Self-learning control algorithms used to manage the operating of an internal combustion engine

• Autorzy: Graba Mariusz , Mamala Jarosław , Bieniek Andrzej

Identyfikatory

DOI

10.2478/kones-2019-0092

• Języki publikacji: EN

Abstrakty

EN

The article presents the possibility of using self-learning control algorithms to manage subassemblies of an internal combustion engine in order to reduce exhaust emissions to the natural environment. In compression ignition (CI) engines, the issue of emissions mainly concerns two components: particulate matter (PM) and nitrogen oxides (NOx). The work focuses mainly on the possibility of reducing the emission of nitrogen oxides. It is assumed that the particularly problematic points when it comes to excessive emission of harmful substances are the dynamic states in which combustion engines operate constantly. In dynamically changing operating points, it is very difficult to choose the right setting of actuators such as the exhaust gas recirculation (EGR) valve to ensure the correct operation of the unit and the minimum emission of these substances. In the light of the above, an attempt was made to develop a selflearning mathematical model, which can predict estimated emission levels of selected substance basing on current measurement signals (e.g. air, pressure, crankshaft rotational speed, etc.). The article presents the results of the estimation of nitrogen oxides by the trained neural network in comparison to the values measured with the use of a sensor installed in the exhaust system. The presented levels of estimated and measured results are very similar to each other and shifted over time in favour of neural networks, where the information about the emission level appears much earlier. On the basis of the estimated level, it shall be possible to make an appropriate decision about specific settings of recirculation system components, such as the EGR valve. It is estimated that by using the chosen control method it is possible significantly to reduce the emission of harmful substances into the natural environment while maintaining dynamic properties of the engine.

Słowa kluczowe

EN

transport; simulation; combustion engines; environmental protection; neural network

• Wydawca: Łukasiewicz Research Network - Institute of Aviation ; European Science Society of Powertrain and Transport KONES Poland ; Wydawnictwo Instytutu Technicznego Wojsk Lotniczych
• Czasopismo: Journal of KONES
• Rocznik: 2019
• Tom: Vol. 26, No. 4
• Strony: 75--82
• Opis fizyczny: Bibliogr. 10 poz., rys.

Twórcy

autor

Graba Mariusz

• Opole University of Technology, Faculty of Mechanical Engineering Mikołajczyka Street 5, 45-271 Opole, Poland tel.: [phone], fax: [phone]

autor

Mamala Jarosław

• Opole University of Technology, Faculty of Mechanical Engineering Mikołajczyka Street 5, 45-271 Opole, Poland tel.: [phone], fax: [phone]

autor

Bieniek Andrzej

• Opole University of Technology, Faculty of Mechanical Engineering Mikołajczyka Street 5, 45-271 Opole, Poland tel.: [phone], fax: [phone]

Bibliografia

• [1] Andersson, H., Hedvall, M., Model Based Control of Air and EGR into a Diesel Engine, Göteborg, Sweden 2008.
• [2] Barszcz, T., Zastosowanie sieci neuronowych do klasyfikacji uszkodzeń maszyn wirujących, Diagnostyka, Nr 1 (37), 2006.
• [3] Bieniek, A., Mamala, J., Graba M., i in., Możliwości poprawy własności emisyjnych silnika o zapłonie samoczynnym przy zastosowaniu katalitycznego dodatku do paliwa, Combustion Engines, pp. 968-977, 2015.
• [4] Brzozowski, K., Nowakowski, J., Toksyczność spalin silnika o zapłonie samoczynnym w warunkach zmiennego obciążenia dla różnych wartości parametrów regulacyjnych, Eksploatacja i Niezawodność, 2011.
• [5] Ericson, C., Westerberg, B., Andersson, M., Egnell, R., Modelling diesel engine combustion and NOx formation for model based control and simulation of engine and exhaust aftertreatment systems, SAE International 2006-01-0687, 2006.
• [6] Ferreau, H. J., Ortner, P., Langthaler, P., del Re, L., Diehl, M., Predictive control of a real-world Diesel engine using an extended online active set strategy, Elsevier Annual Reviews in Control, Vol. 31, pp. 293-301, 2007.
• [7] Gheorghiu, V., Atkinson Cycle and Very High-Pressure Turbocharging for Increasing Internal Combustion Engines Efficiency and Power while Reducing Emissions, IMECE International Mechanical Engineering Congress & Exposition, Denver, USA 2011.
• [8] Graba, M., Identyfikacja systemu recyrkulacji spalin silnika o zapłonie samoczynnym, praca doktorska, Politechnika Opolska, Opole 2014.
• [9] Graba, M., Bieniek, A., i in., Zasilanie silników wysokoprężnych pojazdów pozadrogowych, Ofic. Wydaw. PO, 2012.
• [10] Hennek, K., Bieniek, A., Mamala, J., Graba, M., Nitrogen Oxides Emission Estimator for a Diesel Engine Use to Reduce the Emission of Harmful Substances in Exhaust Gas to Environment, Journal of KONES, Vol. 24, No. 3, pp. 87-94, 2017.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).