9
Application of Vehicle Technologies to Vehicle Classes

INTRODUCTION

In conducting its assessment of technology applicability to different vehicle classes, the committee was guided by the following question included in the statement of task: “What are the estimated cost and potential fuel economy benefits of technology that could be applied to improve fuel economy of future passenger vehicles, given the constraints imposed by vehicle performance, functionality, and safety and emission regulations?” Note that applying technology to improve fuel economy and reduce fuel consumption should not be interpreted to mean simply attaching a component or subsystem that then achieves a subsequent reduction in fuel consumption. Such reductions in fuel consumption typically evolve through an incremental, evolutionary application of components, subsystems, and new power train or vehicle technologies.

Previous chapters of this report have provided technical summaries of current and advanced technologies that are currently being applied to vehicles, or developed for future vehicle applications. Other reports from the National Research Council (NRC) have also looked at the impacts of technologies for reducing fuel consumption—Appendix H provides a summary of other recent NRC studies related to light-duty vehicle technologies. Many of these technologies could, in principle, be applied to almost any vehicle. How ever, the intended use of the vehicle, its price range, consumer characteristics, emissions and safety standards compliance, and packaging constraints influence which technologies will see market penetration on different vehicle types.

Many of the technologies have already seen significant penetration into European or Asian markets where regulatory and market pressure, including significant taxation that results in high fuel prices for consumers, have encouraged early adoption. Others, such as turbocharged, direct-injection gasoline engines, have gained significant attention in the United States because fuel consumption can be reduced with minimal redesign of the total vehicle system.

DEVELOPING BASELINE VEHICLE CLASSES

The concept of dividing U.S. passenger vehicles into so-called classes is both an outcome of regulatory segmentation for the purpose of varying standards and a means whereby vehicle sales categories are differentiated by vehicle size, geometry, and intended use. The NRC CAFE report segmented U.S. passenger vehicle sales into 10 classes that were a subset of the larger number of type and weight classes that the U.S. EPA uses as part of its vehicle certification process (NRC, 2002). These 10 classes are as follows:

  1. Small SUV,

  2. Medium SUV,

  3. Large SUV,

  4. Minivans,

  5. Small pickups,

  6. Large pickups,

  7. Subcompact cars,

  8. Compact cars,

  9. Midsize cars, and

  10. Large cars.

The statement of task directs the current committee to evaluate these vehicle classes and update the technology outlook for future model introduction. However, shifts in consumer preference and vehicle sales have been significantly influenced by the recent instability in fuel prices, vehicle financing costs, U.S. and global economic conditions, and regulatory uncertainty. Significant shifts in vehicle sales between 2002 and 2007 showed a continuing increase in SUV sales, with sales of small pickups essentially disappearing (EPA, 2008a). However, in 2008, large increases in fuel price (above $4 per gallon of gasoline) resulted in a greater than 50 percent reduction in the sale of midsize and large SUVs. Subsequent U.S. and global instability in the financial markets, followed by a period of recession, has resulted in an overall reduction of vehicle sales in the United States of more than 20 percent from 2008 to 2009.



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9 Application of Vehicle Technologies to Vehicle Classes INTRODUCTION DEVELOPING BASELINE VEHICLE CLASSES In conducting its assessment of technology applicability The concept of dividing U.S. passenger vehicles into so- to different vehicle classes, the committee was guided by called classes is both an outcome of regulatory segmentation the following question included in the statement of task: for the purpose of varying standards and a means whereby “What are the estimated cost and potential fuel economy vehicle sales categories are differentiated by vehicle size, benefits of technology that could be applied to improve fuel geometry, and intended use. The NRC CAFE report seg- economy of future passenger vehicles, given the constraints mented U.S. passenger vehicle sales into 10 classes that were imposed by vehicle performance, functionality, and safety a subset of the larger number of type and weight classes that and emission regulations?” Note that applying technology to the U.S. EPA uses as part of its vehicle certification process improve fuel economy and reduce fuel consumption should (NRC, 2002). These 10 classes are as follows: not be interpreted to mean simply attaching a component or subsystem that then achieves a subsequent reduction in fuel 1. Small SUV, consumption. Such reductions in fuel consumption typically 2. Medium SUV, evolve through an incremental, evolutionary application of 3. Large SUV, components, subsystems, and new power train or vehicle 4. Minivans, technologies. 5. Small pickups, Previous chapters of this report have provided technical 6. Large pickups, summaries of current and advanced technologies that are cur- 7. Subcompact cars, rently being applied to vehicles, or developed for future ve- 8. Compact cars, hicle applications. Other reports from the National Research 9. Midsize cars, and Council (NRC) have also looked at the impacts of technolo- 10. Large cars. gies for reducing fuel consumption—Appendix H provides a summary of other recent NRC studies related to light-duty The statement of task directs the current committee to vehicle technologies. Many of these technologies could, in evaluate these vehicle classes and update the technology principle, be applied to almost any vehicle. However, the outlook for future model introduction. However, shifts in intended use of the vehicle, its price range, consumer char- consumer preference and vehicle sales have been signifi- acteristics, emissions and safety standards compliance, and cantly influenced by the recent instability in fuel prices, ve - packaging constraints influence which technologies will see hicle financing costs, U.S. and global economic conditions, market penetration on different vehicle types. and regulatory uncertainty. Significant shifts in vehicle sales Many of the technologies have already seen significant between 2002 and 2007 showed a continuing increase in penetration into European or Asian markets where regula- SUV sales, with sales of small pickups essentially disappear- tory and market pressure, including significant taxation that ing (EPA, 2008a). However, in 2008, large increases in fuel results in high fuel prices for consumers, have encouraged price (above $4 per gallon of gasoline) resulted in a greater early adoption. Others, such as turbocharged, direct-injection than 50 percent reduction in the sale of midsize and large gasoline engines, have gained significant attention in the SUVs. Subsequent U.S. and global instability in the financial United States because fuel consumption can be reduced with markets, followed by a period of recession, has resulted in minimal redesign of the total vehicle system. an overall reduction of vehicle sales in the United States of more than 20 percent from 2008 to 2009. 138

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139 APPLICATION OF VEHICLE TECHNOLOGIES TO VEHICLE CLASSES Therefore, the choice of vehicle classes for future consid- of under 0.055 including crossover vehicles, SUVs, eration as part of this assessment of potential fuel economy and minivans. Most vehicles employ front-wheel technologies should focus on vehicle size, weight, inte- drive. The average 2007 model-year vehicle for this rior passenger volume, intended use, and the potential for class was developed from EPA (2008a) and has the implementation of next-generation power trains, including following characteristics: a six-cylinder, four-valve, hybrid electrics. Based on various factors outlined below, the dual overhead cam engine with intake cam phasing following classification of light-duty vehicles in the United and a 6-speed automatic transmission. The average States was determined by the committee to be an appropriate vehicle for this class is used as the base vehicle in the basis for future technology development and introduction estimation of fuel consumption reductions for multiple into production. technologies as discussed later in this chapter. 6. Unit-body high-performance trucks—Crossover vehi- 1. Two-seater convertibles and coupes—Small vehicles cles, SUVs, and minivans with hp/lb of vehicle weight by interior volume whose function is high-performance ratios of 0.055 or greater. Most have rear-wheel drive and handling. The average 2007 model-year vehicle for (RWD) or all-wheel drive (AWD) and unibody con- this class was developed from EPA (2008a) and has the struction, and most are luxury vehicles. The average following characteristics: a six-cylinder, four-valve, 2007 model-year vehicle for this class was developed dual overhead cam engine with intake cam phasing from EPA (2008a) and has the following characteris- and a 6-speed automatic transmission. The average tics: a six-cylinder, four-valve, dual overhead cam en- vehicle for this class is used as the base vehicle in the gine with intake cam phasing and a 6-speed automatic estimation of fuel consumption reductions for multiple transmission. The average vehicle for this class is used technologies as discussed later in this chapter. as the base vehicle in the estimation of fuel consump- 2. Small cars—Mini-, sub-, and compact cars, standard tion reductions for multiple technologies as discussed performance, mostly four-cylinder, mostly front-wheel later in this chapter. drive (FWD), including small station wagons. The 7. Body-on-frame small and midsize trucks—Pickups less average 2007 model-year vehicle for this class was de- than or equal to 1,500 lb payload capacity (CEC class veloped from EPA (2008a) and has the following char- 14) and SUVs of up to 175 cubic feet of passenger acteristics: a four-cylinder, four-valve, dual overhead volume plus cargo volume with RWD or AWD. The cam engine with intake cam phasing and a 6-speed average 2007 model-year vehicle for this class was automatic transmission. The average vehicle for this developed from EPA (2008a) and has the following class is used as the base vehicle in the estimation of characteristics: a six-cylinder, two-valve, single over- fuel consumption reductions for multiple technologies head cam engine with a 5-speed automatic transmis- as discussed later in this chapter. sion. The average vehicle for this class is used as the 3. Intermediate and large cars—Standard performance, base vehicle in the estimation of fuel consumption mostly FWD, mostly six-cylinder, including large reductions for multiple technologies as discussed later station wagons with less than 0.07 hp/lb of vehicle in this chapter. weight. The average 2007 model-year vehicle for this 8. Body-on-frame large trucks—Pickups of greater than class was developed from EPA (2008a) and has the 1,500 lb payload but less than 10,000 lb GVW, and following characteristics: a six-cylinder, four-valve, SUVs with 175 cubic feet or greater of passenger dual overhead cam engine with intake cam phasing plus cargo volume with RWD or AWD, including all and a 4-speed automatic transmission. The average standard vans, cargo and passenger. The average 2007 vehicle for this class is used as the base vehicle in the model-year vehicle for this class was developed from estimation of fuel consumption reductions for multiple EPA (2008a) and has the following characteristics: technologies as discussed later in this chapter. an eight-cylinder, two-valve, overhead valve engine 4. H igh-performance sedans —Passenger cars with with a 4-speed automatic transmission. The average greater than or equal to 0.07 hp/lb of vehicle weight vehicle for this class is used as the base vehicle in the that are not two-seaters. The average 2007 model-year estimation of fuel consumption reductions for multiple vehicle for this class was developed from EPA (2008a) technologies as discussed later in this chapter. and has the following characteristics: a six-cylinder, four-valve, dual overhead cam engine with intake cam These eight classes allow an evaluation of similar base phasing and a 6-speed automatic transmission. The vehicles designs, where the vehicle size, baseline chassis average vehicle for this class is used as the base vehicle configuration, aerodynamic characteristics, vehicle weight in the estimation of fuel consumption reductions for and type of drive train (FWD, RWD, and AWD) are similar. multiple technologies as discussed later in this chapter. This grouping should result in vehicle classes where similar 5. Unit-body standard trucks—Non-pickup trucks with calibration criteria are associated with similar vehicle per- unibody construction and hp/lb of vehicle weight ratios formance characteristics. A greater number of classes would

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140 ASSESSMENT OF FUEL ECONOMY TECHNOLOGIES FOR LIGHT-DUTY VEHICLES also be possible if there was a desire to narrow the variability been considered proprietary by automobile manufacturers in vehicle characteristics in each class. (referred to as original equipment manufacturers; OEMs) and suppliers, such that only those companies associated with the design, development, and production of such systems have ESTIMATION OF FUEL CONSUMPTION BENEFITS had the data to conduct such analyses. However, partnerships Incremental reductions in fuel consumption through currently exist between the automotive industry and the U.S. t he application of technologies were estimated by the government such that more complete experimental data will committee. As discussed earlier in this report, input came be made available in the future. from many sources including component suppliers, vehicle Another factor in successfully modeling full vehicle sys- manufacturers, and the review of many published analyses tems is the need to understand and capture the tradeoffs that conducted by, or for, the U.S. Department of Transportation OEMs must make in developing final production calibrations National Highway Traffic Safety Administration (NHTSA), of vehicles and their power trains. Calibration is the process U.S. Environmental Protection Agency (EPA), California of power train and vehicle performance optimization that Air Resources Board (CARB), and other agencies or trade focuses on achieving predetermined performance, drivabil- associations. The committee also contracted with several ity, fuel consumption, durability, fuel octane sensitivity, and consultants to provide input. many other parameters while still complying with statutory Relative reductions in fuel consumption can result from requirements such as those for levels of emissions, onboard several factors, many of which are interrelated: diagnostics (OBD), and safety standards. In particular, many potential technologies that can be applied for improving fuel • Reduction in the tractive force needed to propel the consumption could influence performance parameters such vehicle (reduced rolling resistance, aerodynamic drag, as 0-60 mph acceleration times, vehicle passing capabil- vehicle weight, etc.); ity, towing capability, transmission shift quality, or noise • Improvement in the energy conversion efficiency of and vibration characteristics. Different manufacturers must the fuel in the engine into maximum usable energy thus determine their customer-preferred compromises and through increased thermal efficiency (compression calibrate the vehicle control algorithms accordingly. Based ratio increase for gasoline engines, lean combustion, on the number of potential parameters that may be varied in diesel, etc.); modern passenger car engines, tens of thousands of combina- • Reductions in the engine and power train energy losses tions are possible. Therefore, manufacturers and calibration that consume portions of the available energy before service companies have developed optimization strategies and after combustion (gas exchange losses, power train and algorithms to fine-tune these variables while achieving friction, accessory losses, etc.); an OEM’s criteria for performance and drivability within the • Optimization of operational parameters that allow constraints of emissions, fuel economy, and other standards. the engine to run in regions of highest efficiency Calibration logic is normally a highly confidential process (increased number of transmission gears, CVTs, im- that requires the experience of companies involved in the proved lugging characteristics, aggressive shift logic, production release of vehicles (OEMs, Tier 1 suppliers, etc.); and production engineering services companies, etc.) to accu- • Some form of hybridization that allows other forms of rately assess the necessary performance, fuel consumption energy capture, storage, and management to reduce the and exhaust emission, and drivability tradeoffs for accurate total energy consumed over the driving cycle. modeling. Partial discrete approximation (PDA) and lumped The committee thinks that the most accurate method of parameter modeling techniques, as described in Chapter 8, analyzing potential reductions in fuel consumption, which examine and estimate incremental reduction in fuel con- considers the extent to which any of the efficiency improve- sumption associated with applications of discrete technolo - ments or energy loss reductions identified above can be gies or subsystems and their effect on reducing energy losses. realized while maintaining energy balance criteria, utilizes They represent a more time- and cost-effective method of full system simulation (FSS). This analysis technique, as estimating potential reductions in fuel consumption and described in Chapter 8, represents the state of the art in pre- may incorporate routines that attempt to tabulate or account dicting vehicle performance, fuel consumption, direct CO2 for aggregation of energy-loss reductions that focus on fluid emissions, and other regulated and non-regulated emissions. mechanical losses, frictional losses, and heat transfer losses. However, FSS analyses require detailed vehicle, engine, However, the ultimate accuracy of such analyses relies on a transmission, accessory, and other subsystem data, typically sufficiently broad set of empirical or system-simulation data expressed in the form of data maps that quantify power, that do not necessarily provide enough detail to understand torque, fuel consumption, and exhaust emissions over the the base test vehicle distribution of energy losses. Calibra- complete range of operation. Historically, such data (which tion of such models against actual test vehicles provides a may not yet exist for the most advanced technologies) have benchmark of the modeler’s attempt to match performance

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141 APPLICATION OF VEHICLE TECHNOLOGIES TO VEHICLE CLASSES data, but does not provide the same level of explanation of modeled vehicles or power trains against which to refine the the subsystem contribution to total vehicle energy losses that estimates. This variability in estimates for fuel consumption is accomplished in the FSS cases. Furthermore, the influ- reductions also reflects the fact that different OEMs may ences of variations in calibration strategies owing to such obtain different benefits from the same technology due to factors as driver comfort; noise, vibration, and harshness differences in implementation and calibration. Also, posi- (NVH)-related issues; and performance/emissions tradeoffs tive benefits may vary depending on the particular engine/ are typically not considered in such analyses. transmission/vehicle architecture. These factors have been With either modeling approach, it is imperative to under- considered by the committee in its range of estimates or its stand the role that any previously applied technologies play decision to include or exclude the potential application of in reducing energy losses and/or improving the thermo- technologies into different technology paths. Note that the dynamic efficiency of the power train. ranges associated with these technologies do not reflect the possibility that, over time, the average fuel consumption benefit could tend toward the high end of the range as the APPLICABILITY OF TECHNOLOGIES TO VEHICLE lessons learned from the best examples of the technology CLASSES spread across the industry and as the impacts of higher CAFE Not all of the technologies identified in Chapters 4 standards increase. Although the committee recognizes that through 7 of this report can be justifiably applied to all ve- the implementation of these technologies with fuel consump- hicle types. Applicability of the technologies to the various tion benefits at the higher end of the ranges could occur, it is vehicle classes requires an analysis of parametric tradeoffs difficult to assert that this will occur or to what degree this which considers functionality, intended use, impact on war- would impact the average consumption benefit over time. ranty, ease of implementation, product cycle timing, market The issue of how multiple technologies might interact demand, cost-effectiveness, and many other factors. Some when used to reduce fuel consumption is critical. FSS technologies may be discounted for technical reasons, for analyses conducted by Ricardo, Inc., for the EPA and for example, the limitations of continuously variable transmis- the committee shed some light on the issue of synergistic sions (CVTs) in transmitting high torque on vehicle classes interaction of multiple technologies that may attempt to re- with larger engines where towing or off-road capability is duce energy losses of a similar type, such as pumping losses a primary product feature. Others may be excluded based (Ricardo, Inc., 2008, 2009). These analyses show the need on the intended purpose of the vehicle. For example, low- to carefully understand the contribution of technologies in rolling-resistance tires appear to be a cost-effective means of reducing losses whose impact may be only a 1 to 2 percent reducing fuel consumption, potentially justifying their use reduction in fuel consumption. The Ricardo, Inc., analyses on all vehicles. However, in higher performance classes of also show that the type of vehicle and power train influences vehicles, where tire grip is an important product feature or the extent to which different technologies reduce fuel con- for SUV applications where the vehicle may travel off-road, sumption, especially between vehicles of different classes the use of such tires is likely restricted. with different intended uses. This effect is discussed in Table 9.1 shows the committee’s estimation of incremen- Chapter 8, where the primary effect of synergies was shown tal reductions in fuel consumption that may be expected from to dominate the potential for improvement. Accordingly, the application of different technologies and ranges associ- secondary effects of influences that interact across technol- ated with the reductions. In general, the committee estimated ogy improvement paths were found to be minor. what it considered to be the average fuel consumption re- duction for a technology before it attempted to estimate the ESTIMATING INCREMENTAL COSTS ASSOCIATED range. These data, shown in the form of ranges, are in some WITH TECHNOLOGY EVOLUTION cases dependent upon the level of technology applied to a ve- hicle before the next increment is taken. As identified above, Chapter 3 describes the methodologies used for the esti- these data represent estimates by the committee developed mation of incremental costs associated with the introduction from evaluating published data, and analyses conducted by of advanced technology for reducing fuel consumption. A the NHTSA, the EPA, and others. Appendix I contains results range of estimated costs was also prepared and is outlined in from some of these other studies, although the reader should the technology sections presented in Chapters 4 through 7. refer to the original source for the assumptions and study Table 9.2 shows the collection of these cost estimates for all approaches used in these other studies. The expert judgment technologies included in this report. The cost estimates rep- of members of the committee whose careers have focused resent estimates for the current (2009/2010) time period to on vehicle and power train design and development were about 5 years in the future. As with the data on fuel consump- also incorporated in the estimates. Examination of the data tion reductions, incremental cost information was provided in Table 9.1 suggests that significant variations in estimates to the committee by OEMs, Tier 1 suppliers, and studies pub- of the potential for reducing fuel consumption are due to the lished by trade associations, governmental agencies, manu- lack of detailed simulation data on actual or theoretically facturing consultants, and earlier NRC reports. Appendix I

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142 ASSESSMENT OF FUEL ECONOMY TECHNOLOGIES FOR LIGHT-DUTY VEHICLES TABLE 9.1 Committee’s Estimates of Effectiveness (shown as a percentage) of Near-Term Technologies in Reducing Vehicle Fuel Consumption Incremental values - A preceding technology must be included Technologies I4 V6 V8 Spark Ignition Techs Abbreviation Low High A VG Low High AVG Low High AVG 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 Low Friction Lubricants LUB 1.3 1.3 1.5 0.5 2.0 0.5 2.0 1.0 2.0 Engine Friction Reduction EFR 2.3 2.5 3.0 1.5 3.0 1.5 3.5 2.0 4.0 VVT- Coupled Cam Phasing (CCP), SOHC CCP 2.3 2.3 2.5 1.5 3.0 1.5 3.0 2.0 3.0 Discrete V ariable Valve Lift (DVVL), SOHC DVVL 5.0 7.5 NA NA NA 4.0 6.0 5.0 10.0 Cylinder Deactivation, SOHC DEAC 1.5 1.5 1.8 1.0 2.0 1.0 2.0 1.5 2.0 VVT - In take Cam Phasing (ICP) ICP 2.0 2.3 2.3 1.5 2.5 1.5 3.0 1.5 3.0 VVT - Dual Cam Phasing (DCP) DCP 2.3 2.5 3.0 1.5 3.0 1.5 3.5 2.0 4.0 Discrete V ariable Valve Lift (DVVL), DOHC DVVL 4.8 5.0 5.3 3.5 6.0 3.5 6.5 4.0 6.5 Continuously Variable V alve Lift (CVVL) CVVL 5.0 7.5 NA NA NA 4.0 6.0 5.0 10.0 Cylinder Deactivation, OHV DEAC 2.3 2.5 3.0 1.5 3.0 1.5 3.5 2.0 4.0 VVT - Coupled Cam Phasing (CCP), OHV CCP 2.0 2.3 2.5 1.5 2.5 1.5 3.0 2.0 3.0 Discrete V ariable Valve Lift (DVVL), OHV DVVL 2.3 2.3 2.3 1.5 3.0 1.5 3.0 1.5 3.0 Stoichiometric Gasoline Direct Injection (GDI) SGDI Turbocharging and Downsizing TRBDS 3.5 5.0 5.0 2.0 5.0 4.0 6.0 4.0 6.0 Diesel Techs 25.0 25.0 15.0 35.0 15.0 35.0 NA NA NA Conversion to Diesel DSL 10.0 10.0 30.0 Conversion to Advanced Diesel ADSL 7.0 13.0 7.0 13.0 22.0 38.0 Electrification/Accessory Techs 2.0 2.0 2.0 Electric Power Steering (EPS) EPS 1.0 3.0 1.0 3.0 1.0 3.0 1.0 1.0 1.0 0.5 1.5 0.5 1.5 0.5 1.5 Improved Accessories IACC Higher Voltage/Improved Alternator HVIA 0.3 0.3 0.3 0.0 0.5 0.0 0.5 0.0 0.5 Transmission Techs 4.0 4.0 4.0 1.0 7.0 1.0 7.0 1.0 7.0 Continuously Variable Transmission (CVT) CVT 2.5 2.5 2.5 2.0 3.0 2.0 3.0 2.0 3.0 5-spd Auto. Trans. w/ Improved Internals 1.5 1.5 1.5 1.0 2.0 1.0 2.0 1.0 2.0 6-spd Auto. Trans. w/ Improved Internals 2.0 2.0 2.0 2.0 2.0 2.0 7-spd Auto. Trans. w/ Improved Internals 1.0 1.0 1.0 1.0 1.0 1.0 8-spd Auto. Trans. w/ Improved Internals 5.5 5.5 5.5 3.0 8.0 3.0 8.0 3.0 8.0 6/7/8-spd Auto. Trans. w/ Improved Internals NAUTO 7.5 7.5 7.5 6.0 9.0 6.0 9.0 6.0 9.0 6/7-spd DCT from 4-spd A T DCT 6/7-spd DCT from 6-spd A T DCT 3.5 3.5 3.5 3.0 4.0 3.0 4.0 3.0 4.0 Hybrid Techs 3.0 3.0 3.0 12V BAS Micro-Hybrid MHEV 2.0 4.0 2.0 4.0 2.0 4.0 34.0 34.0 34.0 Integrated Starter Generator ISG 29.0 39.0 29.0 39.0 29.0 39.0 37.0 37.0 37.0 Power Split Hybrid PSHEV 24.0 50.0 24.0 50.0 24.0 50.0 35.0 35.0 35.0 2-Mode Hybrid 2MHEV 25.0 45.0 25.0 45.0 25.0 45.0 Plug-in hybrid PHEV NA NA NA NA NA NA NA NA NA Vehicle Techs 0.3 0.3 0.3 0.3 0.3 0.3 Mass Reduction - 1% MR1 1.4 1.4 1.4 1.4 1.4 1.4 Mass Reduction - 2% MR2 3.3 3.3 3.3 Mass Reduction - 5% MR5 3.0 3.5 3.0 3.5 3.0 3.5 6.5 6.5 6.5 Mass Reduction - 10% MR10 6.0 7.0 6.0 7.0 6.0 7.0 12.0 12.0 12.0 Mass Reduction - 20% MR20 11.0 13.0 11.0 13.0 11.0 13.0 2.0 2.0 2.0 Low Rolling Resistance Tires ROLL 1.0 3.0 1.0 3.0 1.0 3.0 1.0 1.0 1.0 1.0 Low Drag Brakes LDB 1.0 1.0 Aero Drag Reduction 10% AERO 1.5 1.5 1.5 1.0 2.0 1.0 2.0 1.0 2.0 NOTE: Some of the benefits (highlighted in green) are incremental to those obtained with preceding technologies shown in the technology pathways described in Chapter 9. contains results from some of these other studies, although, term, high-volume production, whereby analysts attempt to again, the reader should refer to the original source for the normalize all incremental costs into a scenario where the assumptions and study approaches used in these other stud- capitalized development cost becomes a small portion of the ies. During the data gathering process, it became clear that final unit production cost. This is accomplished by assuming the estimated incremental cost ranges were, in many cases, that production volumes are several hundred thousand units very large, depending on the boundary conditions identified per year and remain in production for many years. by the organization offering the information. Generally, the Although this assumption may be quite appropriate to committee notes that cost estimates are always more uncer- normalize overall annual societal costs, it does not necessar- tain than the fuel consumption impact estimates, and the ily recognize the initial development-based costs and quality estimates presented here should be considered very uncer- hurdles that may prevent a manufacturer from pursuing new tain until more detailed studies are completed. A boundary product or technology areas. For example, such analyses condition in the cost estimations is an assumption of long- would not consider factors that may inhibit or prevent the

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TABLE 9.2 Committee’s Estimates of Technology Costs in U.S. Dollars (2008) NR C 2009 Costs Incremental Values - A preceding technology must be included Technologies I4 V6 V8 AVG w/1.5 AVG w/1.5 AVG w/1.5 Low High AVG Low High AVG Low High AVG RPE RPE RPE Spark Ignition Techs Abbreviation Low Friction Lubricants LUB 4 4 3 5 3 5 3 5 4 6 6 6 Engine Friction Reduction EFR 48 78 64 104 42 63 63 94.5 84 126 32.0 52.0 VVT- Coupled Cam Phasing (CCP), SOHC CCP 35 70 70 35 52.5 70 105 70 105 Discrete Variable Valve Lift (DVVL), SOHC DVVL 130 160 180 210 280 320 145 217.5 195 292.5 300 450 Cylinder Deactivation, SOHC DEAC NA NA 340 400 357 420 NA NA 370 555 388.5 582.75 70 70 VVT - In take Cam Phasing (ICP) ICP 35 70 70 35 52.5 105 105 VVT - Dual Cam Phasing (DCP) DCP 35 70 70 70 70 35 52.5 105 105 Discrete Variable Valve Lift (DVVL), DOHC DVVL 180 220 260 300 145 217.5 200 300 280 420 130 160 Continuously Variable Valve Lift (CVVL) CVVL 290 310 350 390 182 273 300 450 370 555 159 205 Cylinder Deactivation, OHV DEAC NA NA 220 250 255 NA NA 235 352.5 255 382.5 VVT - Coupled Cam Phasing (CCP), OHV CCP 35 35 35 35 52.5 35 52.5 35 52.5 225 300 Discrete Variable Valve Lift (DVVL), OHV DVVL 130 160 210 240 280 320 145 218 338 450 Stoichiometric Gasoline Direct Injection (GDI) SGDI 213 323 117 195 169 256 295 351 156 234 319 485 Turbocharging and Downsizing TRBDS -144 205 525 790 31 658 370 490 430 645 46 986 Diesel T echs 3174 NA NA Conversion to Diesel DSL 2154 2632 2857 3491 NA NA 2393 3590 4761 Conversion to Advanced Diesel ADSL 520 520 683 683 3513 4293 683 3903 520 780 1025 5855 Electrification/Accessory Techs Electric Power Steering (EPS) EPS 70 120 70 120 70 120 95 143 95 143 95 143 80 80 80 Improved Accessories IACC 70 90 70 90 70 90 120 120 120 APPLICATION OF VEHICLE TECHNOLOGIES TO VEHICLE CLASSES Higher Voltage/Improved Alternator HVIA 35 35 35 15 55 15 55 15 55 53 53 53 Transmission Techs Continuously Variable Transmission (CVT) CVT 150 170 243 263 243 263 160 240 253 380 253 380 5-spd Auto. T rans. w/ Improved Internals 133 133 133 133 200 133 200 133 200 174 262 174 262 174 262 6-spd Auto. T rans. w/ Improved Internals 133 215 133 215 133 215 7-spd Auto. T rans. w/ Improved Internals 170 300 235 353 235 353 235 353 170 300 170 300 8-spd Auto. T rans. w/ Improved Internals 425 425 425 425 638 425 638 425 638 6/7/8-Speed Auto. T rans. with Improved Inter nals NAUT O 137 425 137 425 281 422 281 422 281 422 137 425 6/7- spd DCT from 6-spd AT DCT -147 185 -147 185 -147 185 19 29 19 29 19 29 6/7- spd DCT from 4-spd AT DCT 193 193 -14 400 -14 400 193 290 290 290 -14 400 Hybrid Techs 12V BAS Micro-Hybrid MHEV 450 550 585 715 720 880 500 665 650 865 800 1064 Integrated Starter Gener ator ISG 1760 2640 2000 3000 3200 4800 2200 2926 2500 3325 4000 5320 Power Split Hybrid PSHEV 2708 4062 3120 4680 4000 6000 3385 4502 3900 5187 5000 6650 6500 8645 6500 8645 6500 8645 2-Mode Hybrid 2MHEV 5200 7800 5200 7800 5200 7800 Series PHEV 40 PHEV 8000 12000 9600 14400 13600 20400 10000 13300 12000 15960 17000 22610 Vehicle T echs Mass Reduction - 1% MR1 37 45 48 58 68 82 41 61 53 80 75 1 13 85 111 156 Mass Reduction - 2% MR2 77 93 100 121 142 170 127 166 234 Mass Reduction - 5% MR5 239 311 439 217 260 283 339 399 479 358 467 659 Mass Reduction - 10% MR10 572 747 1054 520 624 679 815 958 1 150 859 1 120 1581 Mass Reduction - 20% MR20 1650 2475 1700 2550 1750 2625 1600 1700 1600 1800 1600 1900 Low Rolling Resistance T ires ROLL 30 40 30 40 35 53 35 53 35 53 30 40 Aero Drag Reduction 10% AERO 45 45 40 50 40 50 45 68 68 68 40 50 143

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144 ASSESSMENT OF FUEL ECONOMY TECHNOLOGIES FOR LIGHT-DUTY VEHICLES introduction of diesel technology into passenger vehicles due (Auto Alliance), used the DOE-supported VEHSIM model to the significant investment and general lack of familiarity to estimate fuel consumption reduction for various tech- of North American automotive OEMs and suppliers in the nologies using a composite of engine maps provided by production of small, light-duty diesels and the durability of manufacturers. Although the committee recommends a more necessary exhaust aftertreatment systems. This serves as a practical approach to apply FSS for future regulatory actions, reminder that, while overall costs to the industry of new tech- which is discussed later in this chapter, the exclusive use of nologies is an important consideration, it is the individual FSS simulation for the assessment of all technologies con- manufacturers that bear the risk in adapting a technology to sidered under this study was not possible. The committee be- a specific vehicle and this risk may not be fully captured in lieves that sufficient experimental data can be gathered by the a metric of overall industry costs. government to support future analysis and regulatory activi- The committee was briefed on the very detailed and ties through consortia that include both regulatory agencies transparent teardown cost assessment methodology being and automotive manufacturers and suppliers. utilized by the EPA as part of the process for estimating the With this background, the committee evaluated potential cost of fuel economy technologies. Cost estimation using technology paths that could be considered by a manufacturer, the teardown approach is discussed in Chapter 3. The com- depending upon the manufacturer’s actual state of technol- mittee finds this approach an improvement over one where ogy and production capability. Rather than creating decision cost estimates are developed through expert knowledge and trees from which an extremely large number of possible tech- surveys of suppliers and OEMs, which have been the basis nology combinations could be created for each vehicle class, for most published studies and the majority of this report. the committee estimated possible technology evolution paths Furthermore, the committee recommends that the use of for each class that develop from the average baseline vehicle. teardown studies be expanded for future assessments when These pathways are summarized in Figures 9.1 to 9.5. cost-effectiveness is an important evaluation criterion. The baseline attributes were determined on a class-by- class basis using the 2007 EPA test list. If 51 percent of the vehicles in a given class had variable valve timing (VVT), ASSESSING POTENTIAL TECHNOLOGY SEQUENCING then the baseline, class-representative vehicle was given PATHS VVT, and this technology would not be added in the path. When manufacturers consider a strategy for implement- Because the characteristic vehicle in each class represents the ing technologies that reduce fuel consumption, a normal average attributes for that class, there will be some vehicles business decision process must tradeoff many different in that class that have more or less technology content. The parameters, including cost-effectiveness (fuel consumption below-average vehicles may require additional technologies reduction versus production cost), the ability to be integrated and associated costs to address future standards while the into product planning cycles, intended product use, reli- above-average vehicles may not. Using the average attributes ability, impact on vehicle performance characteristics, and should provide a good overall representation of technology customer acceptance. To conduct the current assessment, the benefits relative to the baseline fleet within a class of ve- committee employed a method whereby cost-effectiveness hicles. The technologies of the baseline vehicles are listed (fuel consumption reduction divided by high-volume pro- in the title bar of each technology path. duction incremental cost), vehicle intended use, base power In the absence of a very large number of FSS analyses, but train configuration, and technology state of readiness were guided by a limited number of FSS runs performed for the considered in estimating potential technology paths for the committee by Ricardo, Inc. (2009), the committee evaluated eight vehicle classes described earlier. possible sequences of technology implementation for differ- As previously stated, an attempt to perform FSS on every ent classes of vehicles. The development of the technology vehicle model with all combinations of technologies is not sequences shown in Figures 9.1 to 9.5 also was done with practicable. Such a process would necessitate the analysis of input from OEMs, Tier 1 suppliers, other published analyses, (at least) tens of thousands of vehicle and power train technol- and the expert judgment of committee members. In develop- ogy combinations. It would require potentially confidential ing the ranges of fuel consumption reduction, the committee engine, transmission, accessory, and hybrid power train sys- recognized that the potential reduction for each incremental tem maps; vehicle data such as friction as a function of vehicle step is highly dependent on the extent to which system losses speed; aerodynamic parameters; and many others parameters could have been reduced by previous technologies. These that are either proprietary or would require significant vehicle pathways attempt to include such factors as cost-effectiveness testing to generate for all of the combinations that are possible. (percent fuel consumption reduction/incremental cost), logical In some published studies, OEMs have supported such sequencing based upon preexisting technology, technical limi- analyses for a limited number of vehicles that were chosen tations, and ease of implementation (requirements for major as sufficiently representative for discussion of the technology or minor manufacturing changes, including production line benefits and associated costs. For example, Sierra Research, considerations). Subjective judgment by the committee also in its report for the Alliance of Automobile Manufacturers played a role in the pathway definition process.

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145 APPLICATION OF VEHICLE TECHNOLOGIES TO VEHICLE CLASSES *Item may be replaced by subsequent technology **Not included in totals FIGURE 9.1 Small-car pathways with estimated total fuel consumption reduction and cost shown. Figure 9-1.eps bitmap

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146 ASSESSMENT OF FUEL ECONOMY TECHNOLOGIES FOR LIGHT-DUTY VEHICLES *Item may be replaced by subsequent technology **Not included in totals Figure 9-2.eps FIGURE 9.2 Intermediate- and large-car and unit-body standard truck pathways with estimated total fuel consumption reduction and cost shown. bitmap

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147 APPLICATION OF VEHICLE TECHNOLOGIES TO VEHICLE CLASSES FIGURE 9.3 Two-seater convertible and coupe, high-performance sedan, and unit-body performance truck pathways with estimated total fuel consumption reduction and cost shown. Figure 9-3.eps bitmap

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148 ASSESSMENT OF FUEL ECONOMY TECHNOLOGIES FOR LIGHT-DUTY VEHICLES FIGURE 9.4 Body-on-frame small-truck pathways with estimated total fuel consumption reduction and cost shown. Figure 9.4 bitmapped

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149 APPLICATION OF VEHICLE TECHNOLOGIES TO VEHICLE CLASSES Figure 9-5.eps FIGURE 9.5 Large-truck pathways with estimated total fuel consumption reduction and cost shown. bitmap

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150 ASSESSMENT OF FUEL ECONOMY TECHNOLOGIES FOR LIGHT-DUTY VEHICLES Although the committee believes that some potential re- though prepared in response to the committee’s statement of duction is possible with each of the technologies considered, task, these data are approximate in nature and as such should the extent to which a system energy loss can be reduced is not be used as input to analyses where modeling accuracy highly dependent on all of the system interactive effects, the is important. They are provided here as rough estimates that extent to which the baseline technology package has already can be used in a qualitative comparative sense when compar- reduced different categories of energy losses, and the produc- ing the relative cost-benefits of spark-ignition (SI)-related tion calibration parameters chosen by each manufacturer for technologies that are potential candidates for FSS analyses. the final release of each vehicle. Evaluating the energy losses The committee’s estimates can also be used for a qualitative associated with these technology pathways is discussed later comparison of SI-related technologies to other candidates in this chapter. such as light-duty diesel or hybrid vehicles. Review of Figures 9.1 through 9.5 shows that in certain The results show that significant reductions in fuel con- cases (intermediate and large cars; unit-body standard trucks; sumption are possible with technologies that are already in two-seater convertibles, coupes, and high-performance sedans; production in U.S., European, or Asian markets. For exam- unit-body performance trucks) the technology pathways are ple, for the intermediate car, large car, and unibody standard the same because of the similar base vehicle power train. truck classes, the average reduction in fuel consumption for However, the tradeoffs made as a result of varying perfor- the SI path is 29 percent at a cost of approximately $2,200; mance metrics as these vehicle types go through their product the average reduction for the compression-ignition (CI) en- evolution would result in different levels of fuel consumption gine path is 38 percent at a cost of approximately $5,900; and improvement depending on the specific vehicle application. the average reduction for the hybrids path is about 44 per- Each range in potential fuel consumption reduction is an cent at an average cost of approximately $6,000. In general, attempt by the committee to estimate the potential variation diesel engine and hybrid vehicle technology options offer in energy loss reduction that might be possible when ap- greater improvement potential compared to the SI pathway, plying the technology to different power train and vehicle but at a higher incremental cost. However, as evidenced by packages, taking into consideration known system features the increasingly wide range in estimated fuel consumption that will likely be optimized for different classes of vehicles reduction and incremental cost, actual fuel consumption im - with different intended uses. An example would be the bias provement can vary significantly depending on an individual inherent in production calibration of light-duty trucks or manufacturer’s product strategy. Further, it may be that the SUVs where reasonable towing capability is required. needs to reduce vehicle fuel consumption as mandated by A simple, multiplicative aggregation of the potential recent legislation will result in OEMs implementing these fuel consumption reduction is presented below each path technologies in such a way that the benefits fall toward the in Figures 9.1 through 9.5 as a means to roughly estimate high end of the range. It should be noted that among its provi- the total potential that might be possible. A probabilistic sions related to fuel economy, the Energy Independence and methodology based on the mean square rule was applied Security Act (EISA) of 2007 required periodic assessments to estimate the confidence intervals for the aggregation of by the NRC of automobile vehicle fuel economy technolo- fuel consumption improvements and costs. Appendix J pro- gies and their costs. Thus, follow-on NRC committees will be vides the mathematical explanation for this methodology. responsible for responding to the EISA mandates, including It assumes that the confidence intervals on each individual the periodic evaluation of costs and fuel consumption ben- estimate of technology effectiveness or cost are the same. It efits of individual technologies and the combined impacts of also assumes that ranges in estimates are independent of each multiple technologies. other and that errors are normally distributed. The approach When developing the effectiveness numbers, attempts then maintains a confidence interval for the aggregation of were made by the committee to incrementally adjust the the low or high ends of the estimates that is equal to the con- effectiveness numbers of certain technologies that would fidence intervals estimated for the individual technologies. normally be preceded by another technology. This process The committee assumes that the ranges for the individual allowed the committee to approximate the inclusion of the costs and effectiveness represent a 90 percent confidence synergistic effects resulting from the combination of certain level. As such, the ranges were increased in technical areas technologies that were deemed to usually be packaged to- where, in the opinion of the committee, more uncertainty gether. In an attempt to evaluate the incremental effective- existed with initial estimates. ness numbers for the SI pathway derived by the committee, It should also be noted that when the combination of comparisons were conducted using the FSS data from the fuel consumption improvement predictions and associated Ricardo report prepared for the committee (Ricardo, Inc., incremental costs is considered, the probability drops to 2009), the EPA-provided lumped parameter model, and vari- 81 percent that any actual production technology introduc- ous other SAE papers where combinations of technologies tion would fall within the ranges bounded by both the fuel were assessed. A comparison to the Ricardo data is shown consumption and cost ranges. This reduction is due to the in Figure 9.6. Packages involving CVTs were excluded be- (multiplicative) product of two 90 percent probabilities. Al- cause the committee agrees with the EPA (EPA, 2008b) that

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151 APPLICATION OF VEHICLE TECHNOLOGIES TO VEHICLE CLASSES 45 Ricardo 40 NRC 35 Fuel-Consumption Reduction % 30 25 20 15 10 5 0 Std Car Z Std Car 2 Lg Car 6a Lg Car 4 Lg Car 5 Lg Car 16 Sm MPV 2 Sm MPV Z Truck 12 Sm MPV 5 Sm MPV 15 Lg MPV 4 Truck 9 Truck 10 Truck 17 Lg MPV 6b Lg MPV 16 Truck 11 Package FIGURE 9.6 NRC estimates of effectiveness in reducing the fuel consumption of various light-duty vehicles compared with Ricardo, Inc. (2009) estimates based on data obtained with full system simulation. Figure 9-6.eps x-axis labels tion in energy losses, despite consideration given to the total retyped Ricardo, Inc., used an abnormally small fuel consumption effectiveness value for this type of transmission. system energy consumers. Therefore, as another check on As can be seen in Figure 9.6, the packages’ fuel con- the predicted aggregation of potential technologies, the com- sumption reduction results generally follow the relative mittee contracted with EEA to apply its lumped parameter comparisons between the packages analyzed by Ricardo, modeling approach to evaluate the committee’s estimates. Inc. This is likely due to the engineering judgment of the Simplified lumped parameter models of vehicle energy use members of the committee whose experience in power train (e.g., Sovran and Bohn, 1981) provide a means of evaluating engineering could be applied to the assessment. However, whether the fuel consumption benefits estimate for combina- the absolute levels of potential improvement can vary sig- tions of technologies by the multiplication methods result nificantly between the committee estimates and Ricardo from forcing categories of energy losses (pumping and fric- analyses. Furthermore, a comparison of the step-by-step tion) to physically impossible levels. Appendix K provides incremental estimates that would result from the application a description of the EEA lumped parameter model as well of single technologies was not conducted. Therefore, it is not as a description of the results in terms of the tractive energy possible to determine whether the demonstrated correlations requirements and the engine efficiency for the SI and diesel were a result of accurate incremental estimates, or whether a test cycles. These results indicate that the results from the combination of over- and underestimates resulted in a rough multiplication method used here likely do not greatly over- approximation, where such occurs. state the benefits because this method does not explicitly take In any case, the Ricardo, Inc., packages represent only a into account the theoretical limits of pumping loss reduction. subset of the greater number of technology combinations that Figures 9.7 and 9.8 show the model results versus the would result from proceeding down the entire pathway evalu- committee estimates for eight cases (four for SI paths, and ated by the committee. This underscores the importance of four for diesel paths). The model estimates for incremental using FSS to account for the larger number of technology syn- improvements are relatively close to those of the committee, ergies and ensure that system loss reduction is not overstated. with the committee’s estimates generally exceeding those of Due to the approximate nature of the estimates of incre- the EEA model by a small amount. These comparisons are mental improvements in fuel consumption, the committee made between the average level of the committee’s estimates recognizes the potential to overestimate the potential reduc- and the EEA data with no range presented. It should also be

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152 ASSESSMENT OF FUEL ECONOMY TECHNOLOGIES FOR LIGHT-DUTY VEHICLES NRC Estimate 35.0 Lumped Parameter Estimate 30.0 Fuel-Consumption Reduction % 25.0 20.0 15.0 10.0 5.0 0.0 Small Cars Intermediate & Lg BOF-Small Trucks BOF-Large Trucks Cars FIGURE 9.7 NRC estimates of effectiveness in reducing fuel consumption in spark-ignition engine pathways compared to EEA model outputs. Figure 9-7.eps NRC Estimate 45.0 Lumped Parameter Estimate 40.0 Fuel-Consumption Reduction % 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0 Small Cars Intermediate & Lg BOF-Small Trucks BOF-Large Trucks Cars Figure 9-8.eps FIGURE 9.8 NRC estimates of effectiveness in reducing fuel consumption in diesel engine pathways compared to EEA model outputs. noted that a baseline 4-speed automatic was used for both the potential levels of energy loss reduction are employed in committee’s and EEA estimates because these comparisons both the EEA lumped parameter approach and the expert were conducted prior to the committee’s decision to utilize opinion of the committee members. The EEA model does the average class transmission from the 2007 EPA test data employ an algorithm to account for incremental reductions in the technology paths. of energy losses, as predicted by an industry-derived set of One might conclude that the EEA modeling does, in fact, equations (see Chapter 8). Therefore, it is not surprising that suggest that the committee’s estimates slightly overpredict the estimations are relatively close. the estimate. However, the same general method of com- However, the applications of the EEA’s or the committee’s parison with known production vehicles and estimating the estimation approach is done without a detailed understanding

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153 APPLICATION OF VEHICLE TECHNOLOGIES TO VEHICLE CLASSES of the actual levels of thermal efficiency of a subject vehicle’s quantify, at least for the vehicle model being evaluated, the engine, the influence of combustion chamber design on the interactive or synergistic effects that result. These may be fuel conversion efficiency, the actual levels of gas exchange positive or negative synergies, as outlined in the Ricardo or frictional losses, and all of the other parameters for which report prepared for this committee (Ricardo, Inc., 2009) and additional technologies can be applied to reduce fuel con - discussed in Chapter 8 of this report. An example of these sumption. This is only possible through a combination of synergistic effects is presented in Table 9.3. experimental and analytical analyses, which are necessary Table 9.3 shows that the total improvement in fuel con- to predict the absolute level of fuel consumption. sumption is gained from a combination of primary benefits Stated another way, in the opinion of the committee, attributed to a technology pair and a synergistic benefit (or neither the lumped parameter approaches evaluated by the detriment) as a result of the energy losses that are targeted for committee nor the committee’s aggregated estimates define reduction. If one considers the engine and transmission com- the actual level of energy efficiencies and/or losses of a bination, benefits in reduced pumping losses occur if a down- randomly chosen vehicle with sufficient accuracy to allow sized, higher-specific-power engine is applied. Additional accurate predictions of future technology introductions. benefits can be gained from a more efficient transmission Furthermore, this inaccuracy further degrades as an increas- with reduced hydraulic losses or reduced friction. However, ing number of technologies is employed. Therefore, the when these two are applied, there are additional benefits that committee believes that a physics-based, FSS, in combina- arise from the ability to run the engine at a lower operating tion with experimentally generated data, is required for such speed for a given power level, thereby increasing the brake predictions, especially if technology that is not currently in mean effective pressure in the cylinders and further reduc- production is considered. ing the pumping losses. This contributes to the 2.17 percent improvement outlined in Table 9.3. However, it is important to note that the absolute level and relative levels of improve- IMPROVEMENTS TO MODELING OF MULTIPLE FUEL ment outlined in Table 9.3 may vary significantly, depend- ECONOMY TECHNOLOGIES ing on the application of the same technology sequence to The application of FSS, in which the engine load, thermo- another vehicle application. dynamic efficiency, operational losses of energy, and acces- As evidenced by the Ricardo, Inc., FSS analyses con- sory loads are varied as a function of vehicle operational ducted for the committee, different vehicle types, with performance, offers the best opportunity to evaluate the differing intended uses, demonstrate different optimization- effectiveness of incremental application of vehicle systems of-performance characteristics. Therefore, when attempting in reducing vehicle energy losses, thereby improving overall to estimate the incremental benefits from the application operational cycle efficiency and reducing fuel consumption. of discrete technologies, the vehicle class, intended use, However, since different technologies may be attempting to and associated performance metrics must be considered. reduce the same type of loss, for instance, pumping loss, it Furthermore, the positive or negative synergistic effects of is necessary to evaluate the contribution of each incremen- multiple vehicle energy-loss-reducing technologies will vary tal technology in reducing the different losses in each step depending on the vehicle class and intended performance. along a potential product improvement path. Through the As outlined in Chapter 8 of this report, the current application of incremental technologies, one at a time, and NHTSA method of applying technologies to vehicles applies then optimizing the predicted overall vehicle performance them incrementally and individually to each vehicle in the and fuel economy tradeoffs, it is possible to understand and NHTSA database, starting from the experimentally deter- TABLE 9.3 Fuel Consumption Synergy Values for Inter-tree Technology Pairs—Results for Truck Package 11 Fraction of Total Fuel Consumption Impact Synergy Value—Impact on Total Fuel Consumption Inter-tree Technology Pair Attributed to Inter-tree Technology Pair (%) Reduction from Synergy of Technology Pair (%) Engine–transmission 6.62 2.17 Final drive ratio–engine 2.81 0.88 Aggressive shift logic–engine −1.28 −0.39 Electric accessories–engine 0.88 0.27 Aerodynamic drag–engine 0.44 0.13 Aerodynamic drag–transmission 0.36 0.11 Aerodynamic drag–final drive ratio 0.23 0.07 Aerodynamic drag–electric accessories 0.21 0.06 Aggressive shift logic–aerodynamic drag 0.14 0.04 NOTE: The modeling included three decision trees or families of technologies, one for engine technologies, one for transmission technologies, and a third for vehicle technologies. The results shown are for Truck Package 11 (Ricardo, Inc., 2009)

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154 ASSESSMENT OF FUEL ECONOMY TECHNOLOGIES FOR LIGHT-DUTY VEHICLES mined value for combined fuel economy as demonstrated in The use of such models may be necessary when evaluating the EPA vehicle exhaust emission certification process. One advanced technologies, such as variable compression ratio, potential flaw in this methodology results from the process that may not be readily available from production vehicles. in which the lumped parameter model is used to predict the Vehicle-related data, such as data on frontal area, rolling magnitude of energy loss reduction through the application resistance, and weight also are required input for modeling of discrete technologies on an actual vehicle-by-vehicle of vehicle performance and fuel economy. However, these basis. Without knowing the starting point in terms of how data are more readily approximated based upon simplified much the energy losses have been already been reduced, physics-based calculations or are published in accordance the ability to accurately project further reductions in such w ith vehicle certification testing. Therefore, although system energy losses, and therefore fuel consumption, can physics-based engine simulation models are available, the be highly erroneous. use of experimental engine data, as described above, greatly Stated another way, it appears most logical to begin any improves the accuracy of the modeling. predictive analysis with actual vehicle experimental data, if Experimental methods used to understand the effects they are available, as is the case with all vehicles certified of different technologies in an attempt to reduce system under the EPA Test Car List. However, without knowing energy losses have been developed under the United States how successful each manufacturer has been on a vehicle-by- Council for Automotive Research (USCAR) Benchmarking vehicle basis in an ongoing attempt to reduce such energy Consortium. Actual production vehicles are subjected to a losses, it is not possible, without detailed vehicle and power battery of vehicle, engine, and transmission tests in suffi - train experimental methods, to determine the extent to which cient detail to understand how each is applied and how they any such loss can be further reduced, with a reasonable level contribute to the overall performance and fuel consumption of accuracy, on an actual vehicle model. factors in light-duty vehicles. Combining such experimental With an understanding of the potential errors that will methods with FSS modeling, wherein all simulation vari- result from the approximation method presented above, ables and subsystem maps would be transparent to all inter- or other lumped parameter approaches where insufficient ested parties (both the regulatory agencies and automotive information is known about the level of energy loss reduc- manufacturers, for example), would allow, in the opinion tion that has previously occurred on a particular vehicle, of the committee, the best opportunity to define a techni - the committee proposes an alternative method whereby the cal baseline against which potential improvements could potential for fuel consumption reduction and its associated be more accurately and openly analyzed than the current costs can be assessed. This proposed method would de- methods employed. termine a characteristic vehicle that would be defined as a The advantages of such a method include the ability to reasonable average representative of a class of vehicles. This explicitly account for all energy loss categories, the ability representative vehicle, whether real or theoretical, would to directly estimate fuel consumption (as opposed to the undergo sufficient FSS, combined with experimentally deter- percent change in fuel consumption), and the ability to rep - mined and vehicle-class-specific system mapping, to allow a resent new technologies and combinations of technologies. reasonable understanding of the contributory effects of the It also recognizes the increasingly common utilization of applied technologies in the reduction of energy losses. The FSS models by regulatory agencies and other entities out - reference to a “theoretical” vehicle suggests that if, during side the automotive industry. Finally, the method proposes the regulatory process, the NHTSA and the EPA conclude a procedure whereby engine and vehicle experimental data that a vehicle may be characterized to represent a class that can be obtained without reliance on proprietary data, such may not be in production, FSS models may still be created as engine maps, that have posed a barrier to effective utili - using physics-based vehicle models combined with experi- zation of FSS models by non-OEMs in the past. mentally generated engine maps. The steps in the recommended process are as follows: In any full system simulation, the engine/power train/ vehicle system is defined by input data that are generated 1. Develop a set of baseline vehicle classes from which a by other physics-based analyses, engineering judgment, characteristic vehicle can be chosen to represent each or experimentally or empirically derived tests. Experi - class. The vehicle may be either real or theoretical mentally measured data for engine maps can incorporate and will possess the average attributes of that class as manufacturer-predetermined calibration parameters that determined by sales-weighted averages. have taken into consideration production operational factors 2. Identify technologies with a potential to reduce fuel such as knock-preventing spark timing or air/fuel ratio consumption. adjustments, which are used to protect from component 3. Determine the applicability of each technology to the temperature extremes. Physics-based engine maps, gener- various vehicle classes. ated from engine combustion models, may also be used, but 4. Estimate the technology’s preliminary impact on fuel calibration-specific parameters must also be incorporated consumption and cost. into such models to achieve best possible predictive results.

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155 APPLICATION OF VEHICLE TECHNOLOGIES TO VEHICLE CLASSES Finding 9.2: Data made available to the committee from 5. Determine the optimum implementation sequence (technology pathway) based on cost-effectiveness and original equipment manufacturers and Tier 1 suppliers and engineering considerations. found in various published studies suggest a very wide range 6. Document the cost-effectiveness and engineering in estimated incremental cost that makes assessments of cost- judgment assumptions used in step 5 and make this effectiveness very approximate. Generally, the committee information part of a widely accessible database. notes that estimates of cost are always more uncertain than 7. Utilize modeling software (FSS) to progress through estimates of impact on fuel consumption, and the estimates each technology pathway for each vehicle class to presented here should be considered very uncertain until obtain the final incremental effects of adding each more detailed studies are completed. As noted in Chapter 3, technology. estimates based on teardown cost analysis, currently being utilized by the EPA in its regulatory analysis for light-duty If such a process were adopted as part of a regulatory rule- vehicle greenhouse gas emissions standards, should be ex- making procedure, it could be completed on 3-year cycles panded for developing cost impact analyses. to allow regulatory agencies sufficient lead time to integrate Finding 9.3: In response to the statement of task, the com- the results into future proposed and enacted rules. Based on the eight new vehicle classes proposed by the mittee estimated possible technology evolution paths for committee, an average vehicle, either real or theoretical, each vehicle class that arise from the average baseline ve- would be chosen that possessed the average attributes of hicle. A very simple, multiplicative aggregation of potential the vehicles in that class. It would be of average weight, for reducing fuel consumption is presented as a means to footprint, engine displacement, and other characteristics. roughly estimate the total potential that might be possible. The resulting vehicle would serve as the baseline for FSS The results from this analysis show that, for the intermediate analysis. This would also allow a very important starting car, large car, and unibody standard truck classes, the aver- point for the vehicle systems from which potential improve- age reduction in fuel consumption for the SI path is about ments could be evaluated. Using detailed benchmark data, 29 percent at a cost of approximately $2,200; the average defined levels of energy losses would be used as input into reduction for the CI path is about 38 percent at a cost of the simulation model. The data used to choose the vehicle approximately $5,900; and the average reduction for the hy- consists of the following specifications available from the brids path is about 44 percent at a cost of $6,000. However, EPA test car list: unless calibrated methods are used to accurately consider the synergistic effects of applying several technologies—effects • Footprint, that may reduce the same sources of power train and vehicle • Weight, energy losses—these results are extremely approximate in Engine (displacement, cylinder count, horsepower, nature and, in the committee’s opinion, should not be used as torque), input to analyses for which modeling accuracy is important. • Valve train configuration (OHV, SOHC, DOHC), In general, the technology tables that present incremental • Valve event modulation technology (VVT, VVL), data for percent reduction in fuel consumption and estimated • Combustion technology (SI, CI, HCCI), incremental cost cannot be used in their current form as input • Fuel injection method and fuel type (SE�, GDI, DFI, into lumped parameter-type models without methods to ac- gasoline, diesel), curately consider the synergistic effects of applying several • Aspiration method (natural, supercharged, turbocharged), technologies and without significant expertise in vehicle • Number of occupants, technologies to fully understand integration issues. • Power/vehicle weight ratio, Recommendation 9.1: As noted in Chapter 8, full system • Transmission type and gear ratio spread, • Driveline (FWD, RWD, AWD), and simulation (FSS), based on empirically derived power train • EPA vehicle class. and vehicle performance and fuel consumption data maps, offers what the committee believes is the best available method to fully account for system energy losses and syner- FINDINGS AND RECOMMENDATION gies and to analyze potential reductions in fuel consumption Finding 9.1: Many vehicle and power train technologies that as technologies are introduced into the market. FSS analyses reduce fuel consumption are currently in or entering produc- conducted for the committee show that synergy effects be- tion or are in advanced stages of development in European tween differing types of energy-loss-reducing technologies or Asian markets where high consumer prices for fuel have vary greatly from vehicle application to vehicle application. justified their commercialization. Depending on the intended The committee proposes a method whereby FSS analyses vehicle use or current state of energy-loss minimization, the are used on class-characterizing vehicles, so that synergies application of incremental technologies will produce varying and effectiveness in implementing multiple fuel economy levels of fuel consumption reduction. technologies can be evaluated with what should be greater

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156 ASSESSMENT OF FUEL ECONOMY TECHNOLOGIES FOR LIGHT-DUTY VEHICLES accuracy. This proposed method would determine a char- 6. Document the cost-effectiveness and engineering acteristic vehicle that would be defined as a reasonable judgment assumptions used in step 5 and make this average representative of a class of vehicles. This represen- information part of a widely accessible database. tative vehicle, whether real or theoretical, would undergo 7. Utilize modeling software (FSS) to progress through sufficient FSS, combined with experimentally determined each technology pathway for each vehicle class to a nd vehicle-class-specific system mapping, to allow a obtain the final incremental effects of adding each reasonable understanding of the contributory effects of the technology. technologies applied to reduce vehicle energy losses. Data developed under the United States Council for Automotive If such a process were adopted as part of a regulatory rule- Research (USCAR) Benchmarking Consortium should be making procedure, it could be completed on 3-year cycles considered as a source for such analysis and potentially to allow regulatory agencies sufficient lead time to integrate expanded. Under the USCAR program, actual production the results into future proposed and enacted rules. vehicles are subjected to a battery of vehicle, engine, and transmission tests in sufficient detail to understand how each BIBLIOGRAPHY candidate technology is applied and how it contributes to EPA (U.S. Environmental Protection Agency). 2008a. Light-Duty Auto- the overall performance and fuel consumption of light-duty motive Technology and Fuel Economy Trends: 1975 Through 2008. vehicles. Combining the results of such testing with FSS EPA420-R-08-015. September. modeling, and thereby making all simulation variables and EPA. 2008b. EPA Staff Technical Report: Cost and Effectiveness Estimates subsystem maps transparent to all interested parties, would of Technologies Used to Reduce Light-Duty Vehicle Carbon Dioxide allow the best opportunity to define a technical baseline Emissions. EPA420-R-08-008. Ann Arbor, Mich. NHTSA (National Highway Traffic Safety Administration). 2009. Average against which potential improvements could be analyzed fuel economy standards, passenger cars and light trucks, model-year more accurately and openly than is the case with the current 2011: final rule, RIN 2127 AK-29, Docket No. NHTSA 2009-0062. methods employed. Washington, D.C., March 23. The steps in the recommended process are as follows: NRC (National Research Council). 2002. Effectiveness and Impact of Cor- porate Average Fuel Economy (CAFE) Standards. National Academy Press, Washington, D.C. 1. Develop a set of baseline vehicle classes from which a Ricardo, Inc. 2008. A Study of Potential Effectiveness of Carbon Dioxide characteristic vehicle can be chosen to represent each Reducing Vehicle Technologies. EPA420-R-08-004. Prepared for the class. The vehicle may be either real or theoretical U.S. Environmental Protection Agency, Contract No. EP-C-06-003, and will possess the average attributes of that class as Work Assignment No. 1-14. Ann Arbor, Mich. determined by sales-weighted averages. Ricardo, Inc. 2009. A Study of Interaction Effects Between Light Duty Vehicle Technologies. Prepared for the NRC Committee on Assessment 2. Identify technologies with a potential to reduce fuel of Technologies for Improving Light-Duty Vehicle Fuel Economy by consumption. Ricardo Inc., Van Buren, Mich., February 27. 3. Determine the applicability of each technology to the Sovran, G., and M.S. Bohn. 1981. Formulae for the tractive energy re- various vehicle classes. quirements of vehicles driving the EPA schedule, SAE Paper 810184. 4. Estimate each technology’s preliminary impact on fuel February. consumption and cost. 5. Determine the optimum implementation sequence (technology pathway) based on cost-effectiveness and engineering considerations.