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Engine Knock Evaluation Using a Machine Learning Approach
Technical Paper
2020-24-0005
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Artificial Intelligence is becoming very important and useful in several scientific fields. Machine learning methods, such as neural networks and decision trees, are often proposed in applications for internal combustion engines as virtual sensors, faults diagnosis systems and engine performance optimization. The high pressure of the intake air coupled with the demand of lean conditions, in order to reduce emissions, have often close relationship with the knock events. Fuels autoignition characteristics and flame front speed have a significant impact on knock phenomenon, producing high internal cylinder pressures and engine faults.
The limitations in using pressure sensors in the racing field and the challenge to reduce the costs of commercial cars, push the replacement of a hardware redundancy with a software redundancy. Therefore, it becomes strategically important to develop a robust predictive model that, using the physical properties such as air temperature and pressure, fuel consumption and engine speed, could increase the engine performance under a large range of operating conditions, without computational efforts.
In this paper, three machine learning approaches were implemented to predict the knock onset and knock intensity of a SI engine. The tool is fed by several input variables coming from a CFD-1D engine model whose calibration has been performed by using experimental data. Input parameters influencing the knock phenomenon, such as engine speed, air-fuel ratio, max internal cylinder pressure, combustion timing, and physical air conditions in the plenum, have been used as dataset for training and test phases. Once trained, the machine learning models were tested on their ability to predict outputs based on samples not used during the training set. The outputs predicted were compared with the target ones and the accuracy of the model was evaluated in terms of RMS and R2.
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petrucci, L., Ricci, F., Mariani, F., Cruccolini, V. et al., "Engine Knock Evaluation Using a Machine Learning Approach," SAE Technical Paper 2020-24-0005, 2020, https://doi.org/10.4271/2020-24-0005.Data Sets - Support Documents
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References
- Mao, B., Chen, P., Liu, H., Zheng, Z. et al. , “Gasoline Compression Ignition Operation on a Multi-Cylinder Heavy Duty Diesel Engine,” Fuel 215(Aug. 2017):339–3 51, 2018.
- Alger, T., Gingrich, J., Roberts, C., and Mangold, B. , “A High Energy Continuous Discharge Ignition System for Dilute Engine Applications,” SAE Technical Paper 2013-01-1628 , 2013, https://doi.org/10.4271/2013-01-1628.
- Heywood, J.B. , Internal Combustion Engine Fundamentals (McGraw-Hill, 1988), ISBN:0-07-028637-X.
- Goldwitz, J.A., and Heywood, J.B. , “Combustion Optimization in a Hydrogen-Enhanced Lean-Burn SI Engine,” SAE Technical Paper 2005-01-0251 , 2005, https://doi.org/10.4271/2005-01-0251.
- Aleiferis, P.G., Taylor, A.M.K.P., Ishii, K., and Urata, Y. , “The Nature of Early Flame Development in a Lean-Burn Stratified-Charge Spark-Ignition Engine,” Combustion and Flame 136(3):283–3 02, 2004.
- Tully, E.J., and Heywood, J.B. , “Lean-Burn Characteristics of a Gasoline Engine Enriched with Hydrogen Plasmatron Fuel Reformer,” SAE Technical Paper 2003-01-0630 , 2003, https://doi.org/10.4271/2003-01-0630.
- Cruccolini, V., Discepoli, G., Ricci, F., Petrucci, L. et al. , “Comparative Analysis between a Barrier Discharge Igniter and a Streamer-Type Radio-Frequency Corona Igniter in an Optically Accessible Engine in Lean Operating Conditions,” SAE Technical Paper 2020-01-0276 , 2020, https://doi.org/10.4271/2020-01-0276.
- Francqueville, L., and Michel, J.-B. , “On the Effects of EGR on Spark-Ignited Gasoline Combustion at High Load,” SAE Int. J. Engines 7(4):1808–1 823, 2014, https://doi.org/10.4271/2014-01-2628.
- Ricci, F., Zembi, J., Battistoni, M., Grimaldi, C. et al. , “Experimental and Numerical Investigations of the Early Flame Development Produced by a Corona Igniter,” 2019.
- Gu, W., Zhao, D., Member, S., and Mason, B. , “Real-Time Modelling and Parallel Optimisation of a Gasoline Direct Injection Engine,” 2019, 5544–5 549.
- Cruccolini, V., Discepoli, G., Cimarello, A., Battistoni, M. et al. , “Lean Combustion Analysis Using a Corona Discharge Igniter in an Optical Engine Fueled with Methane and a Hydrogen-Methane Blend,” Fuel 259:116290, 2020.
- Discepoli, G., Cruccolini, V., Ricci, F., Di Giuseppe, A. et al. , “Experimental Characterisation of the Thermal Energy Released by a Radio-Frequency Corona Igniter in Nitrogen and Air,” Appl. Energy 263(Feb.):114617, 2020.
- Fiifi, R., Yan, F., Kamal, M., Ali, A. et al. , “Applications in the Calibration of Spark-Ignition Engines: An Overview,” Eng. Sci. Technol. Int. J. Artificial Neural Network 19(3):1346–1 359, 2016.
- Suzuki, K. (Ed.), Artificial Neural Networks - Industrial and Control Engineering.
- Shamekhi, A., and Shamekhi, A.H. , “Expert Systems with Applications: A New Approach in Improvement of Mean Value Models for Spark Ignition Engines Using Neural Networks,” Expert Syst. Appl. 42(12):5192–5 218, 2015.
- Grimaldi, C.N. and Mariani, F. , “On Line Working Neural Estimator of SI Engines Operational Parameters,” 2000.
- Petrucci, L. et al. , “Performance Analysis of Artificial Neural Networks for Control in Internal Combustion Engines.”
- Zhai, Y., and Ã, D.Y. , “Engineering Applications of Artificial Intelligence Neural Network Model-Based Automotive Engine Air/Fuel Ratio Control and Robustness Evaluation,” 22:171–1 80, 2009.
- Atkinson, C.M. , “Virtual Sensing: A Neural Network-Based Intelligent Performance and Emissions Prediction System for On-Board Diagnostics and Engine Control,” Feb. 1998, 2017.
- Hafner, M., Schu, M., Nelles, O., and Isermann, R. , “Fast Neural Networks for Diesel Engine Control Design,” 8:1211–1 221, 2000.
- Nikzadfar, K., and Shamekhi, A.H. , “Investigating a New Model-Based Calibration Procedure for Optimizing the Emissions and Performance of a Turbocharged Diesel Engine,” Fuel 242(Aug. 2017):455–4 69, 2019.
- Gölc, M., Sekmen, Y., Erduranli, P., and Salman, M.S. , “Artificial Neural-Network Based Modeling of Variable Valve-Timing in a Spark-Ignition Engine,” Applied Energy 81(2):187–1 97, 2005, https://doi.org/10.1016/j.apenergy.2004.07.008.
- Fiifi, R., Yan, F., Kamal, M., Ali, A. et al. , “Applications in the Calibration of Spark-Ignition Engines: An Overview,” Eng. Sci. Technol. Int. J. Artificial Neural Network 19(3):1346–1 359, 2016.
- Pai, P.S., and Rao, B.R.S. , “Artificial Neural Network Based Prediction of Performance and Emission Characteristics of a Variable Compression Ratio CI Engine Using WCO as a Biodiesel at different Injection Timings,” Appl. Energy 88(7):2344–2 354, 2011.
- Grimaldi, C.N., Mariani, F., and Perugia, U. , “Prediction of Engine Operational Parameters for on Board Diagnostics Using a Free Model Technology,” 1999.
- Ravaglioli, V., and Bussi, C. , “Model-Based Pre-Ignition Diagnostics in a Race Car Application,” Energies 12(12):2277, 2019, https://doi.org/10.3390/en12122277.
- Nareid, H., Grimes, M., and Verdejo, J. , “A Neural Network Based Methodology,” 724:2005, 2019.
- Stiebels, B., Schreiber, M., and Sakak, A.S. , “Development of a New Measurement Technique for the Investigation of End-Gas Autoignition and Engine Knock,” SAE Technical Paper 960827 , 1996, 1996, https://doi.org/10.4271/960827.
- Mariani, F., Grimaldi, C.N., and Battistoni, M. , “Diesel Engine NOx Emissions Control: An Advanced Method for the O2 Evaluation in the Intake Flow,” Appl. Energy 113:576–5 88, 2014.
- Capriglione, D., Liguori, C., Pianese, C., and Pietrosanto, A. , “On-Line Sensor Fault Detection, Isolation, and Accommodation in Automotive Engines,” 52(4):1182–1 189, 2003.
- Grimaldi, C.N. and Mariani, F. , “OBD Engine Fault Detection Using a Neural Approach,” 2001.
- Cowart, J., Haghgooie, M., Newman, C., Davis, G. et al. , “The Intensity of Knock in an Internal Combustion Engine: An Experimental and Modeling Study,” SAE Technical Paper 922327 , 1992, https://doi.org/10.4271/922327.
- Kalghatgi, G., Algunaibet, I., and Morganti, K. , “On Knock Intensity and Superknock in SI Engines,” SAE Int. J. Engines 10(3):1051–1 063, 2017, https://doi.org/10.4271/2017-01-0689.
- Chevillard, S., Colin, O., Bohbot, J., Wang, M. et al. , “Advanced Methodology to Investigate Knock for Downsized Gasoline Direct Injection Engine Using 3D RANS Simulations,” SAE Technical Paper 2017-01-0579 , 2017, https://doi.org/10.4271/2017-01-0579.
- Corti, E., and Forte, C. , “Statistical Analysis of Indicating Parameters for Knock Detection Purposes,” SAE Technical Paper 2009-01-0237 , 2009, https://doi.org/10.4271/2009-01-0237.
- Guzzella, L., and Ag, S.W. , “IC-Engine Downsizing and Pressure-Wave Supercharging for Fuel Economy,” SAE Technical Paper 2000-01-1019 , 2020, https://doi.org/10.4271/2000-01-1019.
- Battistoni, M., Grimaldi, C.N., Cruccolini, V., Discepoli, G. et al. , “Assessment of Port Water Injection Strategies to Control Knock in a GDI Engine through Multi-Cycle CFD Simulations,” SAE Technical Paper 2017-24-0034 , 2017, https://doi.org/10.4271/2017-24-0034.
- Bozza, F., De Bellis, V., and Teodosio, L. , “Potentials of Cooled EGR and Water Injection for Knock Resistance and Fuel Consumption Improvements of Gasoline Engines,” Appl. Energy 169:112–1 25, 2016.
- Merola, S.S., Sementa, P., Tornatore, C., and Vaglieco, B.M. , “Effect of the Fuel Injection Strategy on the Combustion Process in a PFI Boosted Spark-Ignition Engine,” Energy 35(2):1094–1 100, 2010.
- Doornbos, G., Hemdal, S., Denbratt, I., and Dahl, D. , “Knock Phenomena under Very Lean Conditions in Gasoline Powered SI-Engines,” SAE Int. J. Engines 11(1):3–1 1, 2018, https://doi.org/10.4271/03-11-01-0003.
- Merola, S.S., Sementa, P., Tornatore, C., and Vaglieco, B.M. , “Effect of the Fuel Injection Strategy on the Combustion Process in a PFI Boosted Spark-Ignition Engine,” Energy 35(2):1094–1 100, 2010.
- Tornatore, C., Marchitto, L., Valentino, G., Corcione, F.E. et al. , “Optical Diagnostics of the Combustion Process in a PFI SI Boosted Engine Fueled with Butanol e Gasoline Blend,” Energy 45(1):277–2 87, 2012.
- Wu, B., Prucka, R.G., Filipi, Z.S., Kramer, D.M. et al. , “Cam-Phasing Optimization Using Artificial Neural Networks as Surrogate Models—Fuel Consumption and NOx Emissions,” 2006(724), 2019.
- Steurs, K., Blomberg, C.K., and Boulouchos, K. , “Knock in an Ethanol Fueled Spark Ignition Engine: Detection Methods with Cycle-Statistical Analysis and Predictions Using Different Auto-Ignition Models,” SAE Int. J. Engines 7(2):568–5 83, 2014, https://doi.org/10.4271/2014-01-1215.
- Hudson, C., Gao, X., and Stone, R. , “Knock Measurement for Fuel Evaluation in Spark Ignition Engines,” 80:395–4 07, 2001.
- Heywood, J.B. , Internal Combustion Engine Fundamentals (McGraw-Hill, 1988), ISBN:0-07-028637-X.
- https://www.fia.com/sites/default/files/technical_regulations_lmph_2020_-_merge_v8_2019.10.23.pdf.
- Kalghatgi, G.T., Golombok, M., and Snowdon, P. , “Fuel Effects on Knock, Heat Release and “CARS” Temperatures in a Spark Ignition Engine,” Combustion Science and Technology, 1995, doi:10.1080/00102209508951924.
- Shah, N., Zhao, P., Delvescovo, D., and Ge, H. , “Prediction of Autoignition and Flame Properties for Multicomponent Fuels Using Machine Learning Techniques,” SAE Technical Paper 2019-01-1049 , 2019, https://doi.org/10.4271/2019-01-1049.
- Çay, Y., Çiçek, A., Kara, F., and Saǧiroǧlu, S. , “Prediction of Engine Performance for an Alternative Fuel Using Artificial Neural Network,” Applied Thermal Engineering 37:217–2 25, 2012, https://doi.org/10.1016/j.applthermaleng.2011.11.019.