National Academies Press: OpenBook

Assessment of Fuel Economy Technologies for Light-Duty Vehicles (2011)

Chapter: 8 Modeling Improvements in Vehicle Fuel Consumption

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Suggested Citation:"8 Modeling Improvements in Vehicle Fuel Consumption." National Research Council. 2011. Assessment of Fuel Economy Technologies for Light-Duty Vehicles. Washington, DC: The National Academies Press. doi: 10.17226/12924.
×

8
Modeling Improvements in Vehicle Fuel Consumption

INTRODUCTION

The potential of technology to reduce fuel consumption can be estimated in three basic ways. One approach involves constructing an actual prototype vehicle with the technologies in question, performing the Environmental Protection Agency (EPA) city and highway dynamometer tests repeatedly, and then measuring the fuel consumption. Although there is some variability from test to test, this method is the most accurate but is also prohibitively expensive. A second approach is to construct a computer model that represents all of a vehicle’s components and their interactions, including representations of the technologies for reducing fuel consumption, and to simulate the behavior of the vehicle over the federal test procedures. This method, which the committee refers to as full system simulation (FSS), is now the state of the art throughout the automotive industry for modeling fuel consumption. Although it is less expensive, FSS still requires very large expenditures of time and money if it is used to calibrate models to the 1,000 or so different vehicle configurations offered for sale in the United States each model-year and to test all relevant combinations of technologies. The third alternative is to construct an algorithm that adds discrete technologies to the set of base-year vehicle configurations and that then calculates their cumulative impact while attempting to account for interactions between them by means of adjustment factors. The committee refers to this third method as partial discrete approximation (PDA). The simplicity of the third approach allows fuel consumption impacts to be calculated for thousands of vehicles and tens of thousands of technology combinations. The key question is whether the third method can be executed with sufficient accuracy to support fuel economy regulation. The Volpe Model (Van Schalkwyk et al., 2009), used by the National Highway Traffic Safety Administration (NHTSA) in its rulemaking analyses, and the EPA’s OMEGA model, used by the EPA in its rulemaking analysis (EPA and NHTSA, 2009), are PDA models that use data on technology costs and fuel consumption impacts from a variety of sources, including FSS models.

This chapter evaluates methods of estimating the potential to decrease automotive fuel consumption by changing vehicle design and technology. It begins with some general observations on modeling technologies’ potential for reducing fuel consumption. Because of the technological complexities of vehicle systems, predicting how combinations of technologies might perform in new vehicle designs involves uncertainty. The present committee summarizes and discusses the method used by the National Research Council (NRC) Committee on the Effectiveness and Impact of Corporate Average Fuel Economy (CAFE) Standards in its 2002 report (NRC, 2002). It then goes on to compare and evaluate the two most widely used approaches to estimating the technological potential for reducing fuel consumption—PDA and FSS. Both methods are described in detail, and applications of the two methods to various types and configurations of vehicles are compared. Although it was able to make useful comparisons between modeling methods, the committee found that information comparing the results of either the FSS or the PDA method to real-world vehicles is scarce. The committee also comments briefly on the methodology used by the NHTSA in its 2011 Final Rule.

Recognizing the limitations of all modeling approaches, the committee considers the FSS method to be the state of the art and therefore the preferred method for estimating the potential of technologies to reduce fuel consumption. However, given the cost of FSS modeling at present, the committee believes that the PDA method, properly executed and supplemented with estimates of technology interaction effects developed by FSS or lumped parameter modeling, can be a reasonably accurate method for assessing the potential for reducing light-duty vehicle fuel consumption over a time horizon on the order of 10 years.

Suggested Citation:"8 Modeling Improvements in Vehicle Fuel Consumption." National Research Council. 2011. Assessment of Fuel Economy Technologies for Light-Duty Vehicles. Washington, DC: The National Academies Press. doi: 10.17226/12924.
×

CHALLENGES IN MODELING VEHICLE FUEL CONSUMPTION

Along with the many potential benefits of using computer models to understand vehicle systems come limitations as well. In addition to enabling insight into how an overall vehicle system might operate, vehicle system modeling can also help measure the interactions between vehicular subsystems and how they affect overall vehicle performance. An understanding of the physics underlying these interactions is important when trying to estimate how future vehicles might perform with different combinations of technologies. All models are inherently simplifications of reality; the physics of real processes will always be considerably more complicated than that reflected in a model. In the end, impacts can only be known with certainty when a technological concept is realized in a real vehicle, and even then realizations of the same technological concept can differ from one vehicle to another. The meaningful question is whether any given model or methodology has sufficient fidelity to competent executions of the technological concept to achieve the goals for which the model has been developed.

With even the most complex and comprehensive models, there are challenges when modeling a known vehicle configuration, and even greater challenges when trying to predict the behavior of future vehicles using new combinations of technologies. When modeling a known or existing vehicle the principal problems are in capturing the desired dynamics to a sufficient level of detail or fidelity, and in collecting and inputting representative parameters or boundary conditions. The advantage of modeling a known vehicle is that data on the vehicle’s actual performance are usually available to the modeler, and the data can be used to tune or validate the model’s performance. Even for existing vehicles, however, experimental data sets are frequently sparse and may not include the precise performance situations of interest.

Detailed computer modeling of vehicle systems can be very expensive. Developing sufficient data on the performance of engines and other components, data that are not generally available in the open literature, is a major source of the expense of FSS modeling. An automobile company might spend many times the resources available to the committee to develop dynamic models to help answer the kinds of questions posed to the committee. On the order of 1,000 different vehicle configurations undergo fuel consumption testing each model year. FSS modeling of even the most promising combinations of advanced technologies for such a large number of vehicles would be expensive for federal agencies. PDA modeling, on the other hand, can be implemented in simplified algorithms that can estimate fuel consumption potentials for thousands of vehicles or more, considering virtually all logical combinations of technologies.

There are at least six sources of error in estimating the potential to reduce vehicle fuel consumption:

  1. Differences between the attributes of the representative or typical vehicle being analyzed and the actual vehicles it represents;

  2. Inaccuracies in the characterization of the base vehicle, especially its energy flows;

  3. Inaccurate assessment of technology impacts, including the inability to fully represent the physics of a new technology in FSS modeling;

  4. Differences in the implementation of a given technology from vehicle to vehicle;

  5. Changes in the nature of a technology over time; and

  6. Inaccurate estimation of the synergies among technologies and how they contribute to the overall end result of their combined application.1

In general, rigorous, quantitative assessments of these potential sources of error and their impacts on the potential to reduce vehicle fuel consumption are scarce.

In this chapter comparisons of the results of FSS and PDA (with lumped parameter modeling) are presented. In addition, the committee contracted with Ricardo, Inc., to perform a statistical analysis of FSS modeling. The goal was to determine whether a limited number of FSS runs could be used to generate accurate data on the main effects of technologies and their interactions, which could then be used as basic input data for PDA modeling. The results of the analysis support the feasibility of this concept. Unfortunately, scientific data about the accuracy of either modeling method in comparison to actual vehicles are very limited.

METHODOLOGY OF THE 2002 NATIONAL RESEARCH COUNCIL REPORT

The 2002 NRC report Effectiveness and Impact of Corporate Average Fuel Economy Standards used a type of PDA method to estimate the potential future reductions of fuel consumption by light-duty vehicles. The 2002 committee recognized the existence of synergies among technologies applied to reduce fuel consumption but did not provide explicit estimates of the effects of such interactions. Technologies were implemented in defined sequences called paths, and the impacts of technologies on fuel consumption were adjusted to account for interactions with other technologies previously adopted.

1

In this report the committee chose to use the term synergies as defined in the joint EPA and NHSTA “Proposed Rulemaking to Establish Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards” (EPA and NHTSA, 2009). Two or more technologies applied together might be negatively synergistic, meaning that the sum of their effects is less than the impact of the individual technologies (contributes less to reducing fuel consumption, in this case), or might be positively synergistic, meaning that the sum of the technologies’ effects is greater than the impact of the individual technologies (in this case, contributes more to reducing fuel consumption).

Suggested Citation:"8 Modeling Improvements in Vehicle Fuel Consumption." National Research Council. 2011. Assessment of Fuel Economy Technologies for Light-Duty Vehicles. Washington, DC: The National Academies Press. doi: 10.17226/12924.
×

Technology changes modify the system and hence have complex effects that are difficult to capture and analyze. It is usually possible, however, to estimate the impacts of specific technologies in terms of a percentage savings in fuel consumption for a typical vehicle without a full examination of all the system-level effects. (NRC, 2002, p. 33)


For each technology assessed, the committee estimated not only the incremental percentage improvement in fuel consumption … but also the incremental cost that applying the technology would add to the retail price of a vehicle. (NRC, 2002, p. 35)

The 2002 NRC committee grouped technologies into three categories: engine technologies, transmission technologies, and vehicle technologies. Vehicles were grouped into 10 classes. Table 8.1 is the 2002 committee’s list of technologies for passenger cars, including ranges for the estimated incremental reductions in fuel consumption and for incremental RPE impacts.

For each vehicle class three different sequences of technology implementation, called “production development paths,” were mapped out. Figure 8.1 shows impacts of the technologies included in the three paths for passenger cars, as noted in Table 8.1, on the fuel consumption of a midsize car. The paths were intended to provide a logical sequence of implementation of the various technologies and to ensure that the incremental fuel consumption reductions from a given technology could be estimated conditional on the technologies that had preceded it. Paths 1 and 2 comprised proven technologies that could be introduced within the next 10 years (from 2002), with Path 2 including some more costly technologies than Path 1. Path 3 included additional emerging technologies the 2002 committee believed would become available within the next 15 years. The list of emerging technologies included several technologies that are now in use (intake valve throttling, automated manual transmission, advanced continuously variable transmissions (CVTs), integrated starter/generator, electric power steering) and several that are still not available (camless valve actuation, variable compression ratio engine). In addition, the 2002 committee judged that the potential for diesels to meet tighter emissions standards was highly uncertain and also excluded hybrids from its quantitative assessment due to uncertainty about their future potential. However, both technologies are now available on mass market vehicles in the United States.

In estimating the potential reduction in fuel consumption (gallons per 100 miles) of each technology, the 2002 committee drew on a variety of sources of information, from published reports to presentations to the committee by experts and consultations with automotive manufacturers and suppliers. Having studied the available information, the 2002 committee used its own expertise and judgment to decide on ranges of estimates for each technology. The ranges were intended to reflect uncertainties with respect to the technology of the baseline vehicle, effectiveness of the implementation, and possible tradeoffs with other vehicle attributes. Ranges were given for costs in order to reflect manufacturer-specific conditions, market uncertainties, and the potential for evolutionary costs reductions for new technologies. The 2002 committee did not specify a confidence interval for the ranges, nor did it explicitly address interdependencies or synergies of performance or cost, except via the incremental effects of sequential application in the technology paths.

The incremental fuel consumption improvement and retail price equivalent estimates in Table 8.1 are additive but only for a particular technology path. The technologies included in a path are indicated by an “X” in the columns labeled 1, 2, and 3. Technologies not contained in a path were not to be added to that path. A range of estimates is provided for both fuel consumption and cost impacts. However, only the midpoints of those estimates can be directly accumulated (as illustrated in Figure 8.1), since accumulation of all the high-end or low-end estimates without adjustment would produce misleading results.

The 2002 NRC committee’s method received some criticism for being overly simplistic. One notable critique (Patton et al., 2002) cited three major issues:

  1. Failure to examine system-level effects;

  2. Inaccurate fuel consumption estimates for individual technologies; and

  3. Overcounting of fuel consumption reductions.

The first point chiefly faulted the 2002 committee for multiplying together the impacts of individual technologies as if they were independent. It observed that when technologies address different energy-loss mechanisms, their impacts generally are independent, but when technologies address the same energy-loss mechanism (e.g., engine pumping losses), the aggregate effect may be more complex. The committee believed that it had addressed this issue by estimating the incremental effects of technologies implemented in a specified order. However, that committee neglected to quantify the energy losses addressed by each technology and did not separately quantify the interactions among technologies.

The second critique covered a variety of points including the degree of optimism in studies cited to support the committee’s estimates and inadequate attention to the dependence of fuel consumption impacts on the characteristics of the vehicle to which they are applied.

An example of this is cylinder deactivation. According to the report, cylinder deactivation is “applied to rather large engines (>4.0 L) in V8 and V12 configurations.” Yet the report applies the same fuel consumption reduction factor for cylinder deactivation to vehicles with six and four cylinder engines, where the actual benefit would be smaller. (Patton et al., 2002, p. 10)

However, the 2002 committee applied cylinder deactivation only to large passenger cars, midsize and larger sport

Suggested Citation:"8 Modeling Improvements in Vehicle Fuel Consumption." National Research Council. 2011. Assessment of Fuel Economy Technologies for Light-Duty Vehicles. Washington, DC: The National Academies Press. doi: 10.17226/12924.
×

TABLE 8.1 Fuel Consumption Technology Matrix: Passenger Cars

Baseline: overhead cams, 4-valve, fixed timing, roller finger follower

Fuel Consumption Improvement (%)

Retail Price Equivalent ($)

Subcompact

Compact

Midsize

Large

Low

High

1

2

3

1

2

3

1

2

3

1

2

3

Production-intent engine technology

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Engine friction reduction

1-5

35

140

X

X

X

X

X

X

X

X

X

X

X

X

Low-friction lubricants

1

8

11

X

X

X

X

X

X

X

X

X

X

X

X

Multivalve, overhead camshaft (2-V vs. 4-V)

2-5

105

140

 

 

 

 

 

 

 

 

 

 

 

 

Variable valve timing (VVT)

2-3

35

140

X

X

X

X

X

X

X

X

X

X

X

X

Variable valve lift and timing

1-2

70

210

 

X

X

 

X

X

 

X

X

 

X

X

Cylinder deactivation

3-6

112

252

 

 

 

 

 

 

 

 

 

 

X

 

Engine accessory improvement

1-2

84

112

X

X

X

X

X

X

X

X

X

X

X

X

Engine supercharging and downsizing

5-7

350

560

 

 

 

 

 

 

 

 

X

 

 

X

Production-intent transmission technology

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Five-speed automatic transmission

2-3

70

154

X

 

 

X

 

 

X

X

 

X

X

 

Continuously variable transmission

4-8

140

350

 

X

X

 

X

X

 

 

X

 

 

X

Automatic transmission w/aggressive shift logic

1-3

70

X

 

 

X

 

 

X

 

 

X

 

 

Six-speed automatic transmission

1-2

140

280

 

 

 

 

 

 

 

X

 

X

X

 

Production-intent vehicle technology

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Aero drag reduction

1-2

140

 

 

 

 

X

X

 

X

X

 

X

X

Improved rolling resistance

1-1.5

14

56

X

X

X

X

X

X

X

X

X

X

X

X

Safety technology

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Safety weight increase

−3 to −4

0

0

X

X

X

X

X

X

X

X

X

X

X

X

Emerging engine technology

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Intake valve throttling

3-6

210

420

 

X

 

 

X

 

 

X

 

 

X

 

Camless valve actuation

5-10

280

560

 

 

X

 

 

X

 

 

X

 

 

X

Variable compression ratio

2-6

210

490

 

 

X

 

 

X

 

 

X

 

 

X

Emerging transmission technology

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Automatic shift/manual transmission (AST/AMT)

3-5

70

280

 

 

 

 

 

 

 

X

 

 

X

 

Advanced CVTs—allows high torque

0-2

350

840

 

 

 

 

 

 

 

 

X

 

 

X

Emerging vehicle technology

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

42-V electrical system

1-2

70

280

 

 

X

 

 

X

 

X

X

 

X

X

Integrated starter/generator (idle off-restart)

4-7

210

350

 

 

X

 

 

X

 

 

X

 

 

X

Electric power steering

1.5-2.5

105

150

 

 

X

 

 

X

 

X

X

 

X

X

Vehicle weight reduction (5%)

3-4

210

350

 

 

 

 

 

 

 

 

 

 

 

 

NOTE: An X means the technology is applicable to the particular vehicle. Safety weight added (EPA baseline + 3.5%) to initial average mileage/consumption values.

SOURCE: NRC (2002), Table 3.1.

Suggested Citation:"8 Modeling Improvements in Vehicle Fuel Consumption." National Research Council. 2011. Assessment of Fuel Economy Technologies for Light-Duty Vehicles. Washington, DC: The National Academies Press. doi: 10.17226/12924.
×
FIGURE 8.1 Estimated cost of fuel consumption reduction in model-year 1999 midsize cars. SOURCE: NRC (2002), Figure 3.6.

FIGURE 8.1 Estimated cost of fuel consumption reduction in model-year 1999 midsize cars. SOURCE: NRC (2002), Figure 3.6.

utility vehicles (SUVs), minivans, and pickup trucks. Nearly all of these vehicles have engines with six or more cylinders. Cylinder deactivation is applied today to six-cylinder engines. Nonetheless, the 2002 committee’s characterization of baseline vehicles was based solely on the typical attributes of the 10 vehicle classes. Using the average characteristics of 10 classes of vehicles will lead to a certain degree of error if the resulting estimates are applied to the vehicles of specific manufacturers.

The criticism of inadequate attention to individual vehicle characteristics can also be leveled at the 2002 NRC committee’s costs estimates. The costs of fuel consumption technologies in the 2002 NRC report were the same for all vehicle classes. In fact, the costs of many technologies scale directly with measurable vehicle attributes such as weight or cylinder count.

The third critique is that the 2002 NRC committee’s estimates overstated the potential benefits of technologies that primarily addressed pumping losses because the methodology did not take into account the theoretical limits of pumping loss reduction.2

Using their own judgments about the allocation of the benefits of technologies to reduction of pumping losses, Patton et al. (2002) divided the 2002 committee’s fuel consumption benefit estimates into six categories of energy losses. Patton et al. (2002) attributed essentially all of the 2002 committee’s 4 to 8 percent benefit to reduction in pumping loss (and even added an additional 0.5 to 1.0 percent to pumping loss reduction that compensated for reduced transmission efficiency). Only a 0.0 to 0.5 percent benefit was assigned to increased thermal efficiency, presumably due to operating the engine in a more efficient portion of the engine map more of the time. Likewise, most of the benefits of 5-speed and 6-speed automatic transmissions (versus 4-speed) were attributed to reducing pumping losses with no benefits for engine thermal efficiency. Similarly, 4.0 to 6.0 percent of the committee’s estimated 5.0 to 7.0 percent benefits of engine boosting and downsizing was attributed to reduced pumping losses. The 2002 committee, on the other hand, judged that the technology derives much of its benefits from increased engine efficiency at light load due to engine downsizing and, when possible, reduced friction due to reduced cylinder count at equivalent power. The 2002 committee asserted that the energy efficiency benefits of multivalve, overhead camshaft engines derived from four different sources:

The application of single and double overhead cam designs, with two, three or four valves per cylinder, offers the potential for reduced frictional losses (reduced mass and roller followers), higher specific power (hp/liter), engine downsizing, somewhat increased compression ratios, and reduced pumping losses. (NRC, 2002, p. 36)

Patton et al. (2002) disagreed, assigning 2.0 to 5.0 percent of the committee’s estimated 2.0 to 5.0 percent

2

Patton et al. (2002) estimated the theoretical limits at between a 13 percent and 17 percent reduction in fuel consumption, depending on the vehicle in question. The U.S. EPA (2008b) estimated pumping plus friction losses at between 10 percent and 13 percent for actual vehicles, assuming a gross indicated engine efficiency of 37 percent.

Suggested Citation:"8 Modeling Improvements in Vehicle Fuel Consumption." National Research Council. 2011. Assessment of Fuel Economy Technologies for Light-Duty Vehicles. Washington, DC: The National Academies Press. doi: 10.17226/12924.
×

improvement to reduced pumping losses, while adding a 0.5 to 1.0 percent benefit in thermal efficiency, offset by a −0.5 to −1.0 percent efficiency loss due to increased friction.

While the benefits of variable valve timing and lift (VVT + L) are largely reductions in pumping losses, they also include improved power, and the benefits of cylinder deactivation include increased engine load (operation in a more efficient region of the engine map) as well as reduced pumping losses. Estimates of the benefits of the aforementioned technologies generated by FSS models have produced results consistent with the 2002 NRC committee’s estimates. Recent estimates from the DOT/NHTSA (2009) and the EPA (2008a) are compared with the 2002 NRC committee’s estimates in Table 8.2. The chief area of disagreement is the benefit of cylinder deactivation applied to multivalve, overhead camshaft engines with VVT and discrete or continuous lift control. The NHTSA estimated a benefit of 0.0 to 0.5 percent, whereas the NRC and the EPA estimated benefits of 3 to 6 percent.

The critics of the 2002 NRC report’s methodology make an important and valid point in calling attention to the lack of a rigorous relation between the estimates of fuel consumption reduction and the physical energy flows in a vehicle. As a consequence, the plausibility of the 2002 NRC estimates relied heavily on the expert judgment of the committee members. The 2002 NRC study’s method also did not explicitly account for the current use of the identified fuel economy technologies in existing vehicles. Practitioners of the PDA method can and often do account for energy constraints using simplified modeling methods called “lumped parameter” models, based on methods developed by Sovran and Bohn (1981) and extended by Sovran and Blaser (2003, 2006) and reviewed in Chapter 2 of this report. FSS models inherently account for energy flows and ensure that physical limits will not be violated.

MODELING USING PARTIAL DISCRETE APPROXIMATION METHOD

The PDA method incrementally adds discrete fuel-consumption-reducing technologies to a baseline vehicle until certain criteria are met. The method is sometimes applied to individual vehicles but more often assumes that the fuel consumption impact and cost of a technology will be approximately the same for all vehicles within at least a subset (or class) of light-duty vehicles. In a presentation to the committee, K.G. Duleep of Energy and Environmental Analysis, Inc. (EEA) identified three important areas in which the PDA method, and especially its application in the 2002 NRC study, had come under criticism (Duleep, 2008).

  1. Adequate definition of baseline vehicles;

  2. Order of implementation of fuel consumption technologies; and

  3. Accounting for synergies among fuel consumption technologies.

The chief disadvantage of the PDA method is that it is entirely empirically based and therefore does not explicitly represent the interactions among any set of technologies. Synergies among technologies are estimated by engineering judgment or by means of simplified analytical tools, such as lumped parameter models of vehicle energy use (Duleep, 2008; Sovran and Blaser, 2003, 2006). Computational simplicity and the ability to quickly and economically process information on thousands of individual vehicles and dozens of alternative combinations of technologies are the method’s chief advantages.

The main steps in the PDA process are the following:

  1. Identify discrete technologies with fuel consumption reduction potential.

TABLE 8.2 Comparison of Benefits of Valve Train Technologies as Estimated by NRC (2002), NHTSA’s Final Rule for 2011, and the EPA

Technology

NRC (2002) (%)

Midpoint (%)

NHTSAa (%)

Midpoint (%)

EPA (%)

Midpoint (%)

Multivalve OHC

2-5

3.5

1-2.6

1.8

NA

NA

Variable valve timing

2-3

2.5

3-5

4

2-4

3

Variable valve lift and timing

1-2

1.5

1.5-3.5

2.5

3-4

3.5

Cylinder deactivation

3-6

4.5

0.0-0.5

0.25

6

6

Subtotal

 

12

 

8.5

 

12.5

Intake valve throttlingb

3-6

4.5

1.5-3.5

2.5

1-2

1.5

Total

 

16.5

 

11

 

14

Camless valvesc

5-10

7.5

NA

NA

5-15

10

aNHTSA’s fuel consumption benefits are path dependent. The path shown here is for dual overhead camshaft engines.

bIn NHTSA’s terminology IVT is continuously variable valve lift (CVVL) and is a substitute for discrete variable valve lift (DVVL). NHTSA argues that cylinder deactivation applied to CVVL has little benefit since pumping losses have already been greatly reduced. Others argue that this misses the benefit of increased engine efficiency at higher load when a six-cylinder engine is operated on only three cylinders.

cEffect of camless valve actuation is incremental to variable valve lift and timing not to intake valve throttling. The two are mutually exclusive.

SOURCE: Based on data in NRC (2002), DOT/NHTSA (2009), and EPA (2008a).

Suggested Citation:"8 Modeling Improvements in Vehicle Fuel Consumption." National Research Council. 2011. Assessment of Fuel Economy Technologies for Light-Duty Vehicles. Washington, DC: The National Academies Press. doi: 10.17226/12924.
×
  1. Determine the applicability of each technology.

  2. Estimate each technology’s impact on fuel consumption and cost.

  3. Determine implementation sequences based on

    1. Cost-effectiveness and

    2. Engineering and manufacturing considerations.

  1. Identify and estimate synergistic effects

    1. Based on empirical data and expert judgment,

    2. Using a simplified model of vehicle energy flows (e.g., lumped-parameter model), or

    3. Using estimates from FSS models.

  1. Determine the “optimal” fuel consumption level by

    1. Using a computer algorithm that sequentially applies technologies,

    2. Using fuel consumption cost curves.

Identifying Technologies That Reduce Fuel Consumption

The PDA method, like the FSS method, begins with the identification of distinct technologies that have the potential to reduce vehicle fuel consumption at a realistic cost.3 The list of all possible technologies with some potential to reduce fuel consumption could range from lower-rolling-resistance tires and improved engine lubricants to human-powered vehicles and the compressed air engine. When the purpose is regulatory rulemaking, not all possible fuel consumption technologies should be included. The world record for automotive fuel economy is held by the Pac Car II, a fuel-cell-powered vehicle that won the 2005 Shell Ecomarathon in Ladoux, France, with a gasoline equivalent fuel economy of 12,666 miles per gallon.4 The three-wheel vehicle accommodates one small passenger, who must drive lying down. The 0.57-m wide, 0.61-m high, 2.78-m long carbon-fiber body has no room for cup holders, not to mention air conditioning. It is a zero-emission vehicle, but meeting safety standards was not a design consideration. Clearly much of the PAC Car II’s fuel economy was achieved by making unacceptable tradeoffs with other vehicle attributes. The CAFE law requires that fuel economy standards must be technologically feasible and economically practicable. This is ultimately a matter of expert judgment, yet there is remarkable agreement among diverse studies on the list of relevant technologies. Most assessments assume no reduction in size or power-to-weight ratios as a premise.

In general, studies of fuel consumption potential intended to inform the regulatory process and using the PDA method select technologies that meet all of the following three criteria:

  1. Technologies already incorporated in at least one mass-produced vehicle somewhere in the world or preproduction technologies judged to have a strong likelihood of widespread adoption within the next decade;

  2. Technologies having no significant negative impact, or a beneficial impact on attributes that are valued by consumers or that are necessary to meet safety and emissions regulations; and

  3. Technologies whose cost does not far exceed the potential value of fuel savings and other private and social benefits.

For example, all but a few of the technologies considered by the 2002 NRC study were already in mass production. In general, PDA studies are most reliable when they are limited to technologies already in production. However, the farther one must look into the future the less tenable this constraint becomes.

Determining Applicability

Not every technology will be applicable to every vehicle. Torque limitations, for example, have so far prevented the use of CVTs in the largest, most powerful light-duty vehicles. Engine downsizing by reducing the number of cylinders with turbo-charging may be considered applicable to six-cylinder engines but less so to four-cylinder engines due to vibration and harshness considerations. Applicability appears to be largely a matter of expert judgment, determined on a case-by-case basis. The applicability step reduces the full set of technologies to only those that can be used on the baseline vehicle being considered.

Estimating Fuel Economy and Cost Impacts

Fuel consumption impacts are estimated for each technology and each class of vehicles (or each individual vehicle) to which it is applicable. Practitioners of the PDA method derive their estimates from a variety of sources. Unlike FSS, the PDA method, by itself, is not able to produce fuel consumption impact estimates for individual technologies. It is a method of aggregating the fuel consumption impacts of various technologies and must obtain the individual technology benefit estimates from other sources. In its report to the committee, EEA cited three principal sources of information on fuel economy benefit.

First, the trade press, engineering journals and technical papers presented at engineering society meetings provide detailed information on the types of technologies available to improve fuel economy and the performance, when applied to current vehicles. Second, most of the technologies

3

The CAFE guidance states that fuel economy standards should be set at the maximum feasible level, taking into consideration technological feasibility, economic practicability, the effect of other federal motor vehicle standards on fuel economy, and the need of the nation to conserve energy (Motor Vehicle Information and Cost Saving Act, Title V, Chapter 329, Section 32902[a]).

4

Details about the competition, the car, and its design can be found at http://www.paccar.ethz.ch/.

Suggested Citation:"8 Modeling Improvements in Vehicle Fuel Consumption." National Research Council. 2011. Assessment of Fuel Economy Technologies for Light-Duty Vehicles. Washington, DC: The National Academies Press. doi: 10.17226/12924.
×

considered in this report have been introduced in at least a few vehicles sold in the marketplace, and actual test data on fuel economy can be used. Third, the world’s largest auto-manufacturers have research and development staff with detailed knowledge of the attributes of each technology, and their inputs in an unconstrained situation can be used to estimate the benefits of technologies. (EEA, 2007, p. 9)

The EPA has provided a similar list of sources of information.

These data sources included: vehicle fuel economy certification data; peer reviewed or publicly commented reports; peer reviewed technical journal articles and technical papers available in the literature; and confidential data submissions from vehicle manufacturers and automotive industry component suppliers. (EPA, 2008a, p. 2)

The EPA considers the vehicle certification test data to be an especially reliable source when a directly comparable vehicle is offered with and without a specific technology. In addition, the NHTSA’s staff has access to proprietary data provided by vehicle manufacturers to directly support the rulemaking process.

Recently, FSS models have been extensively used to estimate the fuel economy impacts of individual technologies and combinations of technologies (e.g., Ricardo, Inc., 2008a,b; Sierra Research, Inc., 2008). A study done by Ricardo, Inc., for the committee and described below indicates that data on technologies’ main and synergistic effects generated by FSS models can be used effectively in PDA analyses (Ricardo, Inc., 2009).

Sequencing Implementation

Sequences for implementing fuel economy technologies are usually determined by a combination of cost- effectiveness and engineering considerations. All else equal, it would be economically efficient to implement first the technology that offered the greatest reduction in fuel consumption per dollar of cost, followed by the technology with the second largest ratio, and so on. Engineering considerations may dictate a different sequence, however. For example, VVT for both intake and exhaust must come after VVT for intake only, regardless of cost-effectiveness.

Fuel consumption benefits must then be converted to incremental benefits, given the implementation sequence. For example, the benefit of a 6-speed transmission must be defined as incremental to that of a 5-speed transmission, even if the base vehicle has a 4-speed, assuming that the 5-speed will be implemented before the 6-speed.5 Obvious incompatibilities (e.g., a vehicle cannot have both a 6-speed automatic transmission and a CVT at the same time) must also be taken into account.

Accounting for Synergies

Undoubtedly the most serious criticism of the PDA method is that it does not adequately account for synergies among fuel economy technologies. Whether or not the PDA approach is capable of appropriately accounting for synergies is one of the key issues addressed by the present committee.

Fuel economy technologies can have both positive and negative synergies (see footnote 1). In addition, the impacts of technologies applied to vehicle subsystems could potentially be significantly nonlinear, and therefore the effects of multiple technologies might not be accurately estimated by summing the effects of the individual technologies. Practitioners of the PDA method draw on three sources of information to estimate such synergistic effects (EEA, 2007). Because most of the technologies under consideration are in use in some mass-produced vehicle, it is occasionally possible to find models using a combination of several technologies. Comparing the actual fuel consumption performance of these vehicles to an estimate based on the sum of their individual effects can provide an estimate of the degree of synergy.

Second, simplified lumped parameter models of vehicle energy use (e.g., Sovran and Bohn, 1981) provide a means of avoiding the double counting of energy savings. Given a few key parameters, lumped parameter models allow the quantification of sources of energy loss and the components of tractive force requirements for a vehicle. By attributing the impacts of technologies to specific energy losses and tractive force requirements, analysts can check that the sequential application of technologies has plausible impacts on the factors determining energy use. A key question is whether the use of a lumped parameter model can sufficiently accurately account for synergistic effects or whether the FSS method must be used in all cases (Hancock, 2007). An analysis of this subject by Ricardo, Inc. (2009) commissioned by the committee, together with an assessment by the EPA considered below, indicates that a reasonably accurate accounting is possible.

The ability of lumped parameter models to accurately predict vehicle fuel use was first demonstrated by Sovran and Bohn (1981). In an updated version of the same methodology, Sovran and Blaser (2003) showed that despite major changes in automotive technology, lumped parameter models still predicted tractive energy requirements with a high degree of accuracy. Development of a lumped parameter model begins with the fundamental physics equations that determine the energy requirements of vehicles over fixed driving cycles, in particular the EPA urban and highway cycles (equations of the lumped parameter model are presented in Chapter 2). Any cycle can be divided into three regimes:

  1. Times when tractive force (FTR) is required from the engine;

5

In the PDA method a leap from a 4-speed transmission directly to a 6-speed transmission would be calculated by combining the incremental costs and fuel consumption effects of the 4- to 5-speed transition and the 5- to 6-speed transition.

Suggested Citation:"8 Modeling Improvements in Vehicle Fuel Consumption." National Research Council. 2011. Assessment of Fuel Economy Technologies for Light-Duty Vehicles. Washington, DC: The National Academies Press. doi: 10.17226/12924.
×
  1. Times when deceleration force is greater than rolling resistance (R) and aerodynamic drag (D); and

  2. Times when no tractive force is required (vehicle stationary or undergoing deceleration provided by R + D).

When tractive force is required on either cycle, it must equal the sum of forces required to overcome rolling resistance, aerodynamic drag, and inertia. The lumped parameter method simplifies the equation for tractive force and other equations for braking and idling modes by integrating over the drive cycles, as explained in detail in Chapter 2 of this report. Sovran and Blaser (2003) found that the lumped parameter model defined by Equations 2.2 and 2.3 could explain the tractive energy required at the wheels and hence indirectly the engine output of vehicles over either EPA test cycle with an R2 = 0.9999.

The lumped parameter method allows changes in pumping losses, engine friction, accessory loads, and other factors to be related in a manner that can prevent double counting if done properly. It reduces the likelihood of overestimating the combined fuel consumption impacts of multiple technologies by requiring that the laws of physics controlling energy flows and tractive requirements be maintained. As such, it is a powerful tool for quantifying synergistic effects for use in the PDA method. The lumped parameter method cannot, however, predict the kind of synergistic effects that occur when two or more technologies alter each other’s performance. This topic is taken up in detail in the following section.

FSS modeling more completely represents such synergistic effects and so it is useful to compare lumped parameter and FSS estimates to test the adequacy of PDA synergy estimates. The U.S. EPA (2008a) used both methods to estimate the fuel economy benefits of 26 technology packages applied to five vehicle types. For most packages they found close agreement between the two types of estimates (Figure 8.2). The EPA’s general conclusion was that both methods were valuable and that the use of lumped parameter modeling in PDA estimation gave reasonable estimates of synergies.

Based on this, EPA concludes that the synergies derived from the lumped parameter approach are generally plausible (with a few packages that garner additional investigation). (EPA, 2008b, p. 44)

FIGURE 8.2 EPA’s comparison of full vehicle simulation model (Ricardo, Inc.) and lumped parameter (L-P) PDA model results. SOURCE: EPA (2008a), Figure 3.3-1.

FIGURE 8.2 EPA’s comparison of full vehicle simulation model (Ricardo, Inc.) and lumped parameter (L-P) PDA model results. SOURCE: EPA (2008a), Figure 3.3-1.

Suggested Citation:"8 Modeling Improvements in Vehicle Fuel Consumption." National Research Council. 2011. Assessment of Fuel Economy Technologies for Light-Duty Vehicles. Washington, DC: The National Academies Press. doi: 10.17226/12924.
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In 10 cases, significant differences were found (EPA, 2008b). For Standard Car Package 1 and Small MPV Package 1, the lumped parameter method estimated a larger fuel economy improvement. The difference was traced to the CVT component. The Ricardo, Inc., FSS CVT representation had a lower efficiency than assumed in the lumped parameter model. Two other cases involved turbo-charging with engine downsizing. The lumped parameter model estimate was also much higher in the case of Large Car Package 6a, involving continuously variable valve lift. In the case of Large Car Package 4, the lumped parameter model estimated a large benefit, but in the case of Truck Package 10, the FSS model produced the higher benefit estimate. For the packages including cylinder deactivation and coordinated cam phasing (Large Car 16, Large MPV, and Truck 12), the FSS modeling results were consistently higher. FSS estimates were also higher for the cases involving camless valve trains (Large Car Y1, Truck X1). The EPA staff is still investigating reasons for the differences but had identified at least some cases in which the comparison between the two methods led to the discovery of inadvertent errors in the FSS modeling. For example, EPA judged that Ricardo’s modeling of cylinder deactivation and coupled cam phasing was incorrect because it did not account for cylinder deactivation’s effect of approximately doubling brake mean effective pressure (BMEP) in the firing cylinders. The EPA staff suggested that conducting both FSS and lumped parameter analysis was a wise strategy since the discrepancies between the two methods had led to the discovery of correctable errors.

Twenty-three of the 26 packages evaluated by Ricardo were also estimated by EEA, Inc. (Duleep, 2008) for comparison. EEA was not able to estimate the packages including homogeneous charge compression ignition (HCCI) due to the novelty of the technology. The FSS method requires an externally provided representation of the physics of a device in order to estimate its impact on fuel consumption. While the FSS method itself cannot characterize the physics of technologies, it can produce impact estimates given such characterizations. The PDA method, on the other hand, must be given estimates of impacts for novel technologies. In 16 of the 23 comparisons the two methods produced estimates with relative differences of less than 5 percent. In two cases involving CVT transmissions the Ricardo estimate was much lower. In the committee’s discussions with Ricardo and EEA, it was determined that this was due to Ricardo’s estimated efficiency of the CVT being much lower than EEA’s. This instance illustrates how both methods depend on assumptions about the performance of key technologies. In the remaining five cases, Ricardo’s FSS estimates were higher but there appeared to be no common technology that could explain the differences. One of these cases was again the Truck Package 10 involving a turbo-charged gasoline direct injection engine: EEA’s lumped parameter PDA method estimated a fuel economy benefit of 26.4 percent, whereas the Ricardo estimate was 42 percent.

Determining the “Optimal” Level of Fuel Economy

Calculation of fuel economy potential and its cost can be accomplished by algorithms that decide which technologies to apply and in what order, or by the use of fuel economy cost curves. The algorithmic approach relies on predefined technology implementation sequences (decision trees or pathways) and is the basis of the Department of Transportation’s Volpe Model (Van Schalkwyk et al., 2009) and the Energy Information Administration’s NEMS model’s Manufacturers’ Technology Choice Submodule (DOE/EIA, 2007). The decision tree methodology is described below. Cost curves developed by the NRC (2002) CAFE study and in a number of other studies have been reviewed in Greene and DeCicco (2000).

A PDA Algorithm: The NHTSA’s Volpe Model

The NHTSA’s Volpe model contains a compliance simulation algorithm that simulates the response of manufacturers to various forms of fuel economy standards. Data are put into the model describing a “CAFE scenario,” a combination of definitions of vehicles included in the program, definitions of vehicle classes, levels of fuel economy standards that must be met each year, and the structure of the standards. The structure comprises several elements, the mathematical formulation (e.g., sales-weighted harmonic mean), the functional form (e.g., footprint metric function), the classes of vehicles to which it applies (e.g., foreign or domestic manufacture), and provisions for trading credits over time and among firms. In the description below, the focus is the determination of a manufacturer’s “optimal” fuel economy level for a given CAFE scenario.

The algorithm begins with a list of vehicles expected to be available during the future period being evaluated. This is typically a narrow window of three to five model years, beginning 2 years in the future. Vehicles are distinguished by make, model, engine, and transmission, as in the EPA test car list. Many other vehicle attributes are in the vehicles data base, including sales volumes, prices, and specifications. The compliance algorithm applies technologies to each vehicle in the database individually. In the past, the technologies were largely taken from the NRC 2002 report’s three technology path lists, but for the 2011 Fuel Economy Rule, the NHTSA developed a new technology list with the assistance of Ricardo, Inc. The new list adds diesel and hybrid power trains (including plug-in hybrids) and materials substitution to reduce vehicle weight. It represents other technologies at a greater level of detail. It also provides a table of estimated pair-wise synergies between technologies. However, the synergies used in the final rule appear to be the same for all vehicles classes. The analysis done for the committee by Ricardo, Inc., described below, indicates that synergy effects can vary across applications to different classes of vehicles (Ricardo, Inc., 2009).

Suggested Citation:"8 Modeling Improvements in Vehicle Fuel Consumption." National Research Council. 2011. Assessment of Fuel Economy Technologies for Light-Duty Vehicles. Washington, DC: The National Academies Press. doi: 10.17226/12924.
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The algorithm evaluates the applicability of each technology to each individual vehicle based on timing of availability and whether or not it is included in decision trees for that vehicle class. The Volpe model’s decision trees are analogous to the 2002 NRC study’s “paths” except that there are separate decision trees for internal combustion engines, transmissions, electrical accessories, material substitution, dynamic load reduction, aerodynamic drag reduction, and hybrid electric technology. The engine technology decision tree is shown in Figure 8.3. After low-friction lubricants and engine friction reduction are accomplished, the tree splits into three paths depending on camshaft configuration. This allows the NHTSA to tailor the technology sequencing to the base vehicle’s engine attributes. If fuel economy is pushed to higher levels the three paths then converge on the stoichiometric, gasoline direct-injection engine. A table of notes can be used to “override” the algorithm’s logic and determine applicability in special cases (e.g., as in Table 4, DOT, 2005).

In the committee’s judgment, it is not necessary to have separate decision trees for engines and transmissions. This view is supported by the Ricardo, Inc. (2009) analysis, which demonstrates that the important across, or interdecision-tree, synergies are between engines and transmissions (Ricardo, Inc., 2009). These inter-tree synergies can be transformed to incremental improvements by combining engines and transmissions into a single power train decision tree. Once this has been done, nearly all important synergies can be addressed by adjusting technology impacts to account for interactions with technologies previously implemented in the decision tree, or pathway.

In the Volpe model, the cost and fuel economy impact of each technology vary by vehicle class. Previously the 10 vehicle classes of the 2002 NRC report were used, but the 2011 rule is based on 12 vehicle classes that include 4 performance-based classes:

  1. Small light truck (including SUVs and pickups),

  2. Midsize light truck (including SUVs and pickups),

  3. Large light truck (including SUVs and pickups and full-size vans),

  4. Minivans,

  5. Subcompact cars,

  6. Subcompact performance cars,

  7. Compact cars,

  8. Compact performance cars,

  9. Midsize cars,

  10. Midsize performance cars,

  11. Large cars, and

  12. Large performance cars.

The sequence in which the technologies are applied to any given vehicle is determined by an optimization algorithm. Technologies already in use in a given vehicle are “carried over” from the previous year so that they are not duplicated.

The algorithm then begins an iterative process of determining a manufacturer’s compliance with the CAFE standards. If a manufacture is not in compliance, the algorithm selects the next-best technology to add to the vehicle.6 A technology is selected from the next steps on each of the applicable decision trees. The single technology that has the lowest “effective cost” is chosen for implementation. Effective cost is defined as the total retail price equivalent (RPE) cost of implementing the technology (the change in RPE times the number of vehicles affected), plus any change in the manufacturer’s potential CAFE fine, minus the total discounted value of fuel saved by the increase in fuel economy, all divided by the number of vehicles affected. Fines are calculated so as to take account of credits for exceeding standards on some vehicles that can be transferred to other vehicles. Some manufacturers are assumed not to be willing to pay fines and so for them that option is removed. The current version of the model calculates credits or deficits (negative credits) generated by exceeding or failing to meet the standard in any given year. It does not, however, attempt to model credit trading either within a manufacturer over time or among manufacturers. The algorithm continues considering and implementing next-best technologies for all vehicle classes until a manufacturer either achieves compliance with the standard, exhausts all available technologies, or finds that paying fines is more cost-effective than increasing fuel economy (Van Schalkwyk et al., 2009, p. 2).

In a joint EPA and NHTSA (2009) notice of proposed rulemaking (NPRM) the EPA introduced its optimization model for reducing emissions of greenhouse gases from auto mobiles (OMEGA) model. Like the Volpe model, OMEGA is based on the PDA method and although the logic of the two models is fundamentally the same, there are some notable differences. The Volpe model operates on individual vehicle configurations (on the order of 1,000 make, model, engine, and transmission combinations), taking into account the existing or planned use of fuel economy technologies on each one. The OMEGA model deals with approximately 200 vehicle platforms broken down by engine size (EPA and NHTSA, 2009). For the purpose of estimating technology impacts the 200+ platforms are divided into 19 vehicle types that attempt to distinguish among power trains and market intent. Each of the 19 vehicle types are grouped into five vehicle classes (small car, large car, minivan, small truck, and large truck) for the purpose of scaling cost estimates. In general, the EPA’s baseline vehicle is defined as one with a port-fuel-injected, naturally aspirated gasoline engine with two intake and two exhaust valves and fixed valve timing and lift, and a 4-speed automatic transmission. For the NHTSA’s Volpe model the baseline is the actual configuration of each

6

The Volpe model allows manufacturers to opt for non-compliance if paying a fine is less costly than missing the standards, and if a switch set in input data files allows such non-compliance. This option is not discussed here for the sake of brevity.

Suggested Citation:"8 Modeling Improvements in Vehicle Fuel Consumption." National Research Council. 2011. Assessment of Fuel Economy Technologies for Light-Duty Vehicles. Washington, DC: The National Academies Press. doi: 10.17226/12924.
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FIGURE 8.3 Volpe model engine technology decision tree.

FIGURE 8.3 Volpe model engine technology decision tree.

Suggested Citation:"8 Modeling Improvements in Vehicle Fuel Consumption." National Research Council. 2011. Assessment of Fuel Economy Technologies for Light-Duty Vehicles. Washington, DC: The National Academies Press. doi: 10.17226/12924.
×

vehicle configuration as it exists or is predicted to exist in the baseline fleet.

The Volpe model applies individual technologies one at a time in a sequential algorithm, whereas the OMEGA model applies predefined packages of technologies that have been ranked by cost-effectiveness for each vehicle type. However, the packages are assembled from individual technology impact estimates, with synergies between technologies within a package incorporated in the technology package impact estimates. The EPA used the lumped parameter method to determine the adjustment factors (EPA and DOT, 2009, p. 171).

Because neither the Volpe CAFE Compliance and Effects Modeling System nor the EPA’s OMEGA model make use of cost curves but rather employ computer algorithms, neither NHTSA nor EPA require cost curves but rather a list of fuel economy technologies including cost, applicability, and synergy estimates. This committee’s method is based on implementation pathways that are analogous to the Volpe model’s decision trees and the OMEGA model’s packages. Therefore, this committee determined that it was not necessary for this study to produce cost curves as such.

Aggregating to Estimate Manufacturers’ Fleet Average Fuel Economy

Because fuel economy standards are enforced on automobile manufacturers, both the FSS and PDA methods require a means of inferring the fuel economy potential of an OEM from the fuel economy potential of individual vehicles or vehicle classes. The FSS method is sufficiently computationally intensive that it has not been practical to carry out simulations for all thousand or so vehicles in the EPA test car database for all relevant combinations of technologies. Using the PDA method, a manufacturer’s fuel economy potential can be calculated using data on individual configurations (make, model, engine, transmission, i.e., a single entry in the test car database) or using data on classes of vehicles. The NHTSA’s Volpe model, for example, calculates a manufacturer’s fuel economy target using individual vehicle data since each vehicle has its own fuel economy target as a function of its footprint. The model also calculates each manufacturer’s fuel economy potential at the test car list level of detail. Estimates based on vehicle classes can also be computed but they will only be approximately equal to estimates based on individual configurations.

Assume that the optimal level of fuel economy for a single vehicle configuration j has been determined to include technologies k = 1 to nj (given a technology implementation sequence and fuel economy impacts adjusted for implementation order and synergies). The cumulative fuel economy impact is calculated by summing the fractional fuel economy (miles per gallon) improvements, adding one, and multiplying by the base fuel economy MPG0j. If the sales of vehicle configuration j are sj, then the fuel economy for manufacturer k selling configurations j = 1 to Nk is the following:

Equation 1

If the calculation is done in terms of fuel consumption, or gallons per mile (GPM), the corresponding equation for the manufacturer’s fuel consumption target is the following:

Equation 2

Equations 1 and 2 make two strong assumptions. First, they assume that the relative fuel consumption impact of a technology will not vary from vehicle to vehicle. Because impacts will vary depending on the initial design of each vehicle, some error will be introduced for each vehicle. In addition, it is assumed that, for a given implementation sequence, any interactions (synergies) among technologies have already been accounted for in the Δ or δ terms. Given information on technology synergies generated by FSS models, equations 1 or 2 could be modified to include synergistic effects as each technology is added. Summing relative fuel economy increases as in equation 1 produces a smaller estimate than sequentially multiplying one plus the relative fuel economy increases. Most fuel economy impact estimates have been determined with the expectation that they will be added to obtain the overall fuel economy benefit. Likewise, multiplying the terms in equation 2 will produce a smaller estimated change in fuel consumption than adding the δi, which could erroneously lead to negative fuel consumption. In either case, adding fuel economy impacts or multiplying fuel consumption impacts is intended to produce an approximation to the true impact in a way that reduces the chances of overestimating fuel consumption benefits.

Aggregation over Vehicles in a Class

The PDA method can be applied to an individual vehicle or to a representative vehicle (e.g., a midsize passenger car). For an individual vehicle, it is necessary to know the existing technology makeup of the vehicle so that incompatibilities are avoided and technologies are not applied twice. In the case of a representative vehicle, it is necessary to know the market shares of fuel economy technologies for vehicles in its class. In general, the exact distribution of all combinations of technologies within the vehicle class is not known. Instead, the total market shares of each technology are used, in effect assuming that their distributions are independent. This introduces a further element of approximation into the estimation.

Suggested Citation:"8 Modeling Improvements in Vehicle Fuel Consumption." National Research Council. 2011. Assessment of Fuel Economy Technologies for Light-Duty Vehicles. Washington, DC: The National Academies Press. doi: 10.17226/12924.
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Let sij,0 be the initial market share of technology i in the vehicle class j, and let sij,max be the maximum market share for technology i. The estimated change in fuel economy (MPG) by application of the full set of technologies is given by equation 3:

Equation 3

The estimated change in fuel consumption by application of the full set of technologies is given by equation 4:

Equation 4

The cost of the above fuel economy increase is calculated similarly, where Ci is the cost of technology i in retail price equivalent:

Equation 5

Although equation 3 approximates the share-weighted harmonic mean change in fuel economy for a class of vehicles with a mixture of technologies it does not precisely equal it. Even performing the calculations in terms of fuel consumption, as in equation 4, will not produce the exact harmonic mean fuel economy, in general.

MODELING USING FULL SYSTEM SIMULATION

The FSS approach to modeling vehicle fuel consumption involves capturing the physics or characteristics of subsystems of the vehicle in software, assembling these subsystems by passing relevant operational variables between these subsystems, and choosing preferred input variables and trajectories to simulate desired vehicle operation. The overall goal is to have the subsystem models work in a synergistic way to reflect the actual performance of the vehicle in various maneuvers. Because of the complexity and nonlinearity of these vehicle subsystems, it is often difficult to anticipate the synergistic effects, especially during transients, and this approach usually provides this useful information to some degree of accuracy. FSS modeling has been used by the auto motive industry since the 1970s, and is a proven method of estimating the impacts of existing and new technologies on vehicle systems (Waters, 1972; Blumberg, 1976). More recently, regulatory agencies and other groups outside the automotive industry are undertaking efforts to develop and utilize FSS in their analysis (NESCCAF, 2004; Rousseau, 2007; EPA, 2008a).

Although modeling approaches differ, all FSS models are based on the time integration of Newton’s second law (i.e., F = m·a) over some driving maneuver, in this case over the FTP and highway driving cycles. The boundary and initial conditions for this integration are based on a description of the vehicle (mass, frontal area, drag coefficient, etc.), the components that compose the driveline (engine and transmission, etc.), accessories (pumps, fans, generators, etc.), and a specification of the drive cycle, or vehicle speed trace, the vehicle is to perform. Components are represented by computer modules and may be described by performance maps represented by tables or equations. All energy flows among components are accounted for by equations linking the modules. FSS models may be backward-looking or forward-looking. Backward-looking models assume that the drive cycle’s velocity and acceleration trajectory will be met, calculate the force required at the wheels, and then work backward to the resulting engine speed, and the necessary throttle and brake commands. Forward-looking models choose throttle and brake commands in order to achieve the specified trace. Some models use a combination of both strategies (see, e.g., Markel et al., 2002).

Modeling can have the potential benefit of helping one to understand these synergies and better predict future performance, either through the careful analysis of available vehicle data, or through creating dynamic models of the vehicles and analyzing the performance of these virtual vehicles. In addition to the synergies within various subsystems of the vehicle, many subsystems within the vehicle exhibit nonlinear behavior. Considering the performance of individual subsystems independently, even if this performance is well known and understood, can therefore result in misleading conclusions for the overall system. When an understanding of each subsystem can be represented by a computer model to an appropriate level of detail, and the interconnectivity or physical communication between each of these subsystems can also be adequately represented, the synergistic and nonlinear effects can be included and analyzed in the behavior of the entire system. Computer modeling of vehicle systems is widely used in the industry for this purpose, as well as to help predict future performance or performance under various conditions. Manufacturers use FSS in the product development process to optimize factors such as shift logic and final drive ratio.

For new technologies not implemented in any mass-produced vehicle, FSS model results are probably the most reliable source of estimates of synergistic effects. Historically, the PDA approach has generally not been used for estimating the fuel consumption impacts of novel vehicle systems for which there are no actual test data (Greene and DeCicco, 2000). Today FSS modeling is more widely used to estimate the potential for reducing fuel consumption than even 5 years ago. A number of studies are available that have used FSS to estimate the fuel consumption impacts of advanced technologies (e.g., Ricardo, Inc., 2008a,b, 2009; Kasseris and Heywood, 2007; Kromer and Heywood, 2007; Sierra Research, 2008). It should be noted, however, that sufficient knowledge of the technology package being investigated is necessary to allow its representation within the model to have an acceptable degree of accuracy. For an ag-

Suggested Citation:"8 Modeling Improvements in Vehicle Fuel Consumption." National Research Council. 2011. Assessment of Fuel Economy Technologies for Light-Duty Vehicles. Washington, DC: The National Academies Press. doi: 10.17226/12924.
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gregate technology, this may take the form of a performance map describing its efficiency over a range of operating conditions. For a technology described by unique operation of an existing subcomponent, relevant performance insight in the corresponding new regime of operation would be necessary.

It is important to note that, although FSS models have the ability to estimate the absolute impacts of vehicle technologies due to their ability to model the physics of system components, they have limited ability to model the dynamic working of individual fuel efficiency technologies and generally rely on a limited set of input data. For novel technologies, many of the input parameters are assumptions based on engineering judgment and experience with related technologies. This emphasizes the fact stated at the outset of this chapter, that one cannot know with absolutely accuracy the impact of technologies until an actual vehicle is constructed and repeatedly tested.

Model Fidelity

An important consideration for FSS modeling is deciding what level of fidelity of the equations or look-up tables is required for the problem being addressed. No set of equations completely reflects the detailed physics of the actual process, so the choice of fidelity should be a conscious choice from a continuum of models of varying fidelity, all of which represent simplifications of the actual process. The objective is to achieve an appropriate balance of fidelity with modeling goals, modeling effort and resources, simulation speed, and available data that specifically characterizes the system being modeled. There is always a difference between the simulation and actual subsystem operation, known as the modeling error. The tolerable level of error depends upon the goals of the simulation.

Unfortunately, data on the predictive accuracy of FSS models are scarce. In part this is because some models and more often the representation of their components are proprietary to firms that use them in their own research or consulting. The committee is not aware of any rigorous study evaluating the accuracy of models for various applications. The few comparisons the committee has seen indicate that for known vehicles, simulation models can reproduce fuel consumption and performance with a high degree of accuracy. Data provided by Ricardo, Inc., based on its research for the EPA indicated a range of error in predicting fuel consumption of 1 to 3 percent for five vehicles (Figure 8.4). For this modeling, the EPA chose a specific representative vehicle for each of the five classes: the Toyota Camry for the standard car, the Saturn Vue for the small MPV, the Chrysler 300 for the full-size car, the Dodge Grand Caravan for the large MPV, and the Ford F150 for the truck. Ricardo, Inc., (2008a) attributed any discrepancies between the simulation results and the actual vehicle data to the use of generic input data for that vehicle class instead of actual data for a specific vehicle. Of course, these are known vehicles so that component representations and the overall model can be calibrated. Prediction errors for truly novel technologies for which no vehicle exists to calibrate to would presumably be larger. In any event, it is the change in fuel consumption from the implementation of a technology that is of most interest. The absolute error of a predicted change can be smaller when prediction errors similarly exist in both the “before” and “after” simulations (i.e., the modeling errors of the before and after cases are strongly correlated). Still, relative errors for a predicted change are likely to be greater. The accuracy of FSS models in predicting fuel consumption changes in actual vehicles deserves additional study. Note that such an accuracy study is made more difficult by the fact that the

FIGURE 8.4 Comparison of actual vehicle combined fuel economies and Ricardo simulated fuel economies for five vehicles. SOURCE: Ricardo, Inc. (2008a).

FIGURE 8.4 Comparison of actual vehicle combined fuel economies and Ricardo simulated fuel economies for five vehicles. SOURCE: Ricardo, Inc. (2008a).

Suggested Citation:"8 Modeling Improvements in Vehicle Fuel Consumption." National Research Council. 2011. Assessment of Fuel Economy Technologies for Light-Duty Vehicles. Washington, DC: The National Academies Press. doi: 10.17226/12924.
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accuracy of FSS estimations depends significantly on the experience and skill of the FSS practitioner.

The flexibility, rigor, and comprehensiveness of the FSS approach to vehicle modeling are significant advantages. Subsystem models may be as simple as a single parameter or table based on steady-state operation, or a detailed, nonlinear, multivariable representation of the dynamics of the subsystem, including transient operation. The choice of how to represent each subsystem model is not only based on modeling error considerations discussed above, but also on balancing fidelity between subsystem models, in order to use computational resources as effectively as possible. One way of looking at balancing fidelity between subsystems is to consider the filtering properties or bandwidth of these subsystems. If one subsystem model has a level of fidelity that generates details in an output variable that are filtered out by a subsequent system, then the effort in generating those details is mostly wasted if the intermediate variables between the subsystems are not of interest. This balance of fidelity within an overall FSS model is a judgment call that is typically developed through experience or trial and error, although the effects can be clearly seen by looking carefully at the content of the variables that are passed between subsystems to see what effects are preserved or eliminated.

An example of these considerations can be seen by examining a typical system model of a turbocharger. In many dynamic system models, the characteristics of both the turbocharger compressor and turbine are simulated based on steady-state maps. However, the rotational dynamics of the rotor is simulated based on Newton’s second law (i.e., a differential equation reflecting dynamic or transient operation). The rationale for choosing and combining these two different types of models is based on the idea that the time constants for the gas dynamics in the compressor and turbine are considerably shorter (i.e., faster) than the time constant of the rotor. If much more detailed dynamic models of the gas dynamics were included in the model when the rotational speed of the rotor is the desired output variable, almost all of the gas dynamic effects would be filtered out by the rotor inertia or rotational bandwidth. This combination of steady-state and dynamic models to represent the turbocharger usually provides an effective dynamic model of its rotational dynamics and transient operation in relation to the rest of the engine. However, if the goal is to capture the pulsed gas dynamics in the turbine or compressor, this choice of subsystem models may not be appropriate (for that specific goal). The important point is that more detail is not necessarily better, but fidelity and balance should be conscious decisions reflecting modeling goals.

Model Validation

An effective way of carrying out model validation, given available data on the system operation, is to subdivide the data into at least two sets covering different operating conditions. One set of data can be used to determine parameters or tune the subsystem models, and a separate and distinct set of data can be used to test the predictive capabilities of the model in different situations after it has been tuned or calibrated. The model should not be tested using the same set of data that was used to calibrate the model.

FSS Model Example

An example of an FSS compression-engine model is illustrated in Figure 8.5 in order to give the reader a better visual idea of a possible subdivision of subsystems within the overall system model, as well as possible choices of fidelity within each subsystem. The overall goal of this model is to represent engine transient performance within the vehicle power train, including cylinder-by-cylinder rotational dynamic effects as well as first order intake and exhaust dynamics that affect turbocharger transient effects on the engine. Some simple emission transient predictive capability is included but is not comprehensive for all constituents.

This model was developed using the MATLAB/Simulink modeling software, and its overall structure is presented by the block diagram structure of MATLAB/Simulink in hierarchical form. Most of the subsystem models are identified for the reader. The core of the model is the engine map that provides brake-specific fuel consumption as a function of engine speed and load. Numerous other modules are necessary to represent the many interacting components of the engine system. Most of these components must be calibrated to the specific engine system of interest.

AN ANALYSIS OF SYNERGISTIC EFFECTS AMONG TECHNOLOGIES USING FULL SYSTEM SIMULATION

At the request of the committee, Ricardo, Inc. (2009) undertook a study to quantify the synergistic effects captured by FSS models. It is important to note that the study is based solely on the predictions of Ricardo, Inc.’s FSS models and therefore can quantify only the synergies those models can represent. In its report, Ricardo estimated the accuracy of its models for predicting fuel economy at 1 percent for well-characterized vehicle systems (systems for which nearly all model subsystems have been calibrated to the actual components) and 3 percent for novel vehicle systems. However, each estimate of accuracy was based on a single data point and so cannot be considered definitive.

Ricardo’s approach was to simulate the technologies contained in five different packages of technologies it had previously modeled for the EPA (2008a) as applied to five different types of vehicles. The technologies were applied one at a time and in combinations according to a rigorously defined design of experiments. The results were then fitted by a response surface model using a neural network method. The response surface model fit the data with maximum errors of 1 percent using terms no higher than second order (Figure 8.6).

Suggested Citation:"8 Modeling Improvements in Vehicle Fuel Consumption." National Research Council. 2011. Assessment of Fuel Economy Technologies for Light-Duty Vehicles. Washington, DC: The National Academies Press. doi: 10.17226/12924.
×
FIGURE 8.5 An example structure for a full system simulation diesel engine dynamic model. SOURCE: Reprinted with permission from John J. Moskwa, Powertrain Control Research Laboratory, University of Wisconsin, Madison, Wisc.

FIGURE 8.5 An example structure for a full system simulation diesel engine dynamic model. SOURCE: Reprinted with permission from John J. Moskwa, Powertrain Control Research Laboratory, University of Wisconsin, Madison, Wisc.

Suggested Citation:"8 Modeling Improvements in Vehicle Fuel Consumption." National Research Council. 2011. Assessment of Fuel Economy Technologies for Light-Duty Vehicles. Washington, DC: The National Academies Press. doi: 10.17226/12924.
×
FIGURE 8.6 Ricardo, Inc., statistical (response surface model [RSM]) predictions versus full system simulation model predictions. SOURCE: Ricardo, Inc. (2009), Figure 3-2.

FIGURE 8.6 Ricardo, Inc., statistical (response surface model [RSM]) predictions versus full system simulation model predictions. SOURCE: Ricardo, Inc. (2009), Figure 3-2.

This shows that a relatively simple 2nd order regression model provides a very suitable representation of the more complex vehicle simulation output with maximum RSM (Response Surface Model, ed.) residual errors of about 1 percent, or that higher order effects (3rd order and above) account for less than 1 percent of the vehicle simulation output characteristics. (Ricardo, Inc., 2009, p. 13)

This finding is significant in that it indicates that important synergistic effects (as represented by the FSS models) are of no higher order than two-way interactions. It is also generally consistent with the ability of a much simpler lumped parameter model to accurately estimate fuel economy over the federal test cycles with Sovran and Blaser (2006).

The next step was to carry out an analysis of variance (ANOVA) to quantify the first-order (main) and second-order effects. The ANOVA estimated that main effects of technologies accounted for 80 to 86 percent of the fuel economy increase. Interaction effects, taken together, accounted for 14 to 20 percent. Ricardo, Inc., concluded that simplified models that did not properly account for interaction effects could have estimation errors of up to 20 percent. However, 20 percent not only is the upper bound on estimation error but also assumes that the error in estimating interaction effects is 100 percent (for example, if they were all estimated to be 0). Interaction effects estimated using lumped parameter models, for example, are likely to be much smaller.

Even more importantly, the interaction effects include second-order main effects and incremental effects. Second-order main effects represent the interaction of a technology with itself and are introduced to account for nonlinear effects in the linear ANOVA model. Thus, they do not depend on the presence or absence of other technologies and so are not synergies in the sense that is of interest. Incremental effects include some true synergistic effects and some purely incremental effects. Purely incremental effects reflect the fact that when technologies are applied in sequence the fuel consumption impact of a technology depends on which technologies have been previously applied. For example, given a base vehicle with a 4-speed transmission, the impact of a 6-speed transmission will be smaller if a 5-speed transmission has been previously applied. The PDA method explicitly recognizes this kind of interaction by ordering technologies for interaction and using only incremental impacts, given that ordering, to estimate the total fuel consumption impact. But incremental effects, as defined in the Ricardo ANOVA, also include true synergistic effects, such as when a 42-volt electrical system is implemented together with electric accessories (e.g., electric power steering). Most PDA modelers attempt to take such interactions into account, but the accuracy with which they do so will depend on the available data sources and the engineering judgment of the analyst.

There are additional synergies of interest that Ricardo terms “inter-tree” or “true” synergies. These are the interactions among technologies that are neither second-order main effects nor incremental effects. PDA modeling cannot, in general, account for this type of synergy. According to the results of Ricardo’s study, these effects are quite small. For example, adding up the synergy (inter-tree) effects for Small MPV Package 5 (allowing positive and negative effects to cancel) results in a total synergy effect of − 1.3 percent of the total fuel economy impact of the technology package. Adding up the inter-tree synergies produces a positive synergy of 4.6 percent for Small MPV Package 15, a positive synergy of 2.8 percent for Large MPV Package 16, and a positive synergy of 10.3 percent of the total fuel economy impact for Truck Package 11. These are percentages of the total fuel economy change and so suggest that errors due to completely ignoring inter-tree synergies are on the order of 10 percent or less for the total fuel economy impact. The size of these effects is roughly consistent with the discrepancies EPA (2008b) found in its comparison of lumped parameter and FSS modeling.

Ricardo, Inc. (2009) concluded that PDA modeling, such as that used in the NHTSA’s Volpe model, if informed by rigorously designed FSS modeling of the kind represented in its study, can produce accurate estimates of fuel consumption reduction potential. This conclusion, however, is conditional on the accuracy of FSS models for predicting EPA test cycle fuel economy. Given the scarcity of evidence on this subject and its importance to validating Ricardo’s conclusion, it merits further investigation.

FINDINGS

Finding 8.1: The state of the art in estimating the impacts of fuel economy technologies on vehicle fuel consumption is full system simulation (FSS) because it is based on integration of the equations of motion for the vehicle carried out over the speed-time representation of the appropriate driving or test cycle. Done well, FSS can provide an accurate

Suggested Citation:"8 Modeling Improvements in Vehicle Fuel Consumption." National Research Council. 2011. Assessment of Fuel Economy Technologies for Light-Duty Vehicles. Washington, DC: The National Academies Press. doi: 10.17226/12924.
×

assessment (within +/−5 percent or less) of the impacts on fuel consumption of implementing one or more technologies. The validity of FSS modeling depends on the accuracy of representations of system components (e.g., engine maps). Expert judgment is also required at many points (e.g., determining engine warm-up rates or engine control strategies) and is critical to obtaining accurate results.


Finding 8.2: The partial discrete approximation (PDA) method relies on other sources of data for estimates of the impacts of fuel economy technologies. Unlike FSS, the PDA method cannot be used to generate estimates of the impacts of individual technologies on vehicle fuel consumption. Thus, the PDA method by itself, unlike FSS, is not suitable for estimating the impacts on fuel consumption of technologies that have not already been tested in actual vehicles or whose fuel consumption benefits have not been estimated by means of FSS. Likewise, the effects of technology interactions must be determined from external estimates or approximated by a method such as lumped parameter modeling. Even FSS, however, depends directly on externally generated information on the performance of individual technology components.


Finding 8.3: Comparisons of FSS modeling and PDA estimation (within the range of cases where the PDA method is applicable) supported by lumped parameter modeling to eliminate double counting of energy efficiency improvements have shown that the two methods produce similar results when similar assumptions are used. In some instances, comparing the estimates made by the two methods has enhanced the overall validity of estimated fuel consumption impacts by uncovering inadvertent errors in one or the other method. In the committee’s judgment both methods are valuable, especially when used together, one providing a check on the other. However, more work needs to be done to establish the accuracy of both methods relative to actual motor vehicles. In particular, the accuracy of applying class-specific estimates of fuel consumption impacts to individual vehicle configurations needs to be investigated. The magnitude of the errors produced when such estimates are aggregated to calculate the potential of individual automobile manufacturers to reduce fuel consumption should also be analyzed.


Finding 8.4: The U.S. Department of Transportation’s Volpe National Transportation Systems Center has developed a model for the NHTSA to estimate how manufacturers can comply with fuel economy regulations by applying additional fuel savings technologies to the vehicles they plan to produce. The model employs a PDA algorithm that includes estimates of the effects of technology synergies. The validity of the Volpe model, and probably also the OMEGA model, could be improved by making use of main effects and interaction effects produced by the FSS methodology described in this chapter. In particular, research done for the committee has demonstrated a practical method for using data generated by FSS models to accurately assess the fuel consumption potentials of combinations of dozens of technologies on thousands of vehicle configurations. A design-of-experiments statistical analysis of FSS model runs demonstrated that main effects and first-order interaction effects alone could predict FSS model outputs with an R2 of better than 0.99. Using such an approach could appropriately combine the strengths of both the FSS and the PDA modeling methods. However, in Chapter 9 the committee recommends an alternate approach that would use FSS to better assess the contributory effects of technologies applied for the reduction of vehicle energy losses and to better couple the modeling of fuel economy technologies to the testing of such technologies on production vehicles.

REFERENCES

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DOE/EIA (U.S. Department of Energy/Energy Information Administration). 2007. Transportation sector module of the National Energy Modeling System: Model documentation 2007, DOE/EIA-M070(2007). Office of Integrated Analysis and Forecasting, Washington, D.C.

DOT (U.S. Department of Transportation). 2005. CAFE compliance and effects modeling system. Volpe Systems Center, Cambridge, Mass., July 19.

DOT/NHTSA (U.S. Department of Transportation/National Highway Traffic Safety Administration). 2009. Average fuel economy standards, passenger cars and light trucks, model year 2011: Final rule, RIN 2127 AK-29, Docket No. NHTSA 2009-0062, Washington, D.C., March 23.

Duleep, K.G. 2008. EEA-ICF analysis update, Presentation to the Committee on Technologies for Improving Light-Duty Vehicle Fuel Economy, April 1, Washington, D.C.

EEA (Energy and Environmental Analysis, Inc.). 2007. Technologies to improve light-duty vehicle fuel economy, Draft report to the National Research Council Committee on Fuel Economy of Light-Duty Vehicles, Arlington, Va., September.

EPA (U.S. Environmental Protection Agency). 2008a. EPA Staff Technical Report: Cost and Effectiveness Estimates of Technologies Used to Reduce Light-Duty Vehicle Carbon Dioxide Emissions. EPA420-R-08-008, Ann Arbor, Mich.

EPA. 2008b. EPA’s technical review of Ricardo simulations, Presentation to the Committee on Technologies for Improving Light-Duty Vehicle Fuel Economy, March 31, 2008, Detroit, Mich.

EPA and DOT (U.S. Environmental Protection Agency and U.S. Department of Transportation). 2009. Proposed Rulemaking to Establish Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards. August 24. Washington, D.C.

EPA and NHTSA (U.S. Environmental Protection Agency and National Highway Traffic Safety Administration). 2009. Draft Joint Technical Support Document: Proposed Rulemaking to Establish Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards, EPA-420-D-09-901, September.

Greene, D.L., and J. DeCicco. 2000. Engineering-economic analysis of automotive fuel economy potential in the United States. Annual Review of Energy and the Environment 25:477-536.

Hancock, D. 2007. Assessing fuel economy. Presentation to the Committee on Fuel Economy of Light-Duty Vehicles, National Research Council, September 10, Washington, D.C.

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Suggested Citation:"8 Modeling Improvements in Vehicle Fuel Consumption." National Research Council. 2011. Assessment of Fuel Economy Technologies for Light-Duty Vehicles. Washington, DC: The National Academies Press. doi: 10.17226/12924.
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Kromer, M.A., and J.B. Heywood. 2007. Electric powertrains: opportunities and challenges in the U.S. light-duty vehicle fleet. Publication No. LFEE 2007-02 RP. Sloan Automotive Laboratory, Massachusetts Institute of Technology, Cambridge, Mass.

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Moskwa, J. 2008. Powertrain Control Research Laboratory, University of Wisconsin, Madison, Wisc.

NESCCAF (Northeast States Center for a Clean Air Future). 2004. Reducing Greenhouse Gas Emissions from Light-Duty Motor Vehicles. March.

NRC (National Research Council). 2002. Effectiveness and Impact of Corporate Average Fuel Economy Standards. National Academy Press, Washington, D.C.

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Ricardo, Inc. 2008a. A Study of Potential Effectiveness of Carbon Dioxide Reducing Vehicle Technologies. Prepared for the U.S. Environmental Protection Agency, EPA420-R-08-004, Contract No. EP-C-06-003, Work Assignment No. 1-14, Ann Arbor, Mich.

Ricardo, Inc. 2008b. A study of potential effectiveness of carbon dioxide reducing vehicle technologies. Presentation to the Committee on Fuel Economy of Light-Duty Vehicles, National Research Council, January 24.

Ricardo, Inc. 2009. A Study of Interaction Effects Between Light Duty Vehicle Technologies. Prepared for the NRC Committee on Assessment of Technologies for Improving Light-Duty Vehicle Fuel Economy by Ricardo Inc., Van Buren, Mich., February 27.

Rousseau, A. 2007. Designing advanced vehicle powertrains using PSAT. Presentation to the Committee on Fuel Economy of Light-Duty Vehicles, National Research Council, September 10.

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Sovran, G., and D. Blaser. 2003. A contribution to understanding automotive fuel economy and its limits. SAE Paper 2003-01-2070. SAE International, Warrendale, Pa.

Sovran, G., and D. Blaser. 2006. Quantifying the potential impacts of regenerative braking on a vehicle’s tractive-fuel consumption for the U.S., European, and Japanese driving schedules. SAE Paper 2006-01-0664. SAE International, Warrendale, Pa.

Sovran, G., and M.S. Bohn. 1981. Formulae for the tractive-energy requirements of vehicles driving the EPA schedules. SAE Paper 810184. SAE International, Warrendale, Pa.

Van Schalkwyk, J., W. Gazda, K. Green, D. Pickrell, and M. Shaulov. 2009. Corporate average fuel economy compliance and effects modeling system documentation. DOT HS 811 012. U.S. Department of Transportation, Research and Innovative Technology Administration, Energy Technology Division, John A. Volpe National Transportation Systems Center, Cambridge, Mass., April.

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Various combinations of commercially available technologies could greatly reduce fuel consumption in passenger cars, sport-utility vehicles, minivans, and other light-duty vehicles without compromising vehicle performance or safety. Assessment of Technologies for Improving Light Duty Vehicle Fuel Economy estimates the potential fuel savings and costs to consumers of available technology combinations for three types of engines: spark-ignition gasoline, compression-ignition diesel, and hybrid.

According to its estimates, adopting the full combination of improved technologies in medium and large cars and pickup trucks with spark-ignition engines could reduce fuel consumption by 29 percent at an additional cost of $2,200 to the consumer. Replacing spark-ignition engines with diesel engines and components would yield fuel savings of about 37 percent at an added cost of approximately $5,900 per vehicle, and replacing spark-ignition engines with hybrid engines and components would reduce fuel consumption by 43 percent at an increase of $6,000 per vehicle.

The book focuses on fuel consumption--the amount of fuel consumed in a given driving distance--because energy savings are directly related to the amount of fuel used. In contrast, fuel economy measures how far a vehicle will travel with a gallon of fuel. Because fuel consumption data indicate money saved on fuel purchases and reductions in carbon dioxide emissions, the book finds that vehicle stickers should provide consumers with fuel consumption data in addition to fuel economy information.

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