Fuel efficiency optimization and emission reduction have become essential parts of engine research, due to the growing demand for environmental protection. In this paper, a computationally efficient optimization method has been applied to maximize the fuel efficiency under constraints of maximum pressure rise rate and various pollutant emissions using the combination of multiple regression analysis and particle swarm optimization. This optimization method has been applied to a Reactivity Controlled Compression Ignition (RCCI) engine. First, a data-driven model has been identified, which shows good agreement with experimental data for gIMEP as well as emissions. Using this RCCI model in the proposed optimization method, the optimal operating conditions for highest gross indicated thermal efficiency are determined under various conflicting emission and safety constraints.
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