Impact of Vehicle Performance on Cost Effective Way to Meet CAFE

Impact of Vehicle Performance on Cost Effective

Way to Meet CAFE 2017–2025 Ayman Moawad, Aymeric Rousseau Argonne National Laboratory [email protected]

Abstract - In September 2010, the U.S. Environmental Protection Four potential GHG targets have been analyzed, Agency (EPA) and the National Highway Traffic Safety representing a 3, 4, 5, and 6%/yr decrease in GHG levels Authority (NHTSA) released the Interim Joint Technical Assessment Report, Light-Duty Vehicle Greenhouse Gas from the MY 2016 fleet wide average of 250 g/mi. Thus, the Emission Standards and Corporate Average Fuel Economy MY 2025 targets analyzed range from 190 g/mi (equivalent to Standards for Model Years 2017–2025. The objective of the study 47 mpg) under the 3%/yr-reduction scenario to 143 g/mi is to determine the most cost-effective technology options to meet (equivalent to 62 mpg) under the 6%/yr–reduction scenario. the 3 and 6% fuel consumption improvements proposed for For purposes of an initial assessment, this range represents a different acceleration performances. To take into account uncertainties, three cases have been considered: low (90%), reasonably broad range of stringency increases for potential medium (50%), and high (10%). The low case represents future GHG emissions standards and is consistent with the “business as usual,” while the high case is based on targets from increases suggested by CARB in its letter of commitment in the U.S. DOE Vehicle Technologies Program. The study, based response to the President’s memorandum. on Argonne’s Autonomie vehicle simulation tool, demonstrates The TAR analysis is very preliminary and inconsistent with that improving vehicle performance benefits the introduction of electric drive vehicles. a number of legal requirements — as a result, the collaborating agencies stressed that the findings of their 3% and 6% scenarios could not be interpreted to represent I. INTRODUCTION findings about appropriate levels of standards, and that much more work would be done in the upcoming rulemaking to President Obama recently issued a presidential assess feasibility with the best inputs and in the contexts of memorandum urging improvement in fuel efficiency and EPA’s and NHTSA’s statutes. reduction of greenhouse gas emissions of light-duty vehicles The objective of this study is to define the most cost- for model years (MYs) 2017–2025. effective powertrain technology combinations to meet Two agencies (U.S. Environmental Protection Agency and potential CAFE requirements for 2017 and 2025. To do so, National Highway Traffic Safety Authority) have been the minimum (3%) and maximum (6%) cases will be working together and closely collaborating with the considered. California Air Resources Board (CARB) on behalf of the

Department of Transportation to propose a program for potential reduction in greenhouse gases (GHGs) and fuel II. CAFE 2017–2025 TARGETS economy [1]. As an initial assessment of the expected A. CO2 Targets technology costs and effectiveness, some rules have been Because CAFE is attribute-based and we cannot simulate proposed, wrapping up fuel economy and GHG emissions all the vehicle footprints at the level of detail considered, we standards for light-duty vehicles of MY 2017. will assume that all vehicles will achieve the same The following paragraph summarizes the Interim Joint improvements in fuel consumption. As a result, we will only Technical Assessment Report (TAR) the agencies released to consider a single vehicle class: midsize car. bring a new generation of clean vehicles. The average GHG level for midsize cars of the MY 2016

fleet is 230 g/mi (Table 1), which represents the CO2 value EPA, NHTSA, and CARB assessed more than 30 vehicle including the projected use of air conditioning (A/C) credits technologies that manufacturers could use to improve fuel by manufacturers. The power necessary to operate an A/C economy and reduce CO2 emissions of their vehicles during compressor places a significant additional load on the engine, MYs 2017–2025. The technologies considered fall into five thus reducing fuel economy and increasing CO2 tailpipe broad categories: emissions. Since CAFE does not include such credits (only • Engine technologies EPA does), 10 g/mi is added to its analysis, resulting in a • Transmission technologies 240-g/mi GHG level projected for the MY 2016 fleet • Vehicle technologies (including mass reduction) (midsize car). The fuel economies shown in Table 1 are • Electrification/accessory technologies referred as ―EPA MPG,‖ while the fuel economy without • Hybrid/vehicle electrification technologies credits can be called ―CAFE MPG.‖ In the remainder of the report, all the numbers are ―CAFE MPG.‖

Table 1 - MY 2016 CO2 and Fuel Economy Targets for Various According to Table 4, we can assume the current 2010 Vehicle Types, including credits (Table I.B.3 from [2]) CAFE standard associated with a midsize car (footprint 46 ft2) would be set to 27.5 mpg. Example CO2 Fuel As shown in Table 5, improvements in fuel consumption of Vehicle Example model emissions Economy 28.1% and 30.3% are needed for Scenarios A and B, Type Models footprint target target 2 respectively, by 2017, and fuel consumption improvements of (ft ) (g/mi) (mpg) 43.5% and 57.6% are needed for Scenarios A and B, Example Passenger Cars respectively, by 2025 Compact Honda 40 206 41.1 Car Fit Table 5 – Fuel Consumption (l/100 km) and Fuel Economy Midsize Ford (mpg) improvements from 2010 to 2017–2025 46 230 37.1 Car Fusion Fullsize Chrysler Scenario A Scenario B 53 263 32.6 Car 300 2010 CAFE (mpg) 27.5 2017/2025 CAFE 38.3/48.9 39.5/64.9 (mpg) Table 2 shows the possible CO2 values for a midsize car for the four scenarios selected to reach the CAFE goal. Only two Improvement (%) 39.1/77.5 43.6/135.5 2010 CAFE cases were analyzed in this study: the worst-case estimation 8.5 (Scenario A: 3%/yr CO2 decrease) and the best-case (L/100 km) 2017/2025 CAFE estimation (Scenario B: 6%/yr CO2 decrease). 6.1/4.8 5.9/3.6 (L/100 km) Table 2 - 2017–2025 CO2 Scenarios Improvement (%) -28.1/-43.5 -30.3/-57.6

Year CO2 (g/mi) 2016 240 III. METHODOLOGY 3% 4% 5% 6% This section assesses the fuel economy potential and cost 2017 232.8 230.4 228.0 225.6 of several component and powertrain technologies that would 2025 182.4 166.2 151.3 137.5 support both Scenarios A and B — 28.1% and 30.3% fuel consumption improvement — compared with current B. 2017/2025 Fuel Economy/Fuel Consumption Equivalent technologies (MY 2010). The simulations were performed Target with Autonomie by using drive cycles and calculations similar to NHTSA’s: Table 3 - 2017 CAFE MPG for Two scenarios A and B • Two drive cycles (UDDS and HWFET) • Cycle weighting of 55/45 2017/2025 Scenario A: 3% Scenario B: 6% • Unadjusted fuel consumption CO (g/mi) 232.8/182.5 225.6/137.5 2 The plug-in hybrid electric vehicle (PHEV) fuel economies Fuel Economy 38.3/48.9 39.5/64.9 (mpg) were defined on the basis of the SAE J1711 standard testing Fuel Consumption procedure by using the NHTSA utility factors. 6.13/4.8 5.94/3.62 (L/100 km) The following powertrain configurations were considered, on the basis of existing and planned vehicles: The attribute-based target was introduced for CAFE • Conventional beginning with MY 2011 fleet vehicles, so there is no • Full hybrid electric vehicles (HEVs) footprint-based regulation before that year. Therefore, it • PHEVs — Power-split technology was considered would be difficult to simulate the current (2010) CAFE for 10- and 20-mi all-electric range (AER) standard on the basis of a midsize car only. The 2010 applications, whereas series technology was used for unadjusted fuel economy value of a typical midsize car 30- and 40-mile AER. (Ford Fusion) has been used as our reference (see Table 4). The potential of each technology was assessed by using a three-point technical uncertainty analysis (low, medium, and Table 4 - 2010 CAFE MPG Reference for a midsize car high). Each vehicle’s characteristics (e.g., mass, power, energy) was designed to meet the following vehicle technical Ford Fusion 2010 EPA EPA specifications (VTS): (MPG) (UDDS) (HWFET) • 0–60 mph < 8 s/9 s/10 s/11 s Adjusted 18 27 • Maximum vehicle speed > 100 mph Unadjusted 22.5 37.7 • 6% grade at 65 mph at gross vehicle weight (GVW) Combined without support from energy storage 27.5 Unadjusted

Two different sets of assumptions were used to simulate Vehicle performance has a greater impact on fuel saved for vehicles for 2017 and 2025. The component performance conventional and hybrid vehicles than for PHEVs. More fuel assumptions are based on state-of-the-art data collected by is saved for conventional vehicles and HEVs with lower Argonne, as well as automotive companies. All costs vehicle performance. represent manufacturing cost. The costs for the main PHEV10 components are defined in Table 6 and Table 7. 4.00

3.50 Table 6 – Battery Cost Assumptions 3.00 2.50 8sec 2015 2020 Parameter Current 2.00 9sec Low Med High Low Med High 1.50 10sec High Power APPLICATIONS 1.00 11sec Cost ($/kW) 45 45 30 25 40 27 22

FUel Consumption(l/100km) 0.50 High Energy APPLICATIONS Power Term 0.00 ($/kW) 32 25 22 20 25 22 0 Low Medium High Energy Term ($/kWh) 600 380 270 250 250 175 160 Figure 3 - Impact of PHEV10 vehicle performance on fuel consumption Table 7 - Electric Machine Cost Assumptions PHEV40 2015 2020 Parameter Current 2.50 Low Med High Low Med High Cost Controller 12 9 5.75 5 7.5 5.375 4.5 2.00 ($/kW) 8sec Cost Motor ($/kW) 13 11 8 7 10 7.5 5.5 1.50 9sec 1.00 10sec IV. AUTONOMIE FUEL ECONOMY/FUEL CONSUMPTION AND 11sec MANUFACTURING COST SIMULATION RESULTS - 0.50 ASSUMPTIONS FUel Consumption(l/100km) 0.00 Figure 1, Figure 2, Figure 3 and Figure 4 show the impact Low Medium High of the different vehicle powertrain performance on the fuel Figure 4 - Impact of PHEV40 vehicle performance on fuel consumption evolution. consumption Conventional The impact of vehicle performance decreases with higher 8.00 hybridization degree. The requirements to follow the UDDS 7.00 in electric-only mode combined with the requirement that the 6.00 engine meet gradeability leads to a high-performance vehicle. 5.00 8sec As a result, modifying the performance requirement for 4.00 9sec PHEVs from 11 to 8 s does not modify the fuel consumption. 3.00 10sec 2.00 11sec A. 8-seconds acceleration sizing

FUel FUel Consumption(l/100km) 1.00 The following tables summarize manufacturing costs ($) 0.00 for 4 different vehicle acceleration performances: 8 s, 9 s, Low Medium High 10 s, and 11 s. Figure 1 - Impact of conventional vehicle performance on fuel

consumption Reference conventional 2010 combined unadjusted FE: 31.2 mpg ― 7.55 L/100 km Full HEV 6.00 Table 8 - Manufacturing Cost for 8-s Acceleration Performance 5.00 Drivetrain Configuration Low Medium High 4.00 8sec GASOLINE 3.00 9sec 10sec 2.00 Conventional 14961 13967 14030 11sec Full HEV 18259 18193 17824 1.00

FUel FUel Consumption(l/100km) PHEV 10 19930 19632 19202 0.00 PHEV 20 21102 20699 20007 Low Medium High PHEV 30 24509 23768 22103 Figure 2 - Impact of HEV vehicle performance on fuel PHEV40 26906 25899 23664 consumption

question is to calculate updated goals on the basis of the B. 9-seconds acceleration sizing assumptions used in Autonomie. It is important to focus on the percentage increases to obtain useful comparisons Reference 2010 combined unadjusted FE: 32.9 mpg ― between real-world and simulation results. Autonomie 7.1 L/100 km simulation results have different fuel economy values. We need to achieve improvements of 39.1% and 43.6% in 2017 Table 9 - Manufacturing Cost for 9-s acceleration performance and 77.5% and 135.5% in 2025, respectively, for scenario A and scenario B on the basis of these simulation results, Drivetrain Configuration Low Medium High regardless of the initial fuel economy. Table 12 shows the new CAFE-targets equivalent on the basis of Autonomie GASOLINE simulation results. Conventional 13813 13744 13790 Full HEV 17531 17225 16915 Table 12 - Autonomie-equivalent CAFE targets PHEV 10 18882 18626 18315 PHEV 20 19668 19276 18823 Initial Autonomie FE result 32.9 mgp PHEV 30 22523 21813 20526 2017 Scenario A (39.1%) 45.8 mpg PHEV40 24423 23504 21687 2017 Scenario B (43.6%) 47.2 mpg 2025 Scenario A (77.54%) 58.4 mpg C. 10-seconds acceleration sizing 2025 Scenario A (135.56%) 77.5 mpg

Reference 2010 combined unadjusted FE: 34 mpg ― VI. OPTIMAL COMBINATION METHOD 6.92 L/100 km To define the most cost-effective way to meet CAFE, we Table 10 - Manufacturing Cost for 10-s acceleration need to find the best combination of technologies with the performance lowest manufacturing cost to reach fuel economy of 44.5 mpg and 45.9 mpg. The problem is equivalent to maximizing fuel Drivetrain Configuration Low Medium High economy over minimizing cost. GASOLINE Our analysis assumes that in 2017, fleet vehicles on the market will consist of six powertrain technologies: Conventional 13730 13668 13715 conventional, HEV, PHEV 10, PHEV 20, PHEV 30, and Full HEV 17234 16952 16688 PHEV 10 18753 18502 18208 PHEV 40. Each technology is translated to an X variable in PHEV 20 19553 19174 18734 the following equations. PHEV 30 22523 21813 20526 Constraint number 1: PHEV40 24423 23504 21687

( ) ∑ D. 11-seconds acceleration sizing

The mean Fuel Economy of these cars is the objective of Reference 2010 combined unadjusted FE: 34.98 mpg ― CAFE2017 and CAFE2025: 6.72 L/100 km Scenario A:

Table 11 - Manufacturing Cost for 8-s acceleration performance Scenario A:

Drivetrain Configuration Low Medium High Scenario B:

GASOLINE Scenario B: Conventional 13656 13600 13660 Constraint number 2: Full HEV 17001 16753 16485 PHEV 10 18660 18425 18135

PHEV 20 19449 19098 18660 ∑ PHEV 30 22523 21813 20526 PHEV40 24423 23504 21687

( ) ∑

V. RECALCULATION OF CAFE TARGETS BASED ON AUTONOMIE RESULTS Constraint number 3:

What is the most cost-effective way to meet CAFE? Since the ( ) results from Autonomie are not based on the 2010 Ford Fusion reference values, the first step in answering this

The Cost function that need to be minimized, where MEDIUM-TECHNOLOGICAL-UNCERTAINTY CASE

represents the cost of each vehicle 100% technology: 90%

80% ( ) ∑ 70% 60%

50% PHEV20 Several methods could be used to solve that minimization 40% Conv problem since it is a linear problem: using the Simplex 30% method or Lagrange multipliers or applying the Karush- 20% Kuhn-Tucker conditions. The last method seems to be the percentage Breakdown Vehicle 10% 0% most appropriate choice since one of the constraints is an 8 sec 9 sec 10 sec 11 sec inequality constraint. Solutions might only be local solutions. Figure 6 - Vehicle breakdown for medium-technology case: 2017 However, using an adapted numerical optimum algorithm Scenario A based on these theories to achieve results is discussed in the next section. The high-technological-uncertainty case illustrated in Figure 7 demonstrates that if one achieves significant advances in technology, conventional cars alone can meet the CAFE VII. RESULTS target. A. 2017 CAFE TARGET HIGH-TECHNOLOGICAL-UNCERTAINTY CASE a. Scenario A (3%) 100% Figure 5 shows the vehicle breakdown for the low- 90% technology case. Overall, the percentage of conventional 80% drivetrains increases with vehicle performance time sizing. 70% The introduction of PHEVs for the 8-s and 9-s cases creates a 60% 50% PHEV20 discontinuity. This is due to the low FE/cost ratio. The slower 40% Conv the car, the more efficient it is, and so the proportion of 30% electric vehicles in the optimal breakdown is lower. For the 20% 8-s and 9-s cases, HEVs are completely absent, giving percentage Breakdown Vehicle 10% 0% priority to more than 35% to 40% of PHEV 20. 8 sec 9 sec 10 sec 11 sec

Figure 7 - Vehicle breakdown for medium-technological case: LOW-TECHNOLOGICAL-UNCERTAINTY CASE 2017 Scenario A 100% 90% Figure 8 shows that optimal cost decreases with performance. 80% In fact, the slower the vehicle, the less powerful it is, and so 70% its components cost less. 60% PHEV20 50% HEV Vehicle performance vs. optimal cost 40% Conv 20000 30% 18000 20% 16000 Vehicle Breakdown percentage Breakdown Vehicle 10% 14000 0% 8 sec 9 sec 10 sec 11 sec 12000 Low 10000 Figure 5 – Vehicle breakdown for low-technology case: 2017 Med 8000 Scenario A high

6000 ManufacturingCost ($) The medium-technology case (Figure 6) shows that the 4000 percentage of conventional drivetrains increases when vehicle 2000 performance decreases. The 8-s case has less than 25% of 0 PHEV 20, whereas all of the vehicles are conventional cars 8 sec 9 sec 10 sec 11 sec for the 11-s case. Figure 8 - Impact of vehicle performance on optimal cost: 2017 Scenario A

b. Scenario B (6%) The scenario B confirms the trend. In that case, the higher fuel-consumption target changes the distribution of the technologies, but the trend remains the same. Figure 9 shows that the share of electric drive vehicles required to meet the

5 target increases. For the 8-s case, the share of PHEV 20 Vehicle performance vs. optimal cost increases from 40 to 45%. 20000 LOW-TECHNOLOGICAL-UNCERTAINTY CASE 18000 16000 100% 14000 90% 12000 80% Low 10000 Med 70% 8000 high 60%

PHEV20 6000 ManufacturingCost ($) 50% HEV 4000 40% 2000 Conv 30% 0 8 sec 9 sec 10 sec 11 sec 20%

Vehicle Breakdown percentage Breakdown Vehicle 10% Figure 12 - Impact of vehicle performance on optimal cost: 2017 0% Scenario B 8 sec 9 sec 10 sec 11 sec Figure 9 - Vehicle breakdown for low-technological case: 2017 B. 2025 CAFE TARGET Scenario B a. Scenario A (3%) As we discuss in this section, the target in this case is more Figure 10 shows that by comparing the last scenario, an aggressive, which means help from alternative hybrid exception can be noted for the medium-technological- configurations is needed to reach the goal. For this reason, uncertainty case, in which the 11-s vehicle has a small PHEVS 20 (and especially PHEVs) proportions are higher percentage of PHEV 20 introduced (4.5%). than in 2017. Note that although HEVs were present in MEDIUM-TECHNOLOGICAL-UNCERTAINTY CASE previous results for the low case, the case for 2025 centers the 100% breakdown between conventional cars and PHEVs (PHEV20 90% in that case), except for the high case. Thus, in 2025, when 80% the CAFE MPG target would be very high, PHEVs would be 70% more likely to be in the market, with PHEV 20 being the best 60% candidate. PHEV20 50% 40% Conv 30% LOW-TECHNOLOGICAL-UNCERTAINTY CASE

20% 100% Vehicle Breakdown percentage BreakdownVehicle 10% 90% 0% 80% 8 sec 9 sec 10 sec 11 sec 70% Figure 10 - Vehicle breakdown for medium-technological case: 60% 2017 Scenario B 50% PHEV20 40% Conv The high-technological case remains the same because only 30% conventional vehicles are shown in Figure 11. 20% Vehicle Breakdown percentage Breakdown Vehicle 10% HIGH-TECHNOLOGICAL-UNCERTAINTY CASE 0% 100% 8 sec 9 sec 10 sec 11 sec 90% Figure 13 - Vehicle breakdown for low-technological case: 2025 80% Scenario A 70% 60% In general, comparing 2025 with 2017, shows that the amount PHEV20 50% of PHEV20 almost doubled for low and medium cases. 40% Conv 30% MEDIUM-TECHNOLOGICAL-UNCERTAINTY CASE 20% 100%

Vehicle Breakdown percentage Breakdown Vehicle 10% 90% 0% 80% 8 sec 9 sec 10 sec 11 sec 70% Figure 11 - Vehicle breakdown for high-technological case: 2017 60% Scenario B 50% PHEV20 40% Conv Figure 12 shows that optimal cost decreases with 30% 20%

performance, as in the previous scenario. The main difference percentage Breakdown Vehicle 10% would be in cost values as optimal cost has slightly increased 0% as a result of the higher amount of PHEVs needed on the 8 sec 9 sec 10 sec 11 sec market. Figure 14 - Vehicle breakdown for med-technological case: 2025 Scenario A

Figure 15, which shows the high-technological uncertainty MEDIUM-TECHNOLOGICAL-UNCERTAINTY CASE case, first note the introduction of PHEVs in the breakdown. 100% Also note that in the 11-s case, about 6% of the breakdown is 90% HEV cars. Overall, electric vehicles are needed for the high- 80% technological case, in contrast to 2017. 70% 60% HIGH-TECHNOLOGICAL-UNCERTAINTY CASE 50% PHEV20 100% 40% Conv 90% 30% 80% 20%

70% percentage Breakdown Vehicle 10% 60% PHEV20 0% 50% 8 sec 9 sec 10 sec 11 sec HEV 40% Conv Figure 18 - Vehicle breakdown for medium-technological case: 30% 2025 Scenario B

20% Vehicle Breakdown percentage Breakdown Vehicle 10% Figure 19 shows that compared to the scenario A, the 0% 8 sec 9 sec 10 sec 11 sec proportion of PHEV 20 more than doubles. In addition, the

Figure 15 - Vehicle breakdown for high-technological case: 2025 percentage of HEVs increases from 5% to 82% of the Scenario A breakdown for the 11-s case. HIGH-TECHNOLOGICAL-UNCERTAINTY CASE Figure 16 shows that optimal cost decreases with 100% performance as in the previous scenarios and year. The trend 90% is the same, but as the PHEV20 percentage is increased, 80% optimal cost increases, as well with higher targets. 70% 60% PHEV20 Vehicle performance vs. optimal cost 50% HEV 20000 40% Conv 18000 30% 16000 20%

14000 percentage Breakdown Vehicle 10% 12000 Low 0% 10000 Med 8 sec 9 sec 10 sec 11 sec 8000 high Figure 19 - Vehicle breakdown for high-technological case: 2025

6000 ManufacturingCost ($) 4000 Scenario B 2000 0 Figure 20 shows that optimal cost decreases with 8 sec 9 sec 10 sec 11 sec performance. However, in 2025, the introduction of a higher Figure 16 - Impact of vehicle performance on optimal cost: 2025 percentage of alternative vehicle technologies will lead to Scenario A higher costs, regardless of the technological uncertainty case forecasted. b. Scenario B (6%) Figure 17 shows that more than 85% of the breakdown is composed of PHEV20. Vehicle performance vs. optimal cost 20000 LOW-TECHNOLOGICAL-UNCERTAINTY CASE 100% 19000 90% 18000 80% Low 70% 17000 Med 60% high 16000

50% PHEV20 ManufacturingCost ($)

40% Conv 15000 30% 20% 14000 8 sec 9 sec 10 sec 11 sec Vehicle Breakdown percentage Breakdown Vehicle 10% 0% Figure 20 - Impact of vehicle performance on optimal cost: 2025 8 sec 9 sec 10 sec 11 sec Scenario B Figure 17 - Vehicle breakdown for low-technological case: 2025 Scenario B

Figure 18 shows that around 70–75% of the breakdowns are PHEV20.

CONCLUSIONS AUTHORS A process has been developed to evaluate the most cost- Ayman Moawad effective technologies to meet CAFE requirements under Research Engineer different timeframe and vehicle performance scenarios. The Argonne National Laboratory, study demonstrated that: 9700 S. Cass Avenue, Argonne, IL 60439, USA - Electric drive penetration could be increased by Tel: +1-630-252-2849 increasing vehicle performance (i.e., 0–60 mph). Email: [email protected] - Increased vehicle performance leads to additional manufacturing cost and impacts the overall choices Ayman Moawad is a research engineer in the Vehicle Modeling and Simulation in powertrain. Section at Argonne National Laboratory. - Significant advances related to conventional vehicle He graduated from the Ecole des Mines de technologies (i.e., engine, transmission, Nantes, France, in 2009 with a Master’s lightweighting) would delay the need for electric Degree in Science, majoring in Automatics, drive, as demonstrated by the high-uncertainty Control Systems, and Industrial Computer Science. He scenario. focuses his research on light-duty vehicle fuel-consumption - In 2017, conventional vehicles can meet the analysis, as well as powertrain costs to support the Government Performance and Results Act. requirements for the high-technology case for both

3% or 6% scenarios, while the low-technology case requires hybrid electric vehicles (HEVs) and also Aymeric Rousseau plug-in HEVs (PHEV20). The medium-technology Program Manager case requires from 0% to 26% of PHEV20, Argonne National Laboratory, depending on vehicle performance and scenario. 9700 S. Cass Avenue, - By 2025, a significant number of electric drive Argonne, IL 60439, USA Tel: +1-630-252-7261 vehicles could be necessary (up to 85%) to meet the Email: [email protected] most aggressive CAFE target. Aymeric Rousseau is the Manager of the Vehicle Modeling The results from this paper depend on the assumptions and Simulation Section at Argonne National Laboratory. He selected (i.e., requirements, efficiencies, cost) and should not received his engineering diploma at the Industrial System be generalized. In addition, this study was based on a single Engineering School in La Rochelle, France, in 1997. After vehicle class (midsize car), whereas CAFE values are based working for PSA Peugeot Citroen in the Hybrid Electric on entire fleets. Because each vehicle class is impacted Vehicle research department, he joined Argonne National differently by technologies, additional vehicle classes should Laboratory in 1999, where he is now responsible for the be considered to refine the results. development of Autonomie. He received an R&D100 Award in 2004 and a 2010 Vehicle Technologies Program R&D The combination of the technology improvements leads to Award in 2010. He has authored more than 40 technical significant fuel consumption and cost reduction across light- papers in the area of advanced vehicle technologies. duty vehicle applications. Because of the uncertainty associated with the evolution of the technologies considered, research should continue to be conducted in areas showing high-fuel-displacement potential.

REFERENCES [1] EPA, NHTSA, CARB, and Interim Joint Technical Assessment Report standards; Light-Duty Vehicle Greenhouse Gas Emission Standards; and Corporate Average Fuel Economy Standards for Model Years 2017–2025, September 2010. [2] EPA, DOT, Part II, Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards; Final Rule, Friday May 7, 2010.