Abstract
For avoiding knock occurrence in SI engines, spark timing is retarded whenever the knock has occurred which leads to a loss of thermal efficiency. Therefore, the knock occurrence needs to be properly controlled. For doing that, knock should preemptively be predicted and controlled. Prerequisite data for knock prediction modelling is a knock onset position, which can be figured out by finding the starting point of the oscillation on pressure data. A deep learning knock onset determination model was developed in a previous study, and showed the highest accuracy among the comparable methods, the model showed weak robustness on knock cycles obtained in different engine experiments. Meanwhile, the 1D CNN model has been widely used in signal processing fields with its advantage of having a feature extraction layer, and the model is introduced in this study for determining the knock onset. Dataset from four different engine types were used for verifying the model accuracy and robustness. The dataset was augmented by calculation windows for producing various data with limited data sources. Hyperparameters of the model were optimized with random search. The accuracy standard deviation following engine types in terms of RMSE was improved by 77.4 % from 0.827 CA to 0.187 CA.
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Acknowledgement
This research was the results of a study on the “HPC Support” Project, supported by the ‘Ministry of Science and ICT’ and NIPA. The test facility was supported by the Advanced Automotive Research Center (AARC) and the Institute of Advanced Machinery and Design (IAMD) of Seoul National University (SNU). The authors would like to express our deep gratitude to Professor Jo Seok-won from Mississippi State University for his significant help and support in acquiring engine experimental data and writing this paper.
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Park, J., Shin, S., Oh, S. et al. Knock Onset Determination with 1D CNN Using Random Search Hyperparameter Optimization and Data Augmentation in SI Engine. Int.J Automot. Technol. 24, 1395–1410 (2023). https://doi.org/10.1007/s12239-023-0113-7
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DOI: https://doi.org/10.1007/s12239-023-0113-7