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New SI Engine Optimization Techniques
Published May 23, 2004 by Society of Automotive Engineers of Korea in South Korea
Two evolutionary optimization algorithms, Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), have been calibrated that allow, for the first time the inclusion of discontinuous design variables. The engine performance is evaluated using a quasi-dimensional engine predictive model with sub models to incorporate friction, heat losses and abnormal combustion such as knocking. The input variables considered for this investigation are manifold air pressure, air-fuel ratio, spark timing, compression ratio, valve timing events including valve open duration, maximum valve lift and engine speed. In addition, the effect of combustion chamber shape parameters and spark plug location, on the optimum engine performance, is also investigated. This enables the maximum thermal efficiency to deliver a given power output to be investigated. The potential benefits of rapid burning with enhanced flame speed such as HAJI system under the optimum conditions are also investigated. Results are compared with the optimum performance with stoichiometric mixture, experimental results as well as model prediction under reference engine's normal operating conditions.