Model Description and Results for the EEA-ICF Model
The lumped parameter approach to fuel consumption modeling uses the same basic principles as all simulation models, but instead of calculating fuel consumption second by second, as is sometimes done, it uses an average cycle. Such an approach has been used widely by industry and regulatory agencies, most recently by the U.S. Environ mental Protection Agency (EPA) to help assess the 2012-2016 proposed fuel economy standards (EPA, 2008). The method can be generally described as a first-principles-based energy balance, which accounts for all the different categories of energy loss, including the following:
Losses based on the second law of thermodynamics,
Heat loss from the combusted gases to the exhaust and coolant,
Mechanical friction loss,
Vehicle road load tire and aerodynamic drag losses, and
Vehicle inertial energy lost to the brakes.
Conceptually, each technology improvement is characterized by the percent change to each of the loss categories. If multiple technologies are employed to reduce the same category of loss, each successive technology has a smaller impact as the category of loss becomes closer to zero.
EEA-ICF Inc.1 has developed a lumped parameter model that is broadly similar in scope and content to the EPA model (Duleep, 2007). In this model, all of the baseline vehicle energy losses are determined computationally, and many of the technology effects on each source of loss have been determined from data presented at technical conferences. However, the EPA does not document how the various losses were determined for the baseline vehicle: It says only that the vehicle has a fixed percentage of fuel lost to each category. The EPA also does not document how the technology-specific improvements in each category of loss were characterized. It appears that the losses for both the baseline vehicle and the effects of technology improvements were based not on computed values but on expert opinion.
Here the committee summarizes the EEA-ICF model. GM researchers Sovran and Bohn (1981) used numerical integration over the Federal Test Procedure city and highway driving cycles to determine the energy required at the wheel to move a vehicle over the driving cycle as a function of its weight, frontal area, drag coefficient, and tire rolling resistance coefficient. This procedure is used to compute the energy requirement at the wheel for the given baseline vehicle and translated to energy at the engine output shaft by using transmission and driveline efficiency factors (which differ by transmission type and number of gears) derived from the open literature. Accessory energy requirements are added as a fixed energy amount that is a function of engine size. This determines total engine output energy; average cycle power is then computed by distributing the energy over the cycle time when positive engine output is required—that is, the time spent at closed throttle braking and idle are accounted for separately. Average cycle RPM excluding idle was obtained for specific vehicles from simulation models on specific vehicles, and these data are scaled by the ratio of the N/V for the data vehicle and the baseline vehicle. The data are used to determine average brake mean effective pressure (BMEP) for the positive power portion of the cycle.
Fuel consumption is determined by the following relationship:
where I is for indicated, F is for friction, P is for pumping, and MEP is the mean effective pressure in each category. The fuel consumption model is derived from a methodology to estimate an engine map using a semiempirical model developed by researchers at Ford and the University of Nottingham (Shayler et al., 1999). In this formulation, fuel consumption is proportional to IMEP divided by indicated thermal efficiency (sometimes called the Willans line), friction is determined empirically from engine layout and is a function of RPM only, and PMEP is simply intake manifold pressure (atmospheric pressure). Intake manifold pressure is solved for any given BMEP, since IMEP is also proportional to intake pressure. This model explicitly derives thermal efficiency, friction loss, and pumping loss for the baseline vehicle. Fuel consumption at idle and closed throttle braking are modeled as functions of engine displacement only. The baseline engine is always modeled with fixed valve lift and timing, and the pumping loss is adjusted for the presence of variable valve timing if applicable. The model can be construed as a two-point approximation of a complete engine map and is a very reasonable representation of fuel consumption at light and moderate loads where there is no fuel enrichment.
The technologies are characterized by their effect on each of the losses explicitly accounted for in the model, and the representation is similar in concept to the representation in the EPA model. In the EEA-ICF analysis, the committee collected information on the effect of each engine technology on peak engine efficiency, pumping loss, and friction loss as a cycle average from technical papers that describe measured changes in these attributes from prototype or production systems. When these losses are not explicitly measured, they are computed from other published values such as the change in compression ratio, the change in torque, or the measured change in fuel consumption.
Comparison of Results to Detailed Simulation Model Outputs
Both EEA-ICF and EPA have compared the lumped parameter results with new full-scale simulation modeling results on several vehicle classes with different combinations of planned technological improvements. The simulations were done by the consulting firm Ricardo, Inc., and documented in a separate report (Ricardo, 2008). The Ricardo work modeled five baseline vehicles (standard car, large car, small MPV, large MPV, and large truck) and 26 technology combinations, covering gasoline and diesel power trains used in the EPA model, but there was no simulation of hybrids.
In a majority of the comparisons done by EPA, the lumped parameter model estimates were close to the Ricardo estimates, and the EPA concluded the results of their model were plausible, although a few technology packages required additional investigation. The EPA has indicated that it will continue to use the lumped parameter approach as an analytical tool, perhaps adjusting it to improve its fidelity as more simulation results become available.
EEA-ICF also performed analysis for the NRC Committee on Assessment of Technologies for Improving LightDuty Vehicle Fuel Economy (Duleep, 2008a, 2008b). Based on the committee’s experience, when a number of engine, transmission, and other technology improvements are simultaneously added to a baseline vehicle, the net fuel economy benefit can be approximated by taking 90 percent of the additive sum of the individual technology benefits, as developed by EEA-ICF. The committee used this technique to develop a quick approximation of the level of agreement likely between the Ricardo simulations and the EEA-ICF lumped parameter model. It was able to perform a quick analysis of only 23 of 26 packages developed by Ricardo, since there were no data on HCCI engines, which were used in three of the Ricardo technology packages.
Ricardo included one technology for which the committee had no specific data. It called this “fast warm-up” technology because it involved the control of coolant flow to the engine immediately after cold start. Based on the data presented by Ricardo, the benefit of the technology was estimated at 1 percent, including the benefit of the electric water pump. All other technology benefits were based on the data from ICF-EEA previous reports to DOE on fuel economy technology. These benefit estimates were adjusted for the presence or absence of technologies on the baseline vehicle, since all benefits in the DOE reports have been typically defined relative to an engine with fixed valve timing and a four-speed automatic transmission. The results are illustrated in Figure K.1, and the plot shows the difference between the Ricardo results and the quick approximation method.
In 16 of the 23 cases, the Ricardo estimate is within +5 percent of the quick estimate. In two cases, the Ricardo estimates were more than 10 percent lower than the quick estimates, as shown in Figure K.1. In five cases, the Ricardo estimates were 10 percent (or more) higher than the quick estimate. The difference implies that the benefits are larger than the simple sum of individual technology benefits and that technology synergies are positive. The committee also examined the technology packages in the two “low” and five “high” outliers. Both low outliers had technology packages with a continuously variable transmission (CVT) as one of the technologies. The five high outliers had no major technology improvement in common.
More detailed analysis was also done with the EEA-ICF lumped parameter model. Constraints on resources and time allowed the committee to analyze only 9 of the 23 cases with the lumped parameter model, but the 9 cases included both high and low outliers from the previous analysis. Three technology packages were analyzed for a standard car, which used a Toyota Camry baseline; three for a compact
van, which used a Chrysler Voyager baseline; and three for a standard pickup, which used a Ford F-150 baseline. Table K.1 shows the results and compares them with those of the quick method. The more detailed modeling reduced the average difference between the Ricardo estimates and the committee estimates for the Toyota Camry and the Chrysler compact van but increased the difference for the Ford F-150 truck. The largest observed difference is for Package 10 on the Ford, where the baseline 5.4-L V8 is replaced by a 3.6-L V6 turbo GDI engine and the downsizing is consistent with the 33 percent reduction that was used.
Comparison of Model Results to NRC Estimates
The NRC study has developed a series of technology paths whose combined effect on fuel consumption was estimated from expert inputs on the marginal benefits of each successive technology given technologies already adopted. Paths were specified for five different vehicles: small cars, intermediate/large cars, high-performance sedans, body-on-frame small trucks, and large trucks. There were no substantial differences in the paths or the resulting fuel consumption estimates across the five vehicles: All estimated decreases in fuel consumption were between 27 and 29 percent for
TABLE K.1 Comparison of Fuel Economy Improvements (in Percent) from Ricardo, Inc., Modeling, EEA-ICF Quick Analysis, and the EEA-ICF Model
spark-ignition engines and 36 and 40 percent for diesel engines. Since the “performance sedan” and intermediate sedan specifications were not very different, only the small car, one intermediate car, and two trucks were simulated. Simulation was done for the spark ignition engine and the diesel engine paths, but not for the hybrid path.
Table K.2 lists the model results versus the committee estimates for the eight cases (four for spark ignition and four for diesel). In general, the model forecasts are very close to but typically slightly lower than the forecasts of experts, although well within the range of uncertainty included in the committee estimate. Only one vehicle, the full-size truck, shows a larger difference on the diesel path. Historically, the committee’s method of forecasting the marginal benefit of technology along a specified path has been criticized as potentially leading to an overestimation of benefits for spark ignition engines since it could lead to infeasible solutions if total pumping loss reduction estimated exceeded the actual pumping loss. The simulation model output’s explicit tracking of the losses addresses this issue directly to ensure that no basic scientific relationships are violated.
Fuel consumption is decreased by reducing the tractive energy required to move the vehicle (by reducing weight, aerodynamic drag, or rolling resistance), reducing losses to the transmission and drive line, reducing accessory energy consumption, or reducing engine fuel consumption during idle and closed throttle braking. Fuel consumption can also be reduced by increasing engine efficiency over the cycle, which is accomplished by increasing peak efficiency or by reducing mechanical friction and pumping loss. Figures K.2 through K.5 show the technology path steps and track the reductions from both approaches separately, with the reduction in energy required to drive through the test cycle shown on top and the engine efficiency shown below. Peak engine efficiency actually decreases slightly due to turbocharging and downsizing, but the cycle efficiency increases from about 24 to 29 percent owing to reduction in pumping and friction loss (blue part of the bar). The general trends are very similar across all four vehicle types, but the key feature is that pumping and friction loss are not reduced to physically impossible levels for the solution.
TABLE K.2 Comparison of Fuel Consumption Reductions (in Percent) for NRC Estimates and the EEA-ICF Model
Duleep, K.G. 2007. Overview of lumped parameter model. Presentation to the National Research Council Committee for the Assessment of Technologies for Improving Light-Duty Vehicle Fuel Economy on October 26, Washington, D.C.
Duleep, K.G. 2008a. EEA-ICF Analysis of Ricardo simulation outputs. Presentation to the National Research Council Committee for the Assessment of Technologies for Improving Light-Duty Vehicle Fuel Economy on February 26, Washington, D.C.
Duleep, K.G. 2008b. EEA-ICF analysis update. Presentation to the National Research Council Committee for the Assessment of Technologies for Improving Light-Duty Vehicle Fuel Economy on April 1, Washington, D.C.
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.
Ricardo, Inc. 2008. A Study of the Potential Effectiveness of Carbon Dioxide Reducing Vehicle Technologies. Report to the Environmental Protection Agency. June 26.
Sovran, G., and M. Bohn, 1981. Formulae for the tractive energy requirements of the vehicles driving the EPA schedules. SAE Paper 810184. SAE International, Warrendale, Pa.
Shayler, P., J. Chick, and D. Eade. 1999. A method of predicting brake specific fuel consumption maps. SAE Paper 1999-01-0556. SAE International, Warrendale, Pa.