Although many studies have been carried out using control-oriented engine models for advanced combustion technologies, methods of adapting model parameters for these models have still not been considerably studied. In this paper, we propose a method to adapt model parameters for an ignition model and a combustion model of a physics-based discrete diesel engine model using neural networks. Using experimental data on an engine bench as well, we examined how to perform adapting and training, such as optimization algorithms, how to select training data, appropriate feature values, and algorithms for switching the neural networks according to the operating regions. As a result, this adaptation method was able to improve the prediction accuracy of the heat release rate of pre-combustion better than when adapted using the previous adaptation method and when the model parameters were fixed.

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