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With the conclusion of the California Air Resources Board (CARB) Stage 1 Ultra-Low NOX (ULN) program, there continues to be a commitment for identifying potential pathways to demonstrate 0.02 g/bhp-hr NOX emissions. The Stage 1 program focused on achieving the ULN levels on the heavy-duty regulatory cycles utilizing a turbo-compound engine which required the integration of novel catalyst technologies and a supplemental heat source. While the aftertreatment configuration provided a potential solution to meet the ULN target, a complicated approach with a greenhouse gas (GHG) penalty was required to overcome challenges from low temperature exhaust. A subsequent Stage 2 program was concerned with the development of a new low load test cycle and evaluating the trade-off between GHG and tailpipe NOX on the Stage 1 ULN solution.

The effects of downspeeding and supercharging a passenger car diesel engine were studied through laboratory investigation and vehicle simulation. Changes in the engine operating range, transmission gearing, and shift schedule resulted in improved fuel consumption relative to the baseline turbocharged vehicle while maintaining performance and drivability metrics. A shift schedule optimization technique resulted in fuel economy gains of up to 12% along with a corresponding reduction in transmission shift frequency of up to 55% relative to the baseline turbocharged configuration. First gear acceleration, top gear passing, and 0-60 mph acceleration of the baseline turbocharged vehicle were retained for the downsped supercharged configuration.

This paper explores the possibility of on-board fuel classification for fuel property adaptive compression-ignition engine control system. The fuel classifier is designed to on-board classify the fuel that a diesel engine is running, including alternative and renewable fuels such as bio-diesel. Based on this classification, the key fuel properties are provided to the engine control system for optimal control of in-cylinder combustion and exhaust treatment system management with respect to the fuel. The fuel classifier employs engine input-output response characteristics measured from standard engine sensors to classify the fuel. For proof-of-concept purposes, engine input-output responses were measured for three different fuels at three different engine operating conditions. Two neural-network-based fuel classifiers were developed for different classification scenarios. Of the three engine operating conditions tested, two conditions were selected for the fuel classifier to be active.