부산대학교 도서관

To optimize combustion efficiency, it is often desired to operate the engine as close to its borderline knock as possible. However, detecting borderline knock is a time-consuming process through an engine mapping process. This paper applies a machine learning algorithm, namely the stochastic Bayesian optimization, to efficiently detect borderline knock based on a tradeoff relationship between knock intensity and fuel economy, considering both system and measurement noises. A dual-surrogate model structure, along with a distribution mapping process, is proposed for implementing the Bayesian iterative optimization with two competing objectives (knock intensity and indicated specific fuel consumption) and two control inputs (spark timing and intake valve timing). The proposed algorithm is validated by running the engine bench test with a pre-defined test budget. Finally, the optimized control parameters are found based on trained surrogate models and guarantee that the engine runs right below the borderline knock with the best fuel economy possible.