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This paper reports on DaimlerChrysler's participation in the Ad Hoc Diesel Fuels Test Program. This program was initiated by the U.S. Department of Energy and included major U.S. auto makers, major U.S. oil companies, and the Department of Energy. The purpose of this program was to identify diesel fuels and fuel properties that could facilitate the successful use of compression ignition engines in passenger cars and light-duty trucks in the United States at Tier 2 and LEV II tailpipe emissions standards. This portion of the program focused on minimizing engine-out particulates and NOx by using selected fuels, (not a matrix of fuel properties,) in steady state dynamometer tests on a modern, direct injection, common rail diesel engine.

Variable Valve Actuation (VVA) technology provides high potential in achieving high performance, low fuel consumption and pollutant reduction. However, more degrees of freedom impose a big challenge for engine characterization and calibration. In this study, a simulation based approach and optimization framework is proposed to optimize the setpoints of multiple independent control variables. Since solving an optimization problem typically requires hundreds of function evaluations, a direct use of the high-fidelity simulation tool leads to the unbearably long computational time. Hence, the Artificial Neural Networks (ANN) are trained with high-fidelity simulation results and used as surrogate models, representing engine's response to different control variable combinations with greatly reduced computational time. To demonstrate the proposed methodology, the cam-phasing strategy at Wide Open Throttle (WOT) is optimized for a dual-independent Variable Valve Timing (VVT) engine.

Cam-phasing is increasingly considered as a feasible Variable Valve Timing (VVT) technology for production engines. Additional independent control variables in a dual-independent VVT engine increase the complexity of the system, and achieving its full benefit depends critically on devising an optimum control strategy. A traditional approach relying on hardware experiments to generate set-point maps for all independent control variables leads to an exponential increase in the number of required tests and prohibitive cost. Instead, this work formulates the task of defining actuator set-points as an optimization problem. In our previous study, an optimization framework was developed and demonstrated with the objective of maximizing torque at full load. This study extends the technique and uses the optimization framework to minimize fuel consumption of a VVT engine at part load.

Current engine development processes typically involve extensive steady-state and simple transient testing in order to characterize the engine's fuel consumption, emissions, and performance based on several controllable inputs such as throttle, spark advance, and EGR. Steady-state and simple transient testing using idealistic load conditions alone, however, is no longer sufficient to meet powertrain development schedule requirements. Mapping and calibration of an engine under transient operation has become critically important. And, independent engine development utilizing accelerated techniques is becoming more attractive. In order to thoroughly calibrate new engines in accelerated fashion and under realistic transient conditions, more advanced testing is necessary.

The Automotive Industry/Government Emissions Research CRADA (AIGER) has been working to develop a new methodology for the direct determination of nonmethane hydrocarbons (DNMHC) in vehicle testing. This new measurement technique avoids the need for subtraction of a separately determined methane value from the total hydrocarbon measurement as is presently required by the Code of Federal Regulations. This paper will cover the historical aspects of the development program, which was initiated in 1993 and concluded in 2002. A fast, gas chromatographic (GC) column technology was selected and developed for the measurement of the nonmethane hydrocarbons directly, without any interference or correction being caused by the co-presence of sample methane. This new methodology chromatographically separates the methane from the nonmethane hydrocarbons, and then measures both the methane and the backflushed, total nonmethane hydrocarbons using standard flame ionization detection (FID).

With the adoption of the California Low-Emission Vehicle Regulations and the associated lower emission standards such as LEV (Low-Emission Vehicle in 1990), ULEV (Ultra-Low-Emission Vehicle), and LEV II (1998 with SULEV-Super Ultra Low Emission Vehicle), concerns were raised by emissions researchers over the accuracy and reliability of collecting and analyzing emissions measurements at such low levels. The primary concerns were water condensation, optimizing dilution ratios, and elimination of background contamination. These concerns prompted a multi-year research program looking at several new sampling techniques. This paper will describe the cooperative research conducted into one of these new technologies, namely the Bag Mini-Diluter (BMD) and Direct Vehicle Exhaust (DVE) Volume system.

As SULEV emission regulations approach, the bag mini-diluter (BMD) technology is gaining acceptance as a replacement for the existing constant volume sampler (CVS) for SULEV exhaust emission measurement and certification. The heart of the BMD system is the direct vehicle exhaust (DVE) flow measurement system. Due to the transient nature of vehicle exhaust during a standard FTP emission test cycle, the DVE must be capable of rapid and accurate response in order to track these varying exhaust flow rates. The DVE must also be robust enough to accurately measure flow rate despite variations in exhaust gas composition, pulsation effects, and rapid changes in both exhaust temperature and pressure. One of the primary DVE systems used on BMDs is the E-Flow, an ultrasonic flow meter manufactured by Flow Technologies, Inc.

It is becoming increasingly difficult to obtain good repeatability from running lab vehicle correlation testing, since vehicle variability is so significant at the Low ULEV and SULEV emissions levels. These new emission standards are becoming so stringent that it makes it very difficult to distinguish whether a problem is a result of vehicle variability, test cell sampling or the analytical system. A vehicle exhaust emission simulator (VEES) developed by Horiba, can simulate emissions from low emitting gasoline vehicles by producing tailpipe flow rates containing emissions constituents ( HC, CH4, CO, NOx, CO2 ) injected at the tailpipe flow stream via mass flow controllers.

As the standards for exhaust emissions have become more stringent, the quality control tools used to evaluate the performance of low level samplers and analyzers has become more important. The Vehicle Exhaust Emissions Simulator (VEES) was developed to evaluate the performance of vehicle or engine exhaust emissions sampling and analytical systems. The simulator emulates emissions from low-emitting gasoline vehicles by producing a simulated exhaust stream containing emission constituents (HC, CO, CO2, and NOx) injected via Mass Flow Controllers (MFCs). This paper discusses various applications of the VEES as a quality control tool for ULEV and SULEV testing. A comparison is made between the injected amount of exhaust species by the VEES and the amounts recovered by the different sampling systems. Different root cause scenarios are discussed as to the source of discrepancies between the results on the CVS and BMD for different driving cycles.

A predictive automatic gear shift system is currently under development. The system optimizes the gear shift process, taking the conditions of the road ahead into account, such that the fuel consumption is minimized. An essential part of the system is a module that predicts the vehicle speed dynamics: This calculates a speed trajectory, i.e. the most probable vehicle speed the driver will desire for the upcoming section of the route. In the paper the theoretical background for predicting the vehicle speed, and simulation results of the predictive shift algorithm are presented.