a car. There's not much else to say. [online] Available at: http://www.caranddriver.com/reviews/2012-toyotayaris-first-drive-review [last accessed in December 2013]. [45] Suzuki. Wagon R Main specifications (in Japanese). [online] Available at: http://www.suzuki.co.jp/car/wagonr/spec/index.html [last accessed in December 2013]. [46] Burnham A, Wang M, Wu Y. Development and applications of GREET 2.7 d the transportation vehicle-cycle model. Argonne: Energy Systems Division, Argonne National Laboratory; 2006. [47] Das S. The cost of automotive polymer composites: a review and assessment of DOE's lightweight materials composites research. Oak Ridge, Tenesse: Oak Ridge National Laboratory; 2001. [48] Lenox C, Dodder R, Gage C, Kaplan O, Loughlin D, Yelverton W, EPA U.S. Nineregion MARKAL database documentation. Cincinnati, OH: US Environmental protection Agency; 2013. [49] DOE. DOE Hydrogen and Fuel Cells Program Record # 14012. Department of Energy United States of America. [online] Available at: http://energy.gov/ sites/prod/files/14012_fuel_cell_system_cost_2013.pdf [last accessed in November 2014]. [50] Van Vliet OPR, Kruithof T, Turkenburg WC, Faaij APC. Techno-economic comparison of series hybrid, plug-in hybrid, fuel cell and regular cars. J Power Sources 2010;195:6570e85. http://dx.doi.org/10.1016/ j.jpowsour.2010.04.077. [51] ECB. Euro exchange rates USD. European Central Bank [Online] Available at: http://www.ecb.int/stats/exchange/eurofxref/html/eurofxref-graph-usd.en. html# [last accessed in July 2014]. [52] Delucchi MA, Burke A, Lipman T, Miller M. Electric and gasoline vehicle lifecycle cost and energy-use model. Report for the California Air Resources Board. Davis, California: Institute of Transportation Studies, University of California Davis; 2000. [53] IEA/OECD. IEA energy technology essentials fuel cells. International Energy Agency and Organization for Economic Cooperation and Development; 2007. [54] Kromer M, Heywood JB. A Comparative Assessment of Electric Propulsion Systems in the 2030 US Light-Duty Vehicle Fleet. SAE Tech Pap Ser 2008; 2008-01-04. [55] Saxena S, Phadke A, Gopal A. Understanding the fuel savings potential from deploying hybrid cars in China. Appl Energy 2014;113:1127e33. http:// dx.doi.org/10.1016/j.apenergy.2013.08.057. [56] Moawad A, Sharer P, Rousseau A. Light-duty vehicle fuel consumption displacement potential up to 2045. Argonne, IL: Argonne National Laboratory; 2011. [57] Kromer M, Heywood J. Electric Powertrains : Opportunities and Challenges in the U.S. Light-Duty vehicle fleet. 2007. Cambridge, Massachusetts. [58] MOE. The strategy to promote Environmentally Friendly Vehicles (in Japanese). Ministry of Environment. [online] Available at: http://www.env.go.jp/ air/report/h22-02/index.html [last accessed in November 2014]. [59] IEA/OECD. Energy technology perspectives 2012; pathways to a clean energy system. Paris: International Energy Agency and Organization for Economic Cooperation and Development; 2012. http://dx.doi.org/10.1787/energy_tech2012-en.

1504


lez Palencia et al. / Energy 93 (2015) 1489e1504 J.C. Gonza

[60] Grübler A. Technology and global change. Cambridge, U.K: Cambridge University Press; 2003. [61] Hollinshead MJ, Eastman CD, Etsell TH. Forecasting performance and market penetration of fuel cells in transportation. Fuel Cells Bull 2005;2005: 10e7. [62] IEA/OECD. CO2 emissions from fuel combustion Highlights 2013. Paris: International Energy Agency and Organization for Economic Cooperation and Development; 2013. [63] Berraho D. Options for the Japanese electricity mix by 2050. Master of Science Thesis. School of Industrial Engineering and Management, Division of Energy and Climate Studies. KTH Royal Institute of Technology; 2012. [64] IEA/OECD. World energy outlook 2012. Paris: International Energy Agency and Organization for Economic Cooperation and Development; 2012.

[65] Delucchi MA, Yang C, Burke AF, Ogden JM, Kurani K, Kessler J, et al. An assessment of electric vehicles : technology, infrastructure requirements, greenhouse-gas emissions, petroleum use, material use, lifetime cost, consumer acceptance and policy. Phil Trans R Soc A 2014;372:20120325. [66] GIO, NIES. Japan’s GHG emissions data (FY1990-2012, Final figures). Greenhouse Gas Inventory Office of Japan, National Institute of Environmental Studies. [Online] Available at: http://www-gio.nies.go.jp/aboutghg/nir/nir-e. html [last accessed in March 2015]. [67] Oshiro K, Masui T. Diffusion of low emission vehicles and their impact on CO2 emission reduction in Japan. Energy Policy 2014. http://dx.doi.org/10.1016/ j.enpol.2014.09.010.