A Dynamic Fleet Model of U.S Light-Duty Vehicle Lightweighting and

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Energy and the Environment

A dynamic fleet model of U.S light-duty vehicle lightweighting and associated greenhouse gas emissions from 2016-2050 Alexandre Milovanoff, Hyung Chul Kim, Robert Dale De Kleine, Timothy J. Wallington, I. Daniel Posen, and Heather L MacLean Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b04249 • Publication Date (Web): 25 Jan 2019 Downloaded from http://pubs.acs.org on January 26, 2019

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Environmental Science & Technology

A dynamic fleet model of U.S light-duty vehicle lightweighting and associated greenhouse gas emissions from 2016-2050 Alexandre Milovanoffa*, Hyung Chul Kimb, Robert De Kleineb, Timothy J. Wallingtonb, I. Daniel Posena, Heather L. MacLeana,c.

a Department

of Civil & Mineral Engineering, University of Toronto, 35 St. George Street,

Toronto, Ontario, M5S 1A4 Canada b Materials

& Manufacturing R&A Department, Ford Motor Company, Dearborn, Michigan

48121-2053, United States c

Department of Chemical Engineering & Applied Chemistry, University of Toronto, 200

College Street, Toronto, Ontario, M5S 3E5 Canada

ACS Paragon Plus Environment

1


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ABSTRACT

1

Substituting conventional materials with lightweight materials is an effective way to

2

reduce the life cycle greenhouse gas (GHG) emissions from light-duty vehicles. However,

3

estimated GHG emission reductions of lightweighting depend on multiple factors including

4

the vehicle powertrain technology and efficiency, lightweight material employed, and end-

5

of-life material recovery. We developed a fleet-based life cycle model to estimate the

6

GHG emission changes due to lightweighting the U.S. light-duty fleet from 2016 to 2050,

7

using either high strength steel or aluminum as the lightweight material. Our model

8

estimates that implementation of an aggressive lightweighting scenario using aluminum

9

reduces 2016 through 2050 cumulative life cycle GHG emissions from the fleet by 2.9 Gt

10

CO2 eq. (5.6%), and annual emissions in 2050 by 11%. Lightweighting has the greatest

11

GHG emissions reduction potential when implemented in the near-term, with two times

12

more reduction per kilometer traveled if implemented in 2016 rather than in 2030.

13

Delaying implementation by 15 years sacrifices 72% (2.1 Gt CO2 eq.) of the cumulative

14

GHG mitigation potential through 2050. Lightweighting is an effective solution that could


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provide important near-term GHG emission reductions especially during the next 10-20

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years when the fleet is dominated by conventional powertrain vehicles.

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TABLE OF CONTENTS (TOC)/ABSTRACT ART

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BODY 1. Introduction

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Climate change resulting from anthropogenic greenhouse gas (GHG) emissions is a

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major threat to the social and political stabilities of modern societies.1 Fossil fuel

23

combustion by the U.S. light-duty vehicle fleet accounted for 1.1 gigatonnes (Gt) of CO2

24

eq. emissions (17% of U.S. GHG emissions) in 2016.2 One way to reduce these GHG

25

emissions is to reduce future light-duty vehicles’ fuel consumption by substituting

26

conventional materials (e.g., mild steel, cast iron) with lightweight materials (e.g., high-

27

strength steel, aluminum).3 Lightweighting a vehicle (i.e., reducing vehicle weight using

28

lightweight materials) reduces energy requirements during the use phase.3 However,

29

vehicle fuel consumption reductions are beneficial only if not outweighed by increased

30

energy consumption elsewhere in the vehicle’s life cycle (e.g., production or end-of-life).4

31

Life cycle assessment (LCA) is a method that evaluates the environmental impacts

32

of a system or a product across its life cycle stages.5 The GHG emissions mitigation

33

potential of adopting lightweight materials in vehicles has been studied in the literature

34

with a life cycle perspective.4,6–13 Over the vehicle lifetime, the improvement in fuel


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consumption due to overall weight reduction is generally found to offset the potentially

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more energy-intensive production phase. However, the estimated reductions are strongly

37

affected by different assumptions and methods in these studies (e.g., the mass ratio

38

between the lightweight material and the substituted baseline material, or the weight-

39

induced fuel consumption).3 As a result, estimates of the life cycle energy reductions of

40

lightweighting with aluminum and/or high strength steel (HSS) vary from a few percent to

41

more than 50%.3 In addition the size and engine technology of the studied vehicle have

42

an important influence on the GHG emission changes due to lightweighting.14–16

43

Alternative vehicles such as electric or hybrid vehicles typically provide lower life cycle

44

GHG emission reductions due to lightweighting compared to conventional (internal

45

combustion engine) vehicles because of their higher powertrain efficiencies.17

46

Equivalently, electrifying a lightweighted vehicle offers less life cycle emission reductions

47

than electrifying a non-lightweighted vehicle suggesting a need to study lightweighting

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and electrification simultaneously. Similarly, lightweighting vehicles with lower fuel

49

efficiency and larger engines provides larger life cycle emission reductions than

50

lightweighing lower fuel efficiency vehicles, particularly when the powertrain is resized to


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maintain constant performance characteristics after-lightweighting.16–18 Thus, besides

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uncertainties in the fundamental lightweighting parameters, there is also substantial

53

variation in the GHG emission reduction potential associated with the type of vehicles

54

being lightweighted.

55

Traditional product-centered LCAs provide useful insights to improve upon current

56

lightweight designs or powertrain technologies, but are not appropriate for examining the

57

dynamic effects of technological changes at a large scale.19,20 Vehicle-based LCAs do

58

not assess the potential fleet-level GHG emission reductions due to vehicle

59

lightweighting. Fleet-based assessments that combine LCA with a dynamic fleet model

60

provide insights into environmental implications over time of new vehicle designs19, or

61

powertrain technologies by capturing the evolution of light-duty vehicle attributes (e.g.,

62

engine technology, size, weight)21. Among the studies that used a life cycle approach to

63

assess the fleet-scale implications of lightweighting the light-duty fleet20–31, only one

64

focused on the U.S. fleet and it only looked at energy use26 and not GHG emissions. Das

65

et al.26 assessed the cumulative energy demand of lightweighting alternative powertrain

66

vehicles with a fleet-based life cycle energy assessment of the U.S. light-duty fleet. They


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found that fleet lightweighting results in greater energy savings in conventional-vehicle

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dominated market scenarios compared to alternative-vehicle-dominated market

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scenarios.26 Other studies that explicitly considered the penetration of alternative

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powertrains along with lightweighting examined these as two distinct ways of reducing

71

GHG emissions or as part of their modeling and did not evaluate interactions among

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these.22,23,28 Palencia et al.22,23 developed fleet-based models to assess different market

73

penetrations of conventional and alternative vehicles with and without lightweighting in

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Colombia. They concluded that alternative powertrain penetration has a larger potential

75

to reduce the fleet GHG emissions than lightweighting in Colombia. Finally, Modaresi et

76

al.28 estimated the worldwide life cycle GHG emission reductions due to lightweighting

77

with a focus on steel and aluminum. They determined that ‘extreme lightweighting’ using

78

aluminum could provide 8% reduction in cumulative life cycle GHG emissions over 40

79

years, and that closing the loops of automotive materials (i.e., improving the recycling

80

processes to increase the recycled content of new vehicles) could enhance these

81

emission reductions by up to 33%. Their modeling included the increasing penetrations


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of alternative vehicles but did not consider the impact of increasing engine efficiency over

83

time on the lightweighting GHG emission mitigation potential.

84

There is little information available on the fleet-scale GHG implications of

85

lightweighting the U.S. light-duty fleet, or the interactions between lightweighting and

86

technological changes such as fuel consumption improvement strategies or electrification

87

at a fleet level. The latter gap is particularly noteworthy given the increase in vehicle

88

efficiency and penetration of electric powertrains that may be required in the U.S. to meet

89

the 2025 CAFE standards.32 We address the above gaps in the present study with the

90

objective of quantifying the changes in GHG emissions that would result from

91

lightweighting the U.S. light-duty fleet from 2016 to 2050. More specifically, we evaluate

92

the influence of electric vehicle penetration, fuel consumption improvements, fleet

93

characteristics (i.e., average light-duty vehicle size and total fleet stock), and automotive

94

material flow on the U.S. fleet-scale GHG emission changes due to lightweighting. Finally,

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we examine GHG emissions as a function of the temporal penetration of lightweighting.

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Overall, we provide insights on: Should lightweighting be implemented in the U.S. light-


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duty fleet? And if so, are there substantial emission reduction benefits to accelerating the

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pace and intensity of lightweighting?

99

2. Method

100

The following section presents an overview of the method with additional details in the

101

Supporting Information (SI) and the first release of the FLAME model can be found in a

102

GitHub repository.33

103

2.1.

Overall method: The FLAME model

104

We develop a fleet-based life cycle model, the FLAME model (Fleet Life cycle

105

Assessment and Material-flow Estimation) for the U.S. light-duty fleet from 2016 to 2050.

106

Four modules (vehicle, fleet, automotive material flow and LCA modules) are linked to

107

comprise FLAME (figure 1). The vehicle module establishes the vehicle characteristics

108

by vehicle type and includes the lightweighting submodule that simulates the vehicle

109

characteristics for different lightweighting scenarios for 2016 onwards. The fleet module

110

calculates the annual fleet stock including model year and fuel type, as well as fleet

111

kilometers traveled and fuel use by vehicle type. Then, the automotive material flow

112

module estimates the quantities of recovered materials available from scrapped vehicles,


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along with consumption of primary and secondary (recycled) materials for new vehicle

114

production. Finally, the LCA module combines outputs from all other modules with fuel

115

and material emission factors to calculate the annual life cycle GHG emissions of the

116

light-duty vehicle fleet.

117 118

Figure 1. The FLAME model. The model is comprised of four modules (vehicle, fleet,

119

automotive material flow, and LCA modules) and is run from 2016 to 2050.

120

2.2.

Vehicle module


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We consider twelve vehicle technology categories34 and two size categories, cars and

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light trucks35, to represent the U.S. light-duty fleet. Each vehicle type is assigned a

123

material composition (% breakdown), curb weight (kg), and fuel consumption (l or

124

kWh/100 km) for each model year. The vehicle material composition consists of six

125

materials or material categories that sum to the curb weight: 1) high strength steel and

126

advanced high strength steel (HSS/AHSS), 2) cast iron, 3) mild steel and other steels, 4)

127

wrought aluminum, 5) cast aluminum, and 6) all other materials. The other materials

128

combined typically represent less than 25% of the vehicle’s weight (Figures SI.5 and SI.6

129

in SI.2.2.3).

130

The vehicle material composition, curb weight and fuel consumption are historically

131

calculated from 1980 to 2015 and estimated from 2016 onwards in the lightweighting

132

submodule with several scenarios. The “No lightweighting” scenario considers a constant

133

set of material composition for all vehicle types based on the 2015 values. The “Steel

134

Intensive”, “Aluminum Intensive” and “Aluminum Maximum” scenarios are derived from

135

the scenarios of Modaresi et al.28, and essentially focus on two common lightweight

136

materials: HSS/AHSS and aluminum. HSS/AHSS and aluminum can provide similar


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structural performance as conventional materials (i.e., steel and cast iron) but with less

138

weight. The lightweighting scenarios consist of a set of subcomponent-specific material

139

replacement rates and material substitution factors of replaced and replacing materials

140

(SI.2.3.1 for more details). For example, the “Steel Intensive” scenario substitutes 100%

141

of steel in body with HSS/AHSS with a substitution factor of 0.8 corresponding to a

142

primary 20% weight reduction of steel in body. The “Aluminum Intensive” scenario

143

possesses the same subcomponent-specific replacement rates as the “Steel Intensive”

144

but with different and often lower substitution factors (e.g., 0.6 from mild steel to cast

145

aluminum in body. Figure SI.9). Finally, the “Aluminum Maximum” scenario possesses

146

larger replacement rates and for more components. By default, the scenarios include a

147

gradual 15-year linear intensification of vehicle lightweighting from 2016 to 2030. The full

148

lightweighted designs are achieved for the 2030 model-year vehicles and in every year

149

from 2016 to 2030, new vehicles are lighter than in the previous year. In all scenarios we

150

account for unrelated vehicle weight increases driven by the inclusion of more vehicle

151

features and assume that 102 kg is added to vehicle weight from 2016 to 2050 based on

152

historical trends reported in MacKenzie et al.36 From the primary material substitutions


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based on the lightweighting scenarios, and feature weight increases, structural secondary

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mass changes are estimated. The secondary mass changes represent further weight

155

changes achieved in some supporting components, the powertrain or in the battery. The

156

method is taken from Alonso et al.37 and Luk et al.14 using equations presented in

157

MacKenzie et al.38 Relatively to baseline, secondary mass savings represent 51% of the

158

total mass savings from lightweighting in the Aluminum and Steel Intensive scenarios and

159

46% of the mass savings in the “Aluminum Maximum” scenario for conventional cars

160

(Figure SI.17). Table 1 contains the material breakdown of the same conventional car

161

before and after application of the complete lightweighting scenarios (Figures SI.18 and

162

SI.19 for other vehicle types).


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Table 1: Material breakdown of an average conventional internal combustion engine

164

gasoline car in 2016 and in 2030 for the No Lightweighting Scenario and three

165

lightweighting scenarios: Steel Intensive, Aluminum Intensive, and Aluminum Maximum.

166

The results include the primary material substitution, the feature content weight increase

167

and the secondary mass savings.

Conventional car material breakdown (kg)

No 2016 Lightweighting (2030)

Steel Intensive (2030)

Aluminum Intensive (2030)

Aluminum Maximum (2030)

HSS/AHSS

291

298

486

280

97

Cast Iron

117

121

116

109

50

Mild steel and other steels

589

602

341

327

255

Wrought Aluminum

42

44

42

136

310

Cast Aluminum

117

121

117

153

181

Other

461

512

504

493

484

Total

1617

1698

1606

1498

1377

Weight savings (kg)

92

199

320

Weight savings (%)

5

12

19

168 169

The overall mass changes directly influence the vehicle fuel consumption. From the

170

technical attributes of the vehicles and the powertrain technology, vehicle type specific

171

fuel reduction values (FRVs) are estimated and used to obtain the fuel consumptions after


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weight changes. The FRVs are computed annually based on the physics-based models

173

in Kim et al.16–18. The theoretical calculations of the FRVs are able to consider

174

adjustments in powertrain characteristics to result in equivalent performance

175

(acceleration time and horsepower) before and after lightweighting. By default, we

176

calculate FRVs as the mean of FRVs with and without powertrain adjustments; the

177

resulting mean values are consistent with assumptions from existing literature16–18. We

178

assess bounding scenarios with and without powertrain adjustments in the sensitivity

179

analysis. In addition, we model changes to FRVs due to annual efficiency improvements

180

derived from Mowad et al.39 that occur independent of the lightweighting scenarios

181

(SI.2.3.2). In 2016, estimated FRVs are 0.36 and 0.08 L of gasoline equivalent per 100

182

kg and 100 km for internal combustion engine gasoline cars and battery electric cars,

183

respectively.

184

In addition, fuel consumptions are changed annually based on the AEO 201834

185

projections. The fuel consumption improvements are based on sets of technological

186

advances (e.g., engine friction reduction) implemented in the AEO projections and

187

dependent on economic and policy factors. We build three scenarios to account for the


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parameter sensitivity (no improvement, default, and high fuel consumption improvements

189

after 2016 in new vehicles).

190

2.3.

Fleet module

191

The fleet module calculates the annual stock of on-road light-duty vehicles by type and

192

model year40, the annual fleet kilometers traveled (Fleet KT), and fleet fuel use (SI.3 for

193

more details). We consider three prospective fleet stock cases based on the reference,

194

high oil price and low oil price cases of AEO 2018.34 The cases represent bounding

195

estimates of the prospective fleet stock size and light-truck shares. In addition, oil prices

196

also influence the prospective fuel consumption improvements and the technology market

197

share of the vehicles. We therefore evaluate the sensitivity of fleet GHG emissions to oil

198

prices.

199

We also develop a High EV Penetration case to represent optimistic projections of

200

battery electric vehicle penetration based on estimates from Bloomberg Energy

201

Finance41. In the High EV Penetration case, battery electric vehicle sales represent 26%

202

of the light-duty vehicle market share in 2030 and 58% by 2040 (Figure SI.34).

203

2.4.

Automotive Material Flow module


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The automotive material flow module estimates annually the amount of recovered

205

materials from scrapped vehicles along with an exogenous contribution of scrap material

206

from other sectors and the consumption from new vehicles of secondary and primary

207

materials. The method is based on Modaresi et al.28 and two scenarios are developed

208

(SI.4 for more details). The “Business As Usual” scenario considers no improvements in

209

the material recycling processes of the scrapped automotive materials. Steel and cast

210

iron are partially downcycled to construction steel, and wrought aluminum components

211

are partially downcycled to cast aluminum. The “Closed Loop” scenario considers

212

improvements in the material recycling processes by 2050 to recover steel and aluminum

213

components of the scrapped vehicles for the new vehicles.

214

2.5.

LCA module

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The LCA module estimates the annual life cycle GHG emissions associated with the

216

U.S. light-duty fleet over the period 2016-2050. For each year, it includes the emissions

217

from new vehicle production (materials production, manufacturing and assembly phases),

218

use-phase of driven vehicles (fuel production and use), and disposal of scrapped

219

vehicles. Imported and U.S. produced vehicles are assumed to have equivalent


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production emissions. GHG emissions are estimated using the 100-year Global Warming

221

Potentials of the IPCC 5th assessment report42 with climate-carbon feedbacks. Emissions

222

factors for fuels and materials are derived from GREET 201743, and the ecoinvent

223

database version 3.344. Emission factors of electricity production and of primary aluminum

224

production are updated annually (SI.5 for more details).

225

3. Results

226

3.1.

Light-duty fleet GHG emissions

227

Figure 2a shows the annual life cycle fleet GHG emissions for a baseline with

228

technological changes (i.e., with penetration of alternative powertrain and fuel, and fuel

229

consumption improvements) and three main lightweighting scenarios. Figure 2b uses a

230

baseline without technological changes to illustrate the relative contribution of fuel

231

consumption improvements, alternative powertrain penetration and Aluminum Maximum

232

lightweighting. Despite an increasing fleet stock (Figure SI.36), alternative powertrain

233

penetration and especially fuel consumption improvements lead to a minimum of 1.38 Gt

234

CO2 eq. per year in 2034 (Figure 2b). Decreasing emissions are due to the gradual

235

penetration of more efficient vehicles (driven by CAFE standards), after which increasing


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Fleet KT begins to dominate again through 2050. These emissions are dominated by fuel

237

production and use (Figure SI.57), respectively representing 17% and 76% of the annual

238

fleet GHG emissions in 2016. By 2050, lightweighting can contribute up to 0.17 Gt CO2

239

eq. reduction in annual fleet GHG emissions or 11% in the case of the Aluminum

240

Maximum scenario.

241

Lightweighting generally brings higher upfront emissions, and a cumulative perspective

242

is needed to assess the resulting life cycle GHG emission changes (Figure 2c). The

243

Aluminum Maximum scenario achieves cumulative reductions of 2.9 Gt CO2 eq. or 5.6%

244

from 2016-2050, compared to the baseline with technological changes. This is 2.3 and

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1.3 times more reductions than the Steel Intensive and Aluminum Intensive scenarios,

246

respectively. Through 2050, the Aluminum Maximum scenario includes large reductions

247

from the fuel production and use phases (3.1 and 0.9 Gt CO2 eq. respectively) but adds

248

1.1 Gt CO2 eq. in primary material production from the higher need for aluminum. The

249

Steel Intensive scenario decreases the GHG emissions of the primary material production

250

phase but brings lower fuel savings. Thus, while scenarios with progressively more

251

lightweighting may or may not have higher upfront emissions (i.e., production phase),


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they result in net GHG emission reductions due to greater fuel savings. The Steel

253

Intensive scenario reduces emissions immediately, but the Aluminum Intensive and

254

Aluminum Maximum scenarios take 1.4 year and 6.3 years, respectively, before reducing

255

cumulative emissions relative to the No Lightweighting scenario.

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Figure 2. a) Annual U.S. light-duty fleet life cycle GHG emissions 2016-2050 by

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lightweighting scenario; (b) Annual U.S. light-duty fleet emissions 2016-2050 showing

259

contributions

260

improvements, and Aluminum Maximum lightweighting (the blue area is the cumulative

261

GHG emission reduction due to Aluminum Maximum lightweighting); (c) Cumulative light-

262

duty fleet GHG emissions changes 2016-2050 due to lightweighting, showing

263

contributions from life cycle phases.

264

3.2.

from

alternative

powertrain/fuel

penetration,

fuel

consumption

Effects of external factors on GHG emission changes from lightweighting

265

We assess four external factors and their influences on GHG emission changes from

266

lightweighting: fuel consumption improvements through vehicle efficiency improvements,

267

alternative powertrain and fuel penetration, automotive material recovery, and oil prices.

268

The first factor (fuel consumption improvements) ranges from no improvement to high

269

vehicle efficiency improvements after 2016 for all vehicle types. The high efficiency

270

improvements lead to decreasing fuel consumptions and FRVs (fuel consumptions for

271

conventional gasoline cars without lightweighting decrease from 8.7 to 6.8 L/100km and


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FRVs from 0.36 to 0.27 (L/100km)/100kg between 2016 and 2030). The alternative

273

powertrain and fuel penetration factor ranges from no further penetration after 2016 to

274

High EV Penetration (amounting to 151 million electric vehicles in 2050). The automotive

275

material recovery factor ranges from a business-as-usual recycling loop to a closed loop.

276

For the Aluminum Maximum scenario in 2030, 78% of the wrought aluminum components

277

are from primary aluminum in the business-as-usual loop and 70% in the closed loop

278

(Figure SI.46). Finally, the oil price factor ranges from low oil prices (313 million vehicles

279

with 47% light-trucks in 2050) to high oil prices (255 million vehicles with 35% light-trucks

280

in 2050).

281

Figure 3a shows ranges for the cumulative GHG emission changes from lightweighting

282

during the period 2016-2050, separated into cases representing bounding assumptions

283

for each of the four external factors. For each simulation, all factors are kept at their

284

default values except the factor under study. Lightweighting has larger GHG emission

285

reduction potential (left bound of each line) when fuel consumption improvements and

286

alternative powertrain are not implemented, when oil prices are low, or when automotive


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material recovery is high for aluminum lightweighting and low for steel lightweighting. The

288

Aluminum Maximum scenario always offers the deepest cumulative emission reductions.

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Figure 3. (a) Cumulative 2016-2050 changes in U.S. light-duty fleet life cycle GHG

291

emissions by lightweighting scenario and bounding cases for four external factors; (b)

292

Annual U.S. light-duty fleet GHG emission changes 2016-2050 assuming no alternative

293

powertrain and fuel penetration beyond 2016; (c) Annual U.S. light-duty fleet GHG

294

emission changes 2016-2050 assuming the High EV Penetration case.

295

3.2.1. Fuel consumption improvements

296

When vehicles are less efficient, lightweighting provides higher cumulative GHG emission

297

reductions, i.e., 3.4 Gt CO2 eq. reductions for Aluminum Maximum lightweighting when

298

assuming no fuel consumption improvements after 2016, compared to 2.7 Gt CO2 eq.

299

reductions with high fuel consumption improvements. Efficiency improvements (largely

300

driven by CAFE standards45) are expected to decrease the FRVs of lightweighting, and

301

vehicles will have less fuel consumption reduction for the same weight savings. However,

302

expected vehicle efficiency improvements lead to lower GHG emissions than in the ‘no

303

improvement’ case, despite diminishing the lightweighting GHG emission mitigation

304

potential.


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3.2.2. Alternative powertrain and fuel penetration

306

Figures 3b and 3c show the annual contribution to GHG emission reductions from

307

alternative powertrain penetration, fuel consumption improvements and Aluminum

308

Maximum lightweighting, for bounding assumptions regarding EV penetration levels. The

309

High EV Penetration case increases the overall efficiency of the light-duty vehicles and

310

therefore reduces the lightweighting GHG emission mitigation potential. Electric vehicles

311

possess lower FRV of lightweighting than conventional vehicles (Figure SI.22), which

312

reduces by 26% the cumulative GHG emission reductions from lightweighting in a high

313

EV scenario compared to no further penetration (from 3.1 to 2.3 Gt CO2 eq. over the

314

period 2016-2050 for Aluminum Maximum lightweighting). Overall, however, the High EV

315

Penetration case offers lower annual fleet GHG emissions, mostly after 2030.

316

Lightweighting can increase the vehicle production cost and may be prohibitive for more

317

costly alternative vehicles. However, we find that selectively lightweighting only

318

conventional or hybrid vehicles is nearly as effective at reducing GHG emissions as

319

complete lightweighting of all technologies. We assess the potential “rivalry” of

320

lightweighting and alternative vehicle penetration by developing an Adapted Aluminum


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Maximum scenario. This scenario is built from the Aluminum Maximum for conventional

322

vehicles (where lightweighting offers the greatest GHG emission reductions), intensive

323

lightweighting using aluminum for hybrid vehicles and no lightweighting for electric

324

vehicles (where lightweighting offers the lowest GHG emission reductions). The Adapted

325

Aluminum Maximum scenario achieves 97% of the complete Aluminum Maximum

326

scenario’s cumulative GHG reductions in the reference fleet stock case and 85% in the

327

High EV Penetration case (Figure SI.59). Nevertheless, lightweighting BEVs may have

328

additional benefits such as increasing vehicle range, which may in turn affect BEV

329

penetration.

330

3.2.3. Automotive material recovery

331

Gradually shifting toward full recycling of all automotive materials increases the

332

cumulative GHG emission reductions from lightweighting when aluminum is the primary

333

lightweighting material (from 2.9 to 3.2 Gt CO2 eq. in the default and closed loop scenarios

334

respectively for Aluminum Maximum). Although closing the loop reduces emissions in all

335

scenarios, it actually provides greater benefit to the baseline No Lightweighting scenario

336

than to the Steel Intensive scenario because of the lower overall steel weight in vehicles


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337

after substitution with HSS/AHSS and the equivalent primary and secondary production

338

emission factors of mild steel and HSS/AHSS. Modaresi et al.28 found that an automotive

339

material closed loop recycling scenario increases the GHG emission reductions from

340

lightweighting with Aluminum by 30%. Our modeling for the U.S. light-duty fleet suggests

341

that closing the loop of the automotive materials increases the GHG emission reductions

342

from lightweighting by 10% in the Aluminum Maximum scenario over the 2016-2050

343

period. The difference in findings is presumably largely a result of the different

344

geographical scopes of the two studies (i.e., U.S. and world) that provide distinct fleet

345

characteristics.

346

3.2.4. Oil prices

347

The GHG emission reductions of lightweighting the U.S. light-duty fleet in a low oil price

348

case are more important than in a high oil price case (3.3 compared to 2.5 Gt CO2 eq.

349

cumulative reductions over the period 2016-2050). When oil prices are high, the light-duty

350

fleet stock decreases, and manufacturers are urged to develop more efficient and/or

351

smaller vehicles. Conversely, low oil prices impede fuel consumption improvements or

352

the penetration of alternative vehicles and increase the fleet stock and sale shares of light


27


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353

trucks (Figure SI.30). From 2016 to 2050 in the U.S., the most important influence of oil

354

prices on the fleet GHG emissions are the size of the fleet stock and the share of light-

355

trucks (Figure SI.60). Oil prices substantially affect the prospective fleet GHG emissions

356

and a low oil price case results in higher fleet GHG emissions even with the increased

357

benefits of lightweighting.

358

3.3.

Pace and timing of U.S. light-duty fleet lightweighting

359

Faced with fuel consumption improvements and increasing penetration of alternative

360

powertrains, the GHG mitigation potential of lightweighting decreases over time. Delaying

361

the lightweighting implementation of the Aluminum Maximum scenario by 15 years (i.e.,

362

starting in 2030, less than halfway through the study period) reduces the 2016-2050

363

cumulative changes due to lightweighting by 72% in the default case (from -2.9 to -0.8 Gt

364

CO2 eq. in figure 4a). The results reflect the non-linearity of the fleet GHG emission

365

reductions, the decreasing assessment period of lightweighting (2030-2050 instead of

366

2016-2050), and the decreasing GHG emission mitigation potential of lightweighting per

367

Fleet kilometer traveled (Fleet KT) over time.


28

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368

We show the GHG emission mitigation potential of lightweighting per fleet kilometers

369

traveled for three lightweighting implementation-starting years (2016, 2023 or 2030) in

370

Figure 4b. The GHG emission changes due to lightweighting are calculated over a 20-

371

year period from each starting year. For example, the lightweighting implementation-

372

starting year 2023 has a gradual linear implementation of lightweighting from 2023 to

373

2038. The cumulative GHG emission changes from lightweighting are calculated from

374

2023 to 2043 and are divided by the cumulative light-duty fleet kilometers traveled from

375

2023 to 2043. Delaying by 15 years the lightweighting implementation decreases by 16%

376

the 20-year normalized lightweighting GHG emission mitigation potential (from -8.0 to -

377

6.7 g CO2 eq. per Fleet KT over 20 years for Aluminum Maximum scenario in default

378

case). The smaller GHG emission changes for later implementation years result from the

379

expected fuel consumption improvements in the near term and the alternative powertrain

380

penetration in the longer term. If high technological changes (i.e., high fuel consumption

381

improvements and High EV Penetration) occur, waiting until 2030 to start lightweighting

382

with the Aluminum Maximum scenario decreases the 20-year normalized GHG emission

383

reductions from lightweighting by 49%, (from -6.5 to -3.3 g CO2 eq. of cumulative GHG


29


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384

emission changes per Fleet KT over 20 years). In such a case, the Aluminum Intensive

385

scenario provides more GHG emission reductions than the Aluminum Maximum over 20

386

years.

387 388

Figure 4. (a) Cumulative U.S. light-duty fleet life cycle GHG emission changes (2016-

389

2050) for three lightweighting implementation starting years, 2016, 2023 and 2030; (b)

390

Cumulative U.S. light-duty fleet life cycle GHG emission changes from lightweighting over

391

a 20-year period normalized by fleet kilometers traveled (Fleet KT) for three lightweighting

392

starting years, 2016, 2023 and 2030. The high technological changes case assumes high

393

fuel consumption improvement and the High EV Penetration case. The no technological


30

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394

changes case assumes no fuel consumption improvement and no further penetration of

395

alternative powertrains after 2016.

396

Finally, faster rates of lightweighting (e.g., reducing the lightweighting implementation

397

period) increase the absolute GHG emission reductions and reduce the GHG emission

398

payback time associated with the lightweighting scenarios (5.0 years for a 2-year

399

implementation period compared to 6.8 years for a 20-year period in the Aluminum

400

Maximum scenario, Figure SI.62).

401

3.4.

Sensitivity analysis

402

A single-factor sensitivity analysis is conducted on key parameters that do not include

403

the scenario factors previously described (Figure SI.63). The GHG emission changes

404

from lightweighting are most sensitive to the substitution factors of the lightweight

405

materials, the FRVs, and secondary weight changes. The results have a low sensitivity

406

to electricity supply mix assumptions for electric vehicles because of the relatively small

407

penetration of BEVs and PHEVs and a more important sensitivity to electric supply mix

408

assumptions for aluminum production. Figure SI.64 shows that for a combination of high


31


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409

substitution factors (e.g., 0.99 and 0.65 kg of cast/wrought aluminum per kg of steel in the

410

body and chassis, respectively) and no secondary weight savings, the Aluminum

411

Maximum lightweighting may increase cumulative fleet GHG emissions by up to 1.9 Gt

412

CO2 eq. or 4% over the period 2016-2050 compared to a baseline scenario without

413

lightweighting. Conversely, for a combination of low substitution factors (e.g., 0.29 and

414

0.5 kg of cast/wrought aluminum per kg of steel in the body and chassis, respectively),

415

high secondary mass savings, and a closed loop automotive material flow, the Aluminum

416

Maximum lightweighting could save 7.3 Gt CO2 eq. or 14% of the 2016-2050 cumulative

417

fleet GHG emissions without lightweighting.

418

4. Discussion

419

4.1.

420

Fleet GHG emission reduction target in 2025: Lightweighting a stand-alone

measure?

421

In 2015, the United States submitted a Nationally Determined Contribution pledging to

422

reduce GHG emissions in 2025 to 26-28% below 2005 levels.46 Assuming equivalent

423

contributions of all economic sectors and the prospective fleet stock of the AEO 2018

424

reference case from 2015 to 2025, the light-duty fleet would require 227 Mt CO2 eq. of


32

Page 33 of 49


425

GHG reductions in 2025 compared to the baseline fleet GHG emissions. Lightweighting

426

the U.S. light-duty fleet would likely prevent an otherwise expected rise in baseline fleet

427

GHG emissions (by 40 MT CO2 eq. in 2025 over 2016) and further provide some fleet

428

emission reductions. Alone, the Aluminum Maximum lightweighting scenario would

429

achieve 28 Mt CO2 eq. of life cycle emission reductions in 2025 compared to a baseline

430

without technological changes. This value results from 54 Mt CO2 eq. of emissions

431

reductions in the transportation sector (i.e., accounting for upstream and tailpipe

432

emissions of fuels including imported fuels) offset by a 26 Mt CO2 eq. increase in sectors

433

associated with vehicle production including imported vehicles in the U.S. (e.g., in 2017,

434

43% of the new vehicles are produced outside the U.S.). Thus, lightweighting is not a

435

stand-alone measure to attain the emissions target pledged in 2015, amounting to only

436

12% of the target with a life cycle approach.

437

We considered two technological changes that would reduce the life cycle fleet GHG

438

emissions: fuel consumption improvements, and alternative vehicle and fuel penetration.

439

Using default values, these two measures, together with Aluminum Maximum

440

lightweighting, provide 149 Mt CO2 eq. of GHG emission reductions in 2025 in the


33


Page 34 of 49

441

transportation sector compared to the baseline without technological changes. However,

442

this also implies 24 Mt CO2 eq. of GHG emission increase in other industrial sectors.

443

These 124 Mt CO2 eq. of life cycle GHG emission reductions correspond to 55% of the

444

emissions target and therefore these measures are still not sufficient. Among our

445

simulations, only a high oil price case coupled with the three previous measures would

446

achieve the emissions target in 2025.

447

4.2.

Long term prospects of lightweighting

448

In the absence of fuel consumption improvements or alternative powertrain penetration,

449

lightweighting could reduce cumulative life-cycle emissions from 2016 to 2050 by 3.7, 2.7

450

or 1.5 Gt CO2 eq. (6.1%, 4.5% and 2.5%) in the Aluminum Maximum, Aluminum Intensive,

451

and Steel Intensive scenarios, respectively. For comparison, our default assumptions for

452

fuel consumption improvements alone, and alternative powertrain and fuel penetration

453

alone reduce cumulative emissions by 6.5 Gt CO2 eq. (11%), and 2.1 Gt CO2 eq. (3.4%)

454

respectively. The more aggressive electric vehicle penetration case provides larger

455

reductions (6.5 Gt CO2 eq. or 12%). However, the reductions due to alternative powertrain

456

penetration are not expected to be substantial before 2030, even in the more aggressive


34

Page 35 of 49


457

case. This means that while a high penetration of alternative powertrains drastically

458

decreases the fleet GHG emissions, conventional vehicles continue to play a major role

459

in the transportation sector in the near- to mid-term. During this period, lightweighting is

460

an effective strategy to decrease the fleet GHG emissions. In the longer term, other

461

lightweight materials, such as magnesium11 or carbon fiber-reinforced polymers47, could

462

provide higher substitution ratios and weight savings, but their wide-scale adoption is

463

currently limited because of their high energy intensities and costs.

464

We outline substantial interactions between fuel consumption improvements,

465

alternative powertrain penetration and lightweighting to reduce the fleet GHG emissions.

466

Estimating the GHG emission changes associated with specific measures depends on

467

the order in which the measures are modeled and implemented (Figure SI.65). No matter

468

the modeling or implementation order, all three options reduce the fleet GHG emissions

469

but their contributions vary considerably. From the best to the worst ordering, cumulative

470

GHG emission reduction potentials over the period 2016-2050 range from 2.8% to 3.4%

471

for alternative powertrain penetration, from 5.6% to 6.1% for Aluminum Maximum

472

lightweighting, and from 10% to 11% for fuel consumption improvements. This raises the


35


Page 36 of 49

473

important question of the most cost-effective way to reduce the fleet GHG emissions,

474

which is left for future work.

475

4.3.

Caveats and limitations

476

Some important model limitations include the lack of detailed consideration of vehicle

477

attributes such as acceleration time or engine power and of the interconnections between

478

lightweighting and vehicle attributes. For example, weight savings from lightweighting may

479

not be used solely for fuel savings but also to enhance the vehicle horsepower.48 In addition, we

480

did not consider economic costs in the lightweighting implementation. For example,

481

economic competition between lightweighting and electric vehicles is not thoroughly

482

assessed as lightweighting can also increase the range of electric vehicles and therefore

483

influence the penetration of alternative vehicles. A consumer choice model could be

484

embodied in the current modeling to study this effect. Finally, the fuel consumption

485

improvement measures implemented in the model are based on the AEO 201834

486

projections. As CAFE standards are still in place at the time of writing, manufacturers’ aim

487

to reach the CAFE standards for their fleet and fuel consumption improvements are


36

Page 37 of 49


488

expected. However, the exact set of measures that will be implemented is unknown and

489

we do not forecast the solutions to be implemented.

490

4.4.

Lightweighting: How and when?

491

The major contribution of this paper is the enhancement of fleet-based models to tackle

492

more complex and challenging questions related to the temporal deployment of

493

lightweighting from a GHG emissions perspective. We develop multiple lightweighting

494

scenarios for conventional and alternative vehicles, and consider the effects of

495

technological improvements on the fuel reductions from lightweighting.

496

We show that lightweighting using high-strength steel or aluminum is likely to provide

497

fleet GHG emission reductions. The choice of aluminum as the lightweighting material

498

could achieve deeper reductions than HSS with the same initial replacement rates. In

499

addition, a more aggressive lightweighting scenario would lead to larger GHG emission

500

reductions. We also determine that focusing lightweighting solely or primarily on

501

conventional vehicles is a satisfactory strategy in terms of GHG emission reductions.

502

Finally, we show that the timing of lightweighting matters. Our simulations always

503

suggest that the optimal effects of lightweighting are achieved when the adoption of


37


Page 38 of 49

504

lightweighted design occur as soon as possible. In an increasingly more efficient fleet,

505

equivalent material substitution and weight savings offer lower fuel use reductions.

506

Furthermore, lightweighting the light-duty fleet with aggressive adoption rates provide

507

more rapid and deeper GHG emission reductions.

508

Lightweighting U.S. light-duty vehicles is not a stand-alone measure to decrease the

509

fleet life cycle GHG emissions but is an effective option in the near-term that could provide

510

important savings while the fleet is dominated by conventional vehicles.

511 512

ASSOCIATED CONTENT

513

Supporting Information.

514

This paper contains a supporting information document. Sections 1-5 of the supporting

515

information document describes the full set of assumptions associated with our method.

516

Section 6 contains additional results and section 7 an additional discussion. In addition,

517

a GitHub repository is accessible free of charge that contains all inputs, codes and

518

numerical

519

(https://github.com/amilovanoff/est_milovanoff_et_al_2019).33

520

AUTHOR INFORMATION

521

Corresponding Author

results

associated

with

this

study


38

Page 39 of 49


522

*e-mail: [email protected]

523

ACKNOWLEDGMENT

524

We thank the reviewers for their insightful comments, which improved greatly this

525

manuscript. This work was financially supported by Ford Motor Company through Project

526

Northern Star. While this article is believed to contain correct information, Ford Motor

527

Company (Ford) does not expressly or impliedly warrant, nor assume any responsibility,

528

for the accuracy, completeness, or usefulness of any information, apparatus, product, or

529

process disclosed, nor represent that its use would not infringe the rights of third parties.

530

Reference to any commercial product or process does not constitute its endorsement.

531

This article does not provide financial, safety, medical, consumer product, or public policy

532

advice or recommendation. Readers should independently replicate all experiments,

533

calculations, and results. The views and opinions expressed are of the authors and do

534

not necessarily reflect those of Ford. This disclaimer may not be removed, altered,

535

superseded or modified without prior Ford permission.

536

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A Dynamic Fleet Model of U.S Light-Duty Vehicle Lightweighting and

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