Application of Particle Swarm Optimization for Diesel Engine Performance Optimization

A particle swarm optimization (PSO) algorithm was implemented with engine testing in order to accelerate the engine development process. The PSO algorithm is a stochastic, population-based evolutionary optimization algorithm. In this study, PSO was used to reduce exhaust emissions while maintaining high fuel efficiency. A merit function was defined to help reduce multiple emissions simultaneously. Engine operations using both single-injection and double-injection strategies were optimized. The present PSO algorithm was found to be very effective in finding the favorable operating conditions for low emissions. The optimization usually took 40-70 experimental runs to find the most favorable operating conditions under the constraints specified in the present testing. High EGR levels, small pilot amount, and late main injection were suggested by the PSO. Multiple emissions were reduced simultaneously without a compromise in the brake specific fuel consumption. In a favorable case that produced low emissions in this study, the raw NOx and PM emissions were reduced to 0.41 and 0.0092 g/kW-h, respectively, with an operating condition of 34% EGR, 5 ATDC main SOI, −24 ATDC pilot SOI, and 5% pilot fuel.