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A project has been undertaken to optimize the engine control software calibration of a modern heavy-duty diesel engine for operation with gas-to-liquids (GTL) diesel fuel, with the objective of developing an understanding of the scope for optimization with this fuel, which has different physical and combustion properties to that of conventional, crude-derived diesel. A data-driven, model-based calibration technique utilizing artificial neural networks was used to develop optimized transient and steady-state calibrations with both conventional diesel fuel, as well as neat GTL fuel. The engine control parameters that were optimized were injection timing, exhaust gas recirculation rate, rail pressure, and charge mass. The optimization aimed to minimize fuel consumption without deterioration in engine-out nitrogen oxide (NOx) and soot emissions. This paper reports on the calibration optimization methodology employed and the results achieved to date.

Speed-time and video data were logged for tractor-trailers performing local deliveries in Akron, OH. and Richmond, VA. in order to develop an emissions test schedule that represented real truck use. The data bank developed using these logging techniques was used to create a Yard cycle, a Freeway cycle and a City-Suburban cycle by the concatenation of microtrips. The City-Suburban driving cycle was converted to a driving route, in which the truck under test would perform at maximum acceleration during certain portions of the test schedule. This new route was used to characterize the emissions of a 1982 Ford tractor with a Cummins 14 liter, 350 hp engine and a 1998 International tractor with a Cummins 14 liter, 435 hp engine. Emissions levels were found to be repeatable with one driver and the driver-to-driver variation of NOx was under 4%, although the driver-to-driver variations of CO and PM were higher.

Pending GHG emissions reduction legislation for medium- and heavy-duty vehicles will require the development of engines and powertrains with significantly increased mechanical and electronic complexity. Increasing powertrain efficiency will require the simulation, control and calibration of an expanding number of highly interdependent air, fuel, exhaust, combustion and energy transfer subsystems. As a result of these increases in complexity, engine and powertrain control is becoming significantly more sophisticated and costly to develop and difficult to optimize. The high cost of developing engines and powertrain systems that demonstrate greater fuel efficiency and emissions benefits than the engines of today, is undeniable. The increased calibration burden and the complexity of optimization require the development and adoption of entirely new methods for transient engine calibration and optimization to achieve maximum vehicle fuel efficiency and lowest regulated emissions.