Torque characteristic of SI engine in dynamic operating states

• Autorzy: Bera P.

Identyfikatory

DOI

10.19206/CE-2017-429

• Języki publikacji: EN

Abstrakty

EN

The article presents torque characteristic of the engine in dynamic operating conditions as a function of engine speed and throttle opening angle. All mentioned parameters are analyzed as independent variables over time. To develop such a characteristic an artificial neural network is used. The training data were obtained from measurements carried out on the test bench on SI engine. The operating states reflect all possible configurations of these parameters, which may occur during use of the vehicle in real traffic conditions. The article shows design of an artificial neural network that allows to designate the required dependences. Moreover, it describes the fit of the model to the measurement data, which clearly indicates its correctness. Then the developed characteristic in dynamic states is compared with the characteristic in static working states. The differences between them for selected cases of engine operation states are presented. It shows the versatility of the presented method.

Słowa kluczowe

EN

engine torque; dynamic states; engine characteristics; artificial neural network

PL

moment obrotowy silnika; stany dynamiczne; charakterystyka silnika; sztuczne sieci neuronowe

• Wydawca: Polskie Towarzystwo Naukowe Silników Spalinowych
• Czasopismo: Combustion Engines
• Rocznik: 2017
• Tom: R. 56, nr 4
• Strony: 175--180
• Opis fizyczny: Bibliogr. 8 poz., wykr.

Twórcy

autor

Bera P.

• AGH University of Science and Technology, Faculty of Mechanical Engineering and Robotics

Bibliografia

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• [2] BERA, P. Fuel consumption analysis in dynamic states of the engine with use of artificial neural network. Combustion Engines. 2013, 162(4), 726-731.
• [3] BERA, P. The use of artificial neural networks trained in supervised mode to the analysis of measurement data of combustion engines and automotive vehicles. Silniki Spalinowe i Ekologia. 2014, Cracow University of Technology Press, 193-204.
• [4] ISERMANN, R. Engine modeling and control. Modeling and Electronic Management of Internal Combustion Engines. Springer. 2014.
• [5] KANG, M., ALAMIR, M., SHEN, T. Nonlinear constrained torque control for gasoline engines. IFAC-PapersOnLine. 2016, 49-18, 784-789.
• [6] SERIKOV, S.A. Neural network model of internal combustion engine. Cybernetics and System Analysis. 2010, 46(6), 998-1007.
• [7] TOGUN, N.K., BAYSEC, S. Prediction of torque and specific fuel consumption of a gasoline engine by using artificial neural networks. Applied Energy. 2010, 87, 349-355.
• [8] YIN, X., GE, A. A dynamic model of engine using neural network description. Vehicle Electronics Conference. 2001.