Artificial neural network–based performance modeling of a diesel engine within the whole operating region considering dynamic conditions

Engine performance under full working conditions, especially dynamic ones, is indispensable in many vehicle-level research fields. To acquire the engine performance parameters, a novel whole-region engine model, considering both steady and dynamic conditions, was developed based on limited test data in this work. This model used throttle position, engine speed, and its acceleration as the input variables to predict torque and brake-specific fuel consumption under all practical conditions within its operating envelope. The engine bench test was first conducted under typical operating conditions to collect test data for model development and validation. Then, the backpropagation neural network with designed structure was employed to perform data fitting for test conditions. After the analysis of parameter distribution tendency, the two-step interpolation method was used to generalize performance parameters under conditions apart from those test ones. The cross-condition prediction accuracy of developed engine model was validated by test data under various operating conditions. Also, the parameter prediction error of proposed modeling method was lower compared to that of existing neural network methods, which further proved its applicability to dynamic engine modeling issues.

[1]  Subrata Bhowmik,et al.  Artificial Neural Network Prediction of Diesel Engine Performance and Emission Fueled With Diesel–Kerosene–Ethanol Blends: A Fuzzy-Based Optimization , 2017 .

[2]  G. Sakthivel,et al.  A neural network model for the prediction of compression ignition engine performance at different injection timings , 2016 .

[3]  Chuanlei Yang,et al.  Investigation of ANN and SVM based on limited samples for performance and emissions prediction of a CRDI-assisted marine diesel engine , 2017 .

[4]  P. Srinivasa Pai,et al.  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 , 2011 .

[5]  Hongji Wu,et al.  ANALYSIS OF KINEMATIC GEOMETRY ON FACE GRINDING PROCESS ON LAPPING MACHINES , 2002 .

[6]  Christopher J. Rutland,et al.  Modeling of a Turbocharged DI Diesel Engine Using Artificial Neural Networks , 2002 .

[7]  Adnan Parlak,et al.  Application of artificial neural network to predict specific fuel consumption and exhaust temperature for a Diesel engine , 2006 .

[8]  G. Vairamuthu,et al.  Experimental and artificial neural network based prediction of performance and emission characteristics of DI diesel engine using Calophyllum inophyllum methyl ester at different nozzle opening pressure , 2017 .

[9]  Adem Çiçek,et al.  Predictive modeling of performance of a helium charged Stirling engine using an artificial neural network , 2013 .

[10]  Qing-song Zuo,et al.  An artificial neural network developed for predicting of performance and emissions of a spark ignition engine fueled with butanol–gasoline blends , 2018 .

[11]  Subrata Bhowmik,et al.  A Comparative Study of Artificial Intelligence Based Models to Predict Performance and Emission Characteristics of a Single Cylinder Diesel Engine Fueled With Diesosenol , 2018 .

[12]  Gholamhassan Najafi,et al.  Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol- gasoline blends , 2010 .

[13]  Sedat Bayseç,et al.  Prediction of torque and specific fuel consumption of a gasoline engine by using artificial neural networks , 2010 .

[14]  Yusuf Çay,et al.  Prediction of a gasoline engine performance with artificial neural network , 2013 .

[15]  Yu Zhu-chun.  Universal Characteristic Experimental Data Fitting of a Certain Type of Engine Based on the BP Neural Network , 2012 .

[16]  Adem Çiçek,et al.  Prediction of engine performance for an alternative fuel using artificial neural network , 2012 .

[17]  Ouyang Minggao.  Modeling of engine control systems using assembled neural networks , 2005 .

[18]  G. A. Velázquez-Carrillo,et al.  Modeling engine fuel consumption and NOx with RBF neural network and MOPSO algorithm , 2015 .

[19]  Sumit Roy,et al.  Performance and exhaust emissions prediction of a CRDI assisted single cylinder diesel engine coupled with EGR using artificial neural network , 2014 .

[20]  Mahdi Shahbakhti,et al.  Performance prediction of HCCI engines with oxygenated fuels using artificial neural networks , 2015 .

[21]  Marc A. Rosen,et al.  Using GMDH Neural Networks to Model the Power and Torque of a Stirling Engine , 2015 .

[22]  Adem Çiçek,et al.  Prediction of engine performance and exhaust emissions for gasoline and methanol using artificial neural network , 2013 .

[23]  G. Nagarajan,et al.  Artificial neural network approach to predict the engine performance of fish oil biodiesel with diethyl ether using back propagation algorithm , 2016 .

[24]  P. Ravi Kumar,et al.  Performance and emission prediction of a tert butyl alcohol gasoline blended spark-ignition engine using artificial neural networks , 2015 .

[25]  B. B. V. L. Deepak,et al.  Application of GRNN for the prediction of performance and exhaust emissions in HCCI engine using ethanol , 2016 .

[26]  Hwai Chyuan Ong,et al.  Experimental study and prediction of the performance and exhaust emissions of mixed Jatropha curcas-Ceiba pentandra biodiesel blends in diesel engine using artificial neural networks , 2017, Journal of Cleaner Production.

[27]  Erfu Yang,et al.  Bubble density gradient with laser detection: A wake-homing scheme for supercavitating vehicles , 2018, Advances in Mechanical Engineering.