Abstract
From a control design point of view, modern diesel engines are dynamic, nonlinear, MIMO systems. This paper presents a method to find lowcomplexity black-box dynamic models suitable for model predictive control (MPC) of NO x and soot emissions based on on-line emissions measurements.
A four-input-five-output representation of the engine is considered, with fuel injection timing, fuel injection duration, exhaust gas recirculation (EGR) and variable geometry turbo (VGT) valve positions as inputs, and indicated mean effective pressure, combustion phasing, peak pressure derivative, NO x emissions, and soot emissions as outputs. Experimental data were collected on a six-cylinder heavy-duty engine at 30 operating points. The identification procedure starts by identifying local linear models at each operating point. To reduce the number of dynamic models necessary to describe the engine dynamics, Wiener models are introduced and a clustering algorithm is proposed. A resulting set of two to five dynamic models is shown to be able to predict all outputs at all operating points with good accuracy.
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Henningsson, M., Ekholm, K., Strandh, P., Tunestål, P., Johansson, R. (2012). Dynamic Mapping of Diesel Engine through System Identification. In: Alberer, D., Hjalmarsson, H., del Re, L. (eds) Identification for Automotive Systems. Lecture Notes in Control and Information Sciences, vol 418. Springer, London. https://doi.org/10.1007/978-1-4471-2221-0_13
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DOI: https://doi.org/10.1007/978-1-4471-2221-0_13
Publisher Name: Springer, London
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