Dynamic Mapping of Diesel Engine through System Identification

• From a control design point of view, modern diesel engines are dynamic, nonlinear, MIMO systems. This paper presents a method to find low-complexity black-box dynamic models suitable for model predictive control (MPC) of NOx 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, NOx 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.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Maskinteknik -- Annan maskinteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Mechanical Engineering -- Other Mechanical Engineering (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)

Keyword

Diesel engines

System identification

Wiener models

Publication and Content Type

kon (subject category)

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