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Cradle-to-gate greenhouse gas (GHG) burdens for aluminum and steelproduction and cradle-to-grave GHG benefits of vehicle lightweighting inChina

Xiaoyi Hea, Hyung Chul Kimb, Timothy J. Wallingtonb, Shaojun Zhangc, Wei Shend,Robb De Kleineb, Gregory A. Keoleiana, Ruoyun Mac, Yali Zhenge, Boya Zhouf, Ye Wuc,g,⁎

a Center for Sustainable Systems, School for Environment and Sustainability, University of Michigan, Dana Bldg. 440 Church, Ann Arbor, MI 48109-1041, United Statesb Ford Motor Company, Dearborn, MI 48121-2053, United Statesc School of Environment, and State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, Chinad Ford Motor Company, Beijing 100022, Chinae China Society of Automotive Engineers, 4F Tianlian Building, Lianhuachi East Rd., Xicheng District, Beijing 100084, Chinaf China Automotive Technology & Research Center Co., Ltd., 68 Xianfeng East Rd, Dongli, Tianjin 300300, Chinag State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China

A R T I C L E I N F O

Keywords:Life cycle assessmentAdvanced high-strength steelAluminumVehicle lightweightingGreenhouse gas emissions

A B S T R A C T

Greenhouse gas (GHG) burdens of steel and aluminum production and life cycle benefits of vehicle light-weighting in China were evaluated. Production of advanced high-strength steel (AHSS) and wrought aluminum(Al) have average cradle-to-gate GHG emissions of 3.9 and 17.5 kg CO2eq/kg. Lightweighting benefits for elevenpassenger car models over five driving cycles (including real-world and regulatory cycles) were determined.Lightweighting using AHSS to replace conventional steel has cradle-to-grave GHG savings in all cases, mainlyattributed to savings in material use. Wrought Al has a much higher GHG production burden than AHSS andrequires greater fuel savings in the use phase to achieve net cradle-to-grave GHG savings. Maximum GHG savingsoccur with Al versus AHSS in cases where the powertrain is resized, travel is congested, or lifetime traveldistance is long. A typical Beijing peak-hour driving cycle with low speed and frequent stop-and-go has higherfuel reduction values (FRVs) and GHG savings than other cycles. Congested travel conditions make light-weighting a particularly effective emissions reduction strategy in China.

1. Introduction

Vehicle lightweighting is an important strategy to improve fueleconomy and mitigate on-road greenhouse gas emissions worldwide.However, lightweight materials such as aluminum, magnesium, andcarbon fiber are generally more energy intensive to produce and gen-erate more emissions than conventional materials such as steel and steelalloys. There is a trade-off between increased emissions in the materialproduction phase and decreased emissions in the fuel-cycle (all stagesrelated to fuel production and consumption) (DOE, 2015; Modaresiet al., 2014; Kim and Wallington, 2013; Kim et al., 2015; Lewis et al.,2014; Schmidt et al., 2004). Life cycle assessments accounting forcontributions from materials production, component manufacture, ve-hicle assembly, fuel production, vehicle use, and end-of-life manage-ment are required to evaluate the environmental impact of

lightweighting.Transparent and accurate data for critical parameters, such as up-

stream energy intensity of material production, recycling rates, materialsubstitution rates, and fuel reduction values (FRVs) are needed to es-timate the potential energy savings and greenhouse gas (GHG) miti-gations from vehicle lightweighting. These parameters need to reflectlocal supply chain and real-world driving conditions. Most publishedstudies have been conducted for vehicle lightweighting in NorthAmerica and Europe (DOE, 2015; Luk et al., 2017; Raugei et al., 2015).However, the upstream industry energy intensity and on-road operationconditions in Asia differ significantly from those in North America andEurope. Recent studies in Japan (Palencia et al., 2015), India(Upadhyayula et al., 2019), and China (Sun et al., 2019) demonstrateincreasing interest in vehicle lightweighting in Asia.

Palencia et al. (2015) reported up to 92.2% GHG reduction potential

https://doi.org/10.1016/j.resconrec.2019.104497Received 4 May 2019; Received in revised form 6 September 2019; Accepted 12 September 2019

⁎ Corresponding author at: School of Environment, and State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing100084, China.

E-mail address: ywu@tsinghua.edu.cn (Y. Wu).

Resources, Conservation & Recycling 152 (2020) 104497

0921-3449/ © 2019 Elsevier B.V. All rights reserved.

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for vehicle lightweighting combined with electricification compared toa baseline fleet in 2050 but focused on fuel cycle emissions instead of acomplete cradle-to-grave analysis. Upadhyayula et al. (2019) report alife cycle GHG emission reduction of 17% from vehicle lightweightingin India, and that material production emission intensity is a criticalparameter. However, Upadhyayula et al. (2019) applied unspecifiedglobal average GHG burdens for steel and aluminum production. Sunet al. (2019) demonstrated that FRVs strongly impact life cycle GHGemissions, but considered generic fuel consumption values and FRVswhich do not provide insight into in-use performance for specific ve-hicle models in the market. None of the above-mentioned studies pro-vided a transparent process-by-process inventory of energy use andemissions during material production in Asia.

To better understand lightweighting benefits from a real-worldperspective, assessments are needed that capture material manu-facturing and real-world driving characteristics in Asian countries.There are several reasons why China, in particular, warrants individualanalysis.

First, there were 29 million vehicles sold in China in 2017 (ATRC,2017), and for the eighth consecutive year, China was the largest globalvehicle market. To address vehicle-related energy and environmentalconcerns, policymakers have mandated fuel efficiency improvements.The Ministry of Industry and Information Technology (MIIT) of theChinese government requires improvement of the fleet average fuelconsumption of passenger vehicles from 6.9 L/100 km in 2016 to 5 L/100 km in 2020 (MIIT, 2012), a compound annual improvement rate of7.7%. The roadmap released by SAE-China proposed fuel consumptionof 4.0 L/100 km in 2025 and 3.2 L/100 km in 2030 (China Society ofAutomotive Engineers (China SAE), 2016). Vehicle lightweighting is aneffective measure to improve vehicle fuel efficiency and mitigate theuse-phase environmental impacts.

Second, China is the largest producer of aluminum and steel, ac-counting for 45% and 50% of global production in 2016 (WorldAluminum Association, 2019; World Steel Association, 2018). Ad-vanced high-strength steel (AHSS) and aluminum are currently thedominant lightweighting options for the mass application. The ChinaSAE roadmap (China Society of Automotive Engineers (China SAE),2016) proposes use of 190 kg aluminum per vehicle and 50% of steel tobe AHSS by 2020. AHSS is generally compatible with existing manu-facturing and materials. Aluminum and Al alloys have well-developedprocessing technologies for high volume manufacturing. Other light-weight materials, including magnesium and carbon fiber, are used inlimited amounts because of high cost and difficulty in manufacturingand are typically viewed as longer-term options (China Society ofAutomotive Engineers (China SAE), 2016; DOE, 2019). A comprehen-sive and self-consistent data set of energy use and emissions associatedwith aluminum and steel production is needed to evaluate and comparethe life cycle energy benefit of vehicle lightweighting. Unfortunately,such a data set is lacking for China. A goal of the present work is toprovide these data.

Third, driving conditions and vehicle fuel economy vary acrosscountries. For example, the average fuel consumption of new light dutyvehicles in 2017 in the U.S., U.K., China, and India is 8.6, 5.8, 7.6, and5.6 L/100 km respectively (Yang and Bandivadekar, 2017). Such var-iance further influences vehicle lightweighting benefits. Greater bene-fits from vehicle lightweighting are observed for trips with higher fuelconsumptions (e.g., city cycle compared with highway cycle), and lessefficient vehicle models (Luk et al., 2017; Kim et al., 2015; Kim andWallington, 2013). Vehicle fuel reduction values (FRVs, reported in L/(100 km 100 kg)) can be used to estimate fuel reduction benefits oflightweighting. Previous studies have often used generic estimates ofFRVs, in the range 0.15–0.3 and 0.25–0.5 L/(100 km 100 kg) forwithout and with powertrain adjustment (Luk et al., 2017). Theseranges lead to uncertainties in estimating energy benefits of light-weighting and cannot be used to estimate benefits for a specific vehiclemodel.

To address the research gap indicated above, this study evaluatesFRVs and life cycle GHG mitigation benefits of eleven passenger carmodels in the Chinese market, with considerations of local features suchas Chinese-specific driving cycles and life cycle inventory data. Cradle-to-gate energy use and GHG emissions associated with the productionof AHSS and aluminum in China are presented and compared withprevious studies in Asia, the U.S., and global markets. FRVs for elevenvehicle models are estimated using on-road fuel consumption data anda physics-based FRV model. An evaluation of the GHG benefits of re-placing conventional steel with AHSS or aluminum in vehicle body isprovided.

2. Method and data

2.1. Scope, assumptions and system boundary

Aluminum and advanced high-strength steel (AHSS) are lightweightalternatives for standard steel that are likely to be widely adopted inpassenger cars in the short term (China Society of AutomotiveEngineers (China SAE), 2016). An estimate of life cycle GHG emissions(ELC) include emissions from material production (Emp), componentmanufacturing and vehicle assembly (Em a/ ), vehicle operation (Evo),fuel production (Efp), maintenance and repair (Em r/ ), and end-of-life(Eeol) as given by Eq. (1).

= + + + +E E E E E ELC mp m a vo m r eol/ / (1)

The present study focuses on the material production, vehicle op-eration, and fuel production stages. Differences in energy use andemissions contributions from other stages (component manufacturingand vehicle assembly, maintenance and repair, and end-of-life) betweenbaseline and lightweighted vehicles are minor (Keoleian and Sullivan,2012; Kelly et al., 2015), and hence not included in the comparativeanalysis. GHG emissions from material production (Emp) include on-siteprocess fuel combustion emissions during material production, up-stream emissions from process fuel production, and non-combustionemissions in the coking process and metal refining (e.g., per-fluorocarbons during aluminum production). Characterization of Empwas based on the Greenhouse gases, Regulated Emissions, and Energyuse in Transportation (GREET) Model developed by Argonne NationalLaboratory (GREET, 2017) using updated material production in-ventory data determined in this study. GHG emissions from vehicleoperation (Evo) includes tailpipe emissions and emissions from gasolineproduction. Tailpipe emissions were calculated from the fuel con-sumption (L/100 km) with changes between the baseline vehicle andlightweighted options determined using FRVs (fuel reduction values, L/(100 km 100 kg)). GHG emissions from gasoline production were cal-culated from fuel consumption determined in the present study, andfuel-cycle emissions factors taken from our previous work (Ke et al.,2017; Wang et al., 2015; Wu et al., 2012).

For simplicity, we do not consider secondary mass reductions, i.e.,mass savings achieved in vehicle supporting parts when the vehiclemass is reduced, because it is less significant than primary mass re-duction, highly uncertain, and requires complex modeling to estimate(Alonso et al., 2012). There are two approaches to account for theimpact of material recycling. The recycled content approach (or cut-offapproach) captures recycling benefits in the material production phasebased on the share of secondary material used. The end-of-life approachaccounts for recycling potential by applying recycling credits. Weadopted the recycled content approach, consistent with the GREETmodel.

2.2. Aluminum (Al) production

Virgin Al production includes bauxite mining, alumina extraction,anode fabrication, electrolysis/ingot casting, and material transforma-tion (casting or forging). Secondary Al processes include scrap

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transportation, scrap preparation, remelting, machining, and materialtransformation (casting or forging) (see Fig. A1 for system boundary).In 2015, the production of primary and secondary aluminum in Chinawas 31.5 and 4.6 million metric tons, respectively. Secondary alu-minum accounted for 19.8% of total cast production and 12.5% of totalwrought production (China Nonferrous Metal Industry AssociationCNMIA, 2016). Domestic scrap Al does not meet the demand for sec-ondary Al in China. In 2015, 2.08million metric tons of aluminumscrap was imported and used to produce 1.24 million metric tons ofsecondary Al, accounting for 27% of total secondary Al production inChina.

Table 1 presents process-by-process energy consumption of alu-minum production in China. Industry data in China were obtained fromapproximately 70 aluminum production enterprises from the ChineseAluminum Association (CAA) and used to calculate the material andenergy consumption database for the two most energy-intensive pro-cesses; alumina extraction and electrolysis (China AluminumAssociation CAA, 2017). Energy use data for other processes were takenfrom official statistics and literature as listed in Table 1. Comparing theresults for China with those for the U.S. from the GREET_2017 model,alumina extraction is much more energy intensive, mainly due to lowerquality bauxite, and electrolysis is less energy intensive, because mostAl production facilities are new, or recently upgraded, and incorporatethe latest technology.

GHG emission intensity for material production is determined bythe energy intensity and share of fuels in each process (oil, electricity,natural gas, coal, etc., see Fig. A1 for details). Primary aluminum pro-duction is an energy-intensive process with electrolysis accounting forthe majority of energy consumption. We used the industry-specificelectricity mix for aluminum production, rather than national averagegrid mix, because the former better represents electricity consumed foraluminum electrolysis (Colett et al., 2016). Provincial primary alu-minum production and electricity consumption data were collected in31 Chinese provinces and used to adjust electricity sources specificallyfor aluminum production. As Fig. 1 shows, coal-fired electricity share ofaluminum electrolysis is estimated to be 82% in 2015, which is higherthan for the average national electricity mix (67%, Chinese ElectricYearbook, 2016). Shandong, Xinjiang, and Henan are the top threeprovinces in terms of aluminum production and the coal-fired elec-tricity shares are higher in these provinces than the national average.

The electrolysis of alumina gives rise to emissions of tetra-fluoromethane (CF4) and hexafluoroethane (C2F6). CF4 and C2F6 areperfluorocarbons (PFCs) with very long atmospheric lifetimes and veryhigh global warming potentials. The CO2-equivalent PFC emissionfactor (EFPFCs, kg CO2eq/t-Al) was calculated using Eq. (2)

= × + ×EF EF EF6630 11100PFCs CF C F4 2 6 (2)

where 6630 and 11100 are the 100-year time horizon global warmingpotentials of CF4 and C2F6 (Hodnebrog et al., 2013), and EFCF4 and

EFC F2 6 are the corresponding emission factors in kg/t-Al. The PFCsemission factors depend on the electrolysis technology and operationconditions. Point Fed Pre Bakes technology is currently used in Chinaand we used the emission factors provided by the Chinese AluminumAssociation, EFCF4 =0.034 kg/t-Al and EFC F2 6 =0.0034 kg/t-Al.

2.3. Advanced high strength steel (AHSS) production

The energy intensity for production of AHSS is generally assumed tobe the same as that of conventional steel (DOE, 2015). Therefore, wechose to evaluate AHSS based on statistics for production of conven-tional steel products in China. Two processes, basic oxygen steelmaking(BOS) and electric arc furnaces (EAF), account for virtually all steelproduction worldwide. Processes in BOS include ore mining, cokeproduction, sintering, pelletizing, blast furnace, basic oxygen furnace,and ingot casting. Processes in EAF include scrap transportation,melting in an electric furnace, and ingot casting. The EAF process inChina uses 550–600 kg scrap to produce one t of crude steel on average,significantly lower than what is observed in other countries, and reliesheavily on hot liquid pig iron input (Vercammen et al., 2017).

Steelmaking in China is dominated by the BOS process, accountingfor 94% of national steel production in 2016 (World Steel Association,2018). The EAF has limited production volume mainly due to the highcost of scrap steel and energy for producing hot liquid pig iron. Weconsider the BOS exclusively below. Steel ingots are converted intosteel sheets through hot rolling and cold rolling processes, and then azinc coating is applied in a galvanizing process (see Fig. A2 for systemboundary). Table 2 shows the process-by-process energy consumptionsfor steel production in China. Energy consumption in major processeswere calculated using data from China Steel Yearbook (2016), whichprovides industry average statistics from China Steel & Iron Association(CSIA) member enterprises that accounted for 84.3% of national pro-duction in 2015. Energy use statistics in the China Steel Yearbook arebased on information from steel enterprises and do not include cokeproduction. Energy consumption data for coke production from Liuet al. (2016) were used in this study.

As seen from Table 2, steel production in China has similar energyintensity to those in the U.S., with slight differences mainly attributedto the different allocation of by-products. The coking process in China,however, is much more energy intensive than the U.S., because small-scale facilities (chamber height of 5.5 m or less) with lower energy ef-ficiency accounts for 48% total coke production in China (China CokingIndustry Association, 2016), while typical coke plants are larger, 6–8mhigh, in western countries (Hein and Kaiser, 2012).

2.4. Lightweighting fuel reduction for real-world driving conditions

2.4.1. FRV modelingA physics-based assessment model (Kim et al., 2015) was used to

Table 1Process energy use for aluminum production in China and the U.S., MJ/t Al.

China (this study) U.S. (GREET) Data source for China

Bauxite Mining 0.523×103 0.671×103 Guo et al., 2014; Ding et al., 2012Alumina extraction: Bayer Process 3.15× 104 2.07× 104 Chinese Aluminum AssociationAnode Production 3.91× 103 1.73× 103 Guo et al., 2014; Ding et al., 2012Electrolysis: Hall-Heroult Process 4.84× 103 5.44× 103 Chinese Aluminum AssociationPrimary Ingot Casting 2.43× 103 1.18× 103 Guo et al., 2014; Ding et al., 2012Scrap transportation and preparation (recycled Al) 5.32× 103 1.27× 103 Updated from Ding et al., 2012Secondary Ingot Casting (recycled Al) 6.17× 103 5.19× 103 Ding et al., 2012

Note: Data here reflect purchased energy inputs and do not account for energy associated with feedstocks or ancillary materials. Chinese statistics usually reportenergy use data in kg of standard coal equivalent per kg of intermediate product, for example, kgce/t Al2O3. We used the thermal unit conversion factors from theNational Bureau of Statistics of the People's Republic of China (see Table A1) and a material conversion factor of 1.95 (t of Al2O3 to produce 1 t of Al). The fuel sharefor each process was broken down into coal, natural gas, electricity, and oil consumption using industry average data (Ding et al., 2012; Keoleian et al., 2012). Fueland material use by process are given in Fig. A1.

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estimate FRVs of eleven passenger cars over five different driving cy-cles. FRVs are usually evaluated without, or with, powertrain resizing.The latter assumes resizing of the powertrain such that the performanceof the lightweighted vehicle matches that of the baseline vehicle. Re-sizing results in additional fuel savings and FRVs with powertrain re-sizing are larger than those determined without powertrain resizing.

Fig. 2 presents the FRV model framework used in the present study.Real-world and regulatory driving cycles in China, and on-road fuelconsumption profiles obtained from eleven passenger cars were used inthe FRV model of Kim et al. (2015). Details of the FRV modeling and thecalculation sheet are given in Appendix.

2.4.2. Real-world and regulatory driving cycles in ChinaWe have recently proposed two cycles to represent real-world

driving in Beijing, namely the Beijing Peak Hour Cycle (BPHC) andBeijing Off-peak Hour Cycle (BOHC) (Ma et al., 2019). Representa-tiveness of the real-world driving cycles was insured by using large-scale GPS data sets from 459 privately-owned passenger sedans andSUVs in Beijing, covering nearly 17,000 sampling days and 3.3 millionkm travelled (He et al., 2016). The cycles are shown in Fig. 3 and de-scribed in detail elsewhere (Ma et al., 2019).

We also include a standard driving cycle developed by the ChinaAutomotive Technology & Research Center (CATARC, 2018), the ChinaLight-duty vehicle Test Cycle (CLTC). The CLTC was developed basedon large-scale real-world driving profiles collected in 41 cities in Chinawith total driving distance of 22.6 million kilometers. Note that the

CLTC is a national average driving cycle whereas the driving conditionsin megacities such as Beijing and Shanghai could be different. Tworegulatory driving cycles, the New European Driving Cycle (NEDC) andthe Worldwide harmonized Light vehicles Test Cycles (WLTC), are in-cluded as well. The NEDC is the current cycle used in China fuel con-sumption regulations, and the WLTC has been adopted in the upcomingChina 6 emission standard for criteria pollutants.

Fig. 1. Electricity mix for aluminum electrolysis (panel a) estimated based on provincial aluminum production (panel b) and provincial electricity mix (panel c; onlytop four provinces for aluminum production are shown here for visual clarity).

Table 2Process energy use for steel production in China and the U.S., MJ/t of steel.

China (this study) U.S. (GREET default) Data source for China

Iron Ore Extraction and Processing (per t of Iron Ore) 0.393× 103 2.09× 103 China Steel Yearbook, 2016Coke Production (per t of Steel) 3.93×104 1.81× 104 Liu et al., 2016Sintering (per t of Steel) 2.21×103 70 China Steel Yearbook, 2016Blast Furnace (per t of Steel) 2.56×103 2.07× 103 China Steel Yearbook, 2016Basic Oxygen Furnace (per t of Steel) 6.69×102 8.04× 102 China Steel Yearbook, 2016On-site Generation and Other Steam Uses and Losses (per t of Steel) 1.22×102 −4.8× 101 China Steel Yearbook, 2016Hot Rolling (per t of Hot Strip) 1.79×103 1.55× 103 China Steel Yearbook, 2016Skin Mill (per t of Hot Rolled Steel) — 0.049×103 see footnoteCold Rolling (per t of Cold Rolled Steel) 2.49×103 1.63× 103 China Steel Yearbook, 2016Galvanizing (per t of Galvanized Steel) 1.61×103 0.81× 103 China Steel Yearbook, 2016Stamping 1.87×103 1.00× 103 see footnote

Note: CSIA=China Steel and Iron Association. These data reflect purchased energy inputs and do not account for energy associated with feedstocks and ancillarymaterials (e.g., lime). Energy input from energy carriers (e.g., blast furnace gas) is combined with purchased energy for CSIA data but listed separately in GREETmodel. Due to the complexity of steel production, some processes were combined in CSIA statistics. Skin mill is merged into hot rolling. Stamping is considered assteel processing in CSIA statistics. The shares of different fuels (coal, natural gas, electricity, coke oven gas, etc.) for each process were estimated using industryaverage data reported by Liu et al. (2016) and Qiu et al. (2007). See Fig. A2 for details.

Fig. 2. Lightweighting fuel reduction value model framework.

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Key cycle parameters for the cycles are compared in Table 3. BPHCand BOHC have the same duration of 1400 s, which is the average tripduration estimated from He et al. (2016). The BPHC represents drivingconditions during peak hours and has an average speed of 23.9 km/hand an idle time of 26%. The BOHC has slightly higher average speed(28.8 km/h) and is similar to the CLTC. The NEDC has higher averagespeed than the two Beijing cycles and is much milder in terms of twotravel dynamics parameters (relative positive acceleration, RPA, andthe 95th percentile of multiply of speed and positive acceleration, S× apos[95]). The WLTC is intermediate in terms of aggressiveness and hastravel dynamics similar to CLTC. However, it has a lower idle timefraction than the Beijing cycles and includes an extra-high speed phase(maximum speed=131 km/h), which is not representative of typical

driving patterns in China.

2.4.3. Real-world energy consumptionVehicle fuel consumption measured in real-world driving is higher

than type-approval values. Our previous study collected on-road fuelconsumption data from eleven vehicles using on-board-diagnostics-II(OBD-II) scan tools (Lu et al., 2018). The OBD-II provides access to real-time diagnostic signals from the engine control unit (ECU). In additionto instantaneous vehicle speed and acceleration, OBD-II also providesreal-time mass airflow and air-fuel ratio to do a quite accurate estimateof fuel rate. The eleven vehicles tested were manufactured from 2015 to2016, including compact, mid-size, and full-size passenger cars (seeTable A5 for displacement and vehicle weight). The vehicles weretested over routes designed to cover different regions, road types, andtravel time.

To evaluate the distance-based fuel consumption, an operatingbinning method was applied, which uses speed and vehicle specificpower (VSP) to represent micro operating conditions and relates themto instantaneous fuel rate. VSP (kilowatts per t) is defined as the in-stantaneous power demand of the vehicle divided by its mass, and is thesum of loads resulting from aerodynamic drag, acceleration, rollingresistance, and hill climbing. The following expression was used tocalculate VSP (Jiménez-Palacios, 1999; Zhang et al., 2014).

= × × + × + + × ×−VSP v a v(1.1 9.81 sin(grade) 0.132) 3.02 10 4 3

(3)

VSP (kW/t) is calculated vehicle specific power; v (m/s) is in-stantaneous vehicle speed recorded by OBD-II; a (m/s2) is instantaneousvehicle acceleration; and grade is the angle of inclination of the road,which is set to 0 in this study.

Fig. 3. Beijing Peak Hour Cycle (BPHC), and Beijing Off-peak Hour Cycle (BOHC) (Ma et al., 2019). Peak hour is 7–9 am and 5–7 pm on weekdays, and off-peak houris all other times.

Table 3Key parameters of different cycles.

Parameters Unit BPHC BOHC CLTC NEDC WLTC

Duration s 1400 1400 1800 1180 1800Distance km 9.3 11.0 14.5 10.9 23.3Average speed km/h 23.9 28.8 29.0 33.3 46.5Average running

speedkm/h 33.1 38.5 37.7 43.6 53.0

Speed [90] km/h 71.0 86.0 114.0 120.0 131.3Idle time percentage % 26 24 23 24 12Average acceleration m/s2 0.507 0.506 0.415 0.480 0.477Average deceleration m/s2 −0.570 −0.558 −0.464 −0.683 −0.514RPA a m/s2 0.21 0.20 0.17 0.11 0.15S× a pos [95]

b m2/s2 6.1 9.2 6.8 6.3 9.2

Note: a RPA stands for relative positive acceleration.b S× a pos[95] is the 95th percentile multiple of speed and positive acceleration.

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Driving conditions were allocated into 28 operating mode binsbased on instantaneous VSP and speed (v), including a deceleration orbraking bin, an idling bin, and others to represent cruise or accelerationdriving modes (Zhang et al., 2014) (see Table A4 for detail).

The distance-based fuel consumption of each driving cycle wascalculated from the average fuel rate for each operating model bin andthe time spent in each bin:

∑ ∑= × × × × ×=

FC PT

FRρ

TD

( 1 ) 1 1 100j ki

i j ki t

T

i j t j kj k

, , ,1

, , ,,

i

(4)

where FCj (L/100 km) is fuel consumption of vehicle j under drivingcycle k; Pi,j,k is time percentage of operating mode i in driving cycle k; Tiis the number of data points for vehicle j in operating mode i ; FRi j t, , isthe instantaneous fuel rate in operating mode i at second t; ρ (g/L) is thedensity of gasoline, 725 g/L for 93 Research Octane Number (RON)gasoline; and Tj k, and Dj k, are travel time and total distance of vehicle jfor driving cycle k. Table A5 shows the distance-based fuel consumptionof eleven vehicles tested over five driving cycles, which were used tocalculate the FRVs and FRVs+.

2.5. Fuel-cycle GHG emissions

The fuel-cycle GHG emissions for combustion of one liter of gasolineis calculated as:

= ⎛⎝ ∂

+ ⎞⎠

× + × ÷EF EF EF LHV ρϕ 1244fc

crudefuel gaso c gaso (5)

where EFFC is the fuel-cycle GHG emission factor, g CO2eq/L gasoline;EFcrude is the GHG emissions for producing crude, 11.7 g CO2eq/MJ; ∂ isloss factor, 0.86MJ gasoline/MJ crude; EFfuel is the GHG emission forproducing gasoline (gasoline blendstock), 13.4 g CO2eq/MJ; LHVgaso islow heating value of gasoline, 116,090 btu/gal =32.4MJ/L ; ϕc is thecarbon content by mass of gasoline, 86.3%; ρgaso =0.749 kg/L. Thevalues above are China-specific data taken from Wang et al. (2015) andKe et al. (2017). 12/44 is the ratio of the atomic mass of carbon to themolecular weight of CO2. Using Eq. (5) gives EFfc =3241 g CO2eq/L.

3. Results

3.1. Cradle-to-gate GHG emissions for AHSS and aluminum

The cradle-to-gate GHG emission results for AHSS and aluminumare given in Fig. 4. We consider wrought aluminum (W. Al) as thelightweight material, which has GHG emissions of 17.5 t CO2eq/t W. Al,based on a share of 87.5% for primary Al. For primary Al ingot, theGREET 2017 model and IAI (2017) reported 8.3 and 17.6 t CO2eq/t Alingot in the U.S. and globally, which are 57% and 10% lower than ourestimation for China (19.5 t CO2eq/t Al ingot). This is mainly a result ofmuch more carbon-intensive electricity used in China. IAI (2017) re-ported 20.1 t CO2eq/t Al ingot in China, higher than our estimation by3%, mainly because they assumed 90% coal-based electricity ratherthan 82% estimated in the present study. Sun et al. (2019) reported15.1 t CO2eq/t primary Al ingot, 23% lower than our estimation, re-ferring to a China Automotive Life Cycle Database (CALCD), but thematerial production system boundaries and process-by-process energyuse and emission profiles were not reported and hence it is difficult tocompare or validate their results. Hao et al. (2016) estimated 16.5 tCO2eq/t Al ingot in China, which is 15% lower than our estimation.This can be explained by the fact that Hao et al. (2016) used a sim-plified approach that accounts for direct emissions from Al industry andindirect emissions from electricity, instead of a complete life cycleanalysis that accounts for upstream fuel-cycle emissions. McMillan andKeoleian (2009) reported 21.9 t CO2eq/t Al primary ingot in Asia,which is 12% higher than our estimation for China. While our estima-tion is based on a higher share of coal-fired electricity (82%, compared

with 60% in McMillan and Keoleian (2009) study), the PFC emissionintensity is lower (2.5 versus 3.25 t CO2eq/t Al primary ingot), re-flecting improvements in emission control progress over the pastdecade. Moreover, our estimation also implies lower smelter electricityintensity compared with previous years. Gao et al. (2009) had a higherestimation of 22 t CO2eq/t primary Al ingot but their study is based on2003 industry data with outdated technology. GHG emissions for sec-ondary Al ingot in China are higher than that in the US (GREET defaultvalue) because of the carbon-intensive electricity and emissions fromlong distance transport of imported scrap Al.

AHSS is estimated to have GHG emissions of 3.9 t CO2eq/t, which isapproximately 20% higher than the U.S. value (3.2 CO2eq/t, GREETdefault). Sun et al. (2019) reported a value of 3.1 CO2eq/t in their studyfor China, but did not provide system boundary and a process-by-pro-cess inventory. The World Steel Association reported much lower GHGemissions for global average steel production (2.4 t CO2eq/t hot-dipgalvanized steel (World Steel Association, 2016)), but did not provide aprocess-by-process inventory, making it difficult to compare resultsdirectly. We do not account for use of secondary steel (produced viaEAF process) in the present work because the EAF process has only 6%share in total steelmaking in China, and also because secondary steel isless likely to be used to produce AHSS due to the need for specific alloyscompositions that are less tolerant of impurities.

3.2. Fuel reduction values

Fuel reduction values without and with powertrain adjustment(FRVs and +FRVs ) of eleven vehicles over five different driving cyclesare presented in Fig. 5. The FRVs and +FRVs are 0.15–0.27 and0.24–0.61 L/(100 km 100 kg), respectively (see Table A5 for calculationsheet), consistent with, but with upper-bounds 20% higher than, thosereported in previous studies (Luk et al., 2017; Kim et al., 2015; Kellyet al., 2015). With powertrain resizing, +FRVs have a positive correla-tion with fuel consumption (see fit in Fig. 5a), indicating that vehicleswith higher fuel consumption (depending on vehicle and driving cycle)generally have greater benefits from weight reduction. Without pow-ertrain resizing, FRVs show no discernable correlation with fuel con-sumption for a given cycle (see fit in Fig. 5b), consistent with the ob-servations by Kim and Wallington (2013). More aggressive drivingcycles, e.g. BPHC and BOHC (see Table A2 for cycle parameters) tend tohave higher FRVs and higher fuel consumption than milder drivingcycles, because acceleration is a key contributor to FRVs and fuelconsumption.

We now consider the influence of driving cycles on FRV and FRV+.As seen from inspection of Table 4, BPHC has the highest FRVs[(0.257 ± 0.007) L/(100 km 100 kg)] and +FRVs [(0.507 ± 0.058) L/(100 km 100 kg)] of all the cycles. In terms of FRV+, an averagelightweighted vehicle in this study has 19%, 39%, 61% greater fuelreduction benefits over the BPHC than over the CLTC, NEDC and WLTC.This difference reflects the different levels of congested driving in thesecycles. BPHC and BOHC have lower average speed than the NEDC andWLTC, with more stop-and-go driving. Driving cycles that require morepower demand overall have larger FRVs and FRVs+. The WLTC has anextra high-speed phase, requiring the highest power demand to over-come aerodynamic resistance, but this aerodynamic load is independentof vehicle weight and hence is not affected by vehicle lightweighting.

3.3. Cradle-to-grave GHG benefits

To illustrate the cradle-to-grave GHG mitigation benefits of AHSSand Al, we consider a case study of vehicle body lightweighting. Asshown in Table 5, replacing 50% of steel in the body (original weight475 kg (GREET, 2017) with AHSS is estimated to give 47.6 kg massreduction (median value adopted from Kelly et al. (2015).) Given thatAHSS has a GHG emissions intensity that is assumed to be the same asconventional steel (3.9 kg CO2eq/kg), this results in a

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47.6×3.9=185.6 kg/CO2eq reduction in cradle-to-gate GHG emis-sions per vehicle. Note that the reduction in AHSS GHG emissions in thematerial cycle is the result of a reduction of material use (47.6 kg).Aluminum has greater potential for mass reduction (107 kg), but theGHG emissions intensity is much higher (17.5 kg CO2eq/kg). This re-flects the high carbon intensity electricity currently used in the Chinesealuminum industry. Therefore, replacing steel with wrought Al results

in a net increase of material cycle GHG emissions (1358 kg/CO2eq pervehicle). In the future as the Chinese grid CO2 emissions intensity de-creases, the Al production burdens will decrease and lightweightingusing Al will become more attractive.

A complete picture of life cycle benefits must take into accountchanges in vehicle material manufacture and vehicle operation over itslifetime (200,000 km total mileage for light-duty vehicles in China

Fig. 4. Cradle-to-gate GHG emissions for aluminum and AHSS, t CO2eq/t product.

Fig. 5. Fuel reduction values a) with, and b) without, powertrain resizing versus fuel consumption for eleven vehicles and five driving cycles (BPHC, BOHC, CLTC,NEDC, and WLTC).

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(Zhou, 2016); sensitivity analysis for 100,000 km and 300,000 km isgiven in Appendix). We present the life cycle lightweighting GHGsavings in Fig. 6 and note that a generic gasoline vehicle has a cradle-to-grave GHG burden of 50.0 t CO2eq per vehicle (Qiao et al., 2019) as abaseline for comparison. Four points are evident from Fig. 6.

First, compared with conventional steel, AHSS has GHG savings inall scenarios considered, 0.7–1.0 and 1.1–1.7 t CO2eq per vehicle(1.4%–2.0% and 2.2%–3.4% of cradle-to-grave emissions for a genericgasoline vehicle) without, and with powertrain resizing. This is acombined result of decreased cradle-to-gate GHG emissions and miti-gation of on-road GHG emissions. Wrought Al, on the other hand, doesnot have cradle-to-gate GHG savings. Therefore, whether there will benet life cycle GHG savings relies on the amount of GHG savings duringthe use phase. Without powertrain resizing, use of wrought Al results inlower GHG emissions for the three China cycles (BPHC, BOHC, andCLTC) and the WLTC, but higher GHG emissions for the NEDC.

Second, AHSS is more consistent in achieving GHG savings in allcases, while Al has greater GHG savings in cases with high GHG re-duction during the use-phase but negative GHG savings in some othercases. Given the much higher GHG burden to produce Al than AHSS,wrought Al only outperforms AHSS when on-road fuel savings are high.For example, cases for congested driving cycles (BPHC and BOHC) withpowertrain resizing have GHG savings of 1.9–2.2 t CO2eq per vehicleusing aluminum, and 1.6–1.7 t CO2eq per vehicle using AHSS. Cases forlong lifetime driving distance (300,000 km, see Fig. A3) with power-train resizing have GHG savings of 1.9–3.9 t CO2eq per vehicle usingaluminum, and 1.6–2.5 t CO2eq per vehicle using AHSS.

Third, the two Beijing cycles (BPHC and BOHC) have the highestcradle-to-grave GHG savings, the CLTC has intermediate GHG savings,and NEDC and WLTC have the lowest cradle-to-grave GHG savings. Theresults indicate that congested traffic conditions make lightweighting aparticularly effective emission reduction strategy. BPHC and BOHChave lower average speed and more stop-and-go driving, requiringgreater mass-related drag force, i.e., acceleration resistance. Vehiclelightweighting has greater fuel consumption and emission reductionbenefits for the Beijing cycles than other cycles. This has not been re-ported in previous studies, which typically use type-approval fuelconsumption values and generic FRVs (e.g., Sun et al. (2019)).

Fourth, lightweighting with powertrain resizing is substantiallymore effective than that without powertrain resizing. AHSS andwrought Al have 1.1–1.7 and 0.8–2.2 t CO2eq/vehicle GHG reductionswith powertrain resizing, much higher than the cases without power-train resizing (0.7–1.0 and (−0.1)–0.4 t CO2eq/vehicle). This high-lights the importance of the physics-based estimation of FRV and FRV+

applied in this study which captures the impact of powertrain resizing.This emission reduction benefit is further enhanced in cases for two

Beijing cycles that have higher FRVs, and higher substitution ratio (seeFig. A4).

4. Conclusion

Life cycle GHG mitigation benefits of vehicle lightweighting usingAHSS and aluminum in China were evaluated. Production of AHSS and

Table 4Fuel reduction values without (FRV) and with (FRV+) powertrain resizing for eleven vehicles over the five driving cycles (mean ± standard deviation).

BPHC BOHC CLTC NEDC WLTC

FRV 0.257 ± 0.007 0.241 ± 0.008 0.227 ± 0.009 0.176 ± 0.011 0.210 ± 0.014FRV+ 0.507 ± 0.058 0.464 ± 0.054 0.425 ± 0.054 0.365 ± 0.047 0.314 ± 0.049

Table 5Weight and material-cycle GHG emissions impact of replacing steel in the vehicle body.

Metric Lightweight materialAHSS Wrought Al

Cradle-to-gate GHG, kg CO2eq/ kg 3.9 17.5Baseline mass, kg 475 (Conventional steel) 475 (Conventional steel)Lightweighted mass, kg 427.4= 189.9 (AHSS)+ 237.5 (Conventional steel) 368= 130.5 (W. Al)+ 237.5 (Conventional steel)Change in material-cycle GHG emissions, kg/ CO2eq per vehicle −185.6 1358

Note: Original weight of the steel in vehicle body is 475 kg (GREET, 2017). Replacement ratio of original material is set at 50%, since it is unlikely that all originalmaterial could be replaced. Replacement ratios of 20% and 80% are discussed in Appendix. Weight change values are taken from Kelly et al. (2015).

Fig. 6. Average cradle-to-grave GHG changes for vehicle lightweighting ofeleven vehicle models using AHSS (blue bars) or wrought Al (gray bars) with(open) or without (filled) powertrain resizing over five driving cycles. Errorbars are one standard deviation. The substituted material is steel in the vehiclebody with original mass of 475 kg. Lifetime distance is 200,000 km. A sensi-tivity analysis considering 100,000 km and 300,000 km is given in Fig. A3. Thereplacement ratio of original material is 50%, cases for 20% and 80% are givenin Fig. A4.

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wrought aluminum have average cradle-to-gate GHG emissions of 3.9and 17.5 kg CO2eq/kg. A physics-based model was used to evaluatelightweighting benefits for eleven passenger car models over fivedriving cycles (including real-world and regulatory cycles). Replacingsteel with AHSS results in GHG savings in all scenarios, 0.7–1.0 and1.1–1.7 t CO2eq per vehicle without, and with powertrain resizing.Despite the much higher GHG emissions associated with wrought Alproduction, its use results in life cycle GHG savings in most casesanalyzed except when driving distance is short (100,000 km per life-time) or driving conditions are mild. Maximum GHG savings occur withAl versus AHSS in cases where the powertrain is resized, travel iscongested, or lifetime travel distance is long. Beijing driving cycles(BPHC and BOHC) have the highest GHG savings, 1.9–2.2 t CO2eq pervehicle (aluminum, with powertrain resizing), compared to 0.8–1.6 tCO2eq per vehicle for other cycles, which implies that congested trafficconditions in Beijing make lightweighting a particularly effectiveemission reduction strategy. The results also highlight the difference inelectricity carbon intensity between China and the U.S. and its impacton the breakeven of lightweighting material production and vehicleoperation, which affects aluminum more significantly than steel. In thefuture, as the Chinese grid CO2 emissions intensity decreases, the alu-minum material production burdens will decrease in China and light-weighting using aluminum will become more attractive.

Acknowledgments

This study was supported by the National Key Research andDevelopment Program of China (2017YFC0212100) and the NationalNatural Science Foundation of China (91544222), and also partiallyfunded by Ford University Research Programs with the University ofMichigan and Ford University Research Programs with TsinghuaUniversity.

Appendix A. Supplementary data

Supplementary material related to this article can be found, in theonline version, at doi:https://doi.org/10.1016/j.resconrec.2019.104497.

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