This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Development and Assessment of Machine-Learning-Based Intake Air Charge Prediction Models for a CNG Engine
Technical Paper
2022-01-0166
ISSN: 0148-7191, e-ISSN: 2688-3627
Annotation ability available
Sector:
Language:
English
Abstract
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.
Citation
Zhang, 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.Also In
References
- Yiming , L. , Yongquan , C. Zhiyuan , H. Research on Application of Air-Fuel Ratio Control of CNG Engine Based on Neural-Network Predictive Algorithm Small Internal Combustion Engine and Motorcycle 2012 41 6 49 49
- Prasada Rao , K. , Victor Babu , T. , Anuradha , G. , and Appa Rao , B.V. IDI Diesel Engine Performance and Exhaust Emission Analysis Using Biodiesel with an Artificial Neural Network (ANN) Egyptian Journal of Petroleum 26 2017 2016 593 600 10.1016/j.ejpe.2016.08.006
- Aida , D.-S. , Ratta , G.A. , and Barrios , C.C. Prediction of Exhaust Emission in Transient Conditions of a Diesel Engine Fueled with Animal Fat using Artificial Neural Network and Symbolic Regression Energy 149 2018 675 683 10.1016/j.energy.2018.02.080
- Xingwang , X. , Fujian , H. , Lubo , W. et al. Simulation Research on Intake Flow of Turbocharged CNG Engine AUTO SCI-TECH 02 2015 05 09 10.3969/j.issn.1005-2550.2015.02.002
- Tian , Z.D. , Ren , Y. , and Wang , G. Short-Term Wind Power Prediction Based on Empirical Mode Decomposition and Improved Extreme Learning Machine Journal of Electrical Engineering & Technology 13 5 2018 1841 1851
- Ning , S. Short-Term Prediction of the CSI 300 Based on the BP Neural Network Model Journal of Physics: Conference Series. 2019 10.1088/1742-6596/1437/1/012054
- Jiangping , H. , Haojun , W. et al. Image Recognition Based on Support Vector Machine Journal of Chongqing University (Natural Science Edition) 29 1 2006 57 60
- Yanyun , Z. and Anni , H. Fingerprint Image Segmentation Method using Support Vector Machine Journal of Beijing University of Posts and Telecommunications. 29 2 2006 38 41
- Chiranjeeva , R. A GRNN based Frame Work to Test the Influence of Nano Zinc Additive Biodiesel Blends on CI Engine Performance and Emissions 27 2017 641 647
- Xuejun , W. Research on Optimizing BP Neural Network Based on PSO-EO Science Technology and Engineering. 10 24 2010 6047 6049
- Jinfang , Y. , Yongjie , Z. , Dongfeng , W. , and Daping , X. Prediction Based on Support Vector Regression Series Proceedings of The Chinese Society for Electrical Engineering. 25 17 2005 110 114