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Development and Assessment of Machine-Learning-Based Intake Air Charge Prediction Models for a CNG Engine
ISSN: 0148-7191, e-ISSN: 2688-3627
Published March 29, 2022 by SAE International in United States
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Based on the sample data obtained from the bench test of a four-cylinder naturally aspirated CNG engine, three different machine learning models, BP, SVM and GRNN, were used to develop the intake charge prediction model for the intake system of this engine, in which engine speed, intake manifold pressure and intake temperature, VVT angle and gas injection time were taken as input parameters and intake charge was used as output parameter. The comparative analysis of the experimental data and model prediction data showed that the mean absolute error (MAE) of BP model, GRNN model, and SVM model were 2.69, 8.11and 5.13, and the root mean square error (MSE) were 3.53, 9.29, and 7.17, respectively. BP model has smaller prediction error and higher accuracy than SVM and GRNN models, which is more suitable for the prediction of the intake charge of this type of four-cylinder naturally aspirated CNG engine.
CitationZhang, P., Ni, J., and Shi, X., "Development and Assessment of Machine-Learning-Based Intake Air Charge Prediction Models for a CNG Engine," SAE Technical Paper 2022-01-0166, 2022, https://doi.org/10.4271/2022-01-0166.
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