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