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A micro-genetic algorithm (μGA) optimization method was applied to a heavy-duty, direct-injected diesel engine via an automated test bed system. The goal of this application was to demonstrate the feasibility and advantages of automated optimization experiments. With the genetic algorithm, no user input was required other than the factors of interest and their allowable ranges. This means that once the routine was initiated, it was essentially run undisturbed until a preset objective level was reached or a preset number of generations had been run. The automated μGA was successfully demonstrated at all points of the six-mode transient cycle simulation, excluding idle. To accomplish the automated experiments, an automated testing system was developed around a Caterpillar single-cylinder diesel engine.

Optimizations were performed on a single-cylinder heavy-duty Caterpillar SCOTE 3401E engine for NOx, PM and BSFC reductions. The engine was equipped with a Caterpillar 300B HEUI fuel injection system capable of up to four injections with timings from 90 BTDC to 90 ATDC. The engine was operated at a medium load (57%), high speed (1737 rev/min) operation point. A micro-genetic algorithm was utilized to optimize a hybrid, double-injection strategy, which incorporated an early, premixed pilot injection with a late main injection. The fuel injection parameters, intake boost pressure, and EGR were considered in the optimization. The optimization produced a parameter set that met the 2007 and 2010 PM emissions mandate of 0.0134 g/kW-hr, and was within the 1.5x not to exceed NOx + HC mandate of 2.694 g/kW-hr. Following the optimization exercise, further parametric interaction studies were performed to reveal the underlying interactions and phenomena.

Multiple injection strategies have been experimentally and computationally studied for simultaneously reducing diesel engine NOx and particulate emissions. However, injection strategies featuring three or more pulses per engine cycle have not been sufficiently studied previously. The large number of parameters to be considered, in addition to the complicated interactions among them, challenge the capability of experimental hardware, computational models, and optimization methods. In the present work, multiple injection strategies including up to five pulses per engine cycle, are computationally investigated to optimize High Speed Direct Injection (HSDI) diesel engine combustion and emissions at a single part-load operating condition. The KIVA-3V code coupled with a Genetic Algorithm were used as the modeling and optimization tools, respectively. It was found that widely separated injection with two-stage combustion appears to provide optimal HSDI diesel performance at part load.