An Experimental Study on Emissions Optimization Using Micro-Genetic Algorithms in a HSDI Diesel Engine

Current automotive diesel engine research is motivated by the need to meet more-and-more strict emission regulations. The major target for future HSDI combustion research and development is to find the most effective ways of reducing the soot particulate and NOx emissions to the levels required by future emission regulations. Recently, a variety of statistical optimization tools have been proposed to optimize engine-operating conditions for emissions reduction. In this study, a micro-genetic algorithm technique, which locates a global optimum via the law of “the survival of the fittest”, was applied to a high-speed, direct-injection, single-cylinder (HSDI) diesel engine. The engine operating condition considered single-injection operation using a common-rail fuel injection system was at 1757 rev/min and 45% load. The engine parameters controlled in the optimization were the injection pressure (ranging from 80 to 110 MPa), the boost pressure (from 138 to 207 kPa), start-of-injection timing (from -10 to 5 degrees ATDC), and cooled Exhaust Gas Recirculation percentage (from 10 to 35%). The optimum results showed significant improvements for the NOx and particulate emissions. The baseline NOx and particulate emissions were 3.75 g/kW-hr and 0.52 g/kW-hr, respectively, and the optimized NOx and particulate emissions were reduced to 0.48 g/kW-hr (87% reduction) and 0.12 g/kW-hr (77% reduction). Moreover, the final optimized results met emissions targets, which were derived from the EPA Tier II 2004 automotive diesel mandates.