Neuro-fuzzy model tree approach to virtual sensing of transient diesel soot and NOx emissions

Abstract

Diesel engine combustion and emission formation are highly nonlinear and thus create a challenge related to engine diagnostics and engine control with emission feedback. This article describes the development of neuro-fuzzy models for prediction of transient NOX and soot emission from a diesel engine. The modeling techniques are motivated by the idea of divide and conquer the input–output space. The complex problem is divided into multiple simpler subproblems, which are then identified using simpler class of models. This article explores two different choices of local models, specifically polynomial and neural networks. The modeling technique is augmented with input relevance algorithm to select the most relevant input regressors. Two algorithms, namely, orthogonal least square and automatic relevance determination, are introduced. The models are data driven, and an advanced experimental setup incorporating a medium duty diesel engine and fast emission analyzers for soot and NOX is used to generate training data. The choice of local models and input relevance algorithm is validated with instantaneous emission recorded during transient schedules different from those used in development. High prediction accuracy, both qualitatively and quantitatively, is demonstrated with low computational cost.

Investigation of a Diesel-Engined Vehicle’s Performance and Emissions during the WLTC Driving Cycle—Comparison with the NEDC

“…In parallel, Kirchen et al [12] developed a mean value model for soot and showed the effectiveness with tip-in operations employing empirical correlations between engine-out emissions and engine operating conditions. Furthermore, artificial neural networks have been employed by various researchers (e.g., [13,14]) to determine transient emissions based on steady-state test results used to generate the training data. On the other hand, a different prediction approach was followed by Bishop et al [15], who, based on data collected from on-board diagnostics (OBD) and portable emissions measurement systems (PEMS) when driving under real-world conditions, created fuel consumption and emission engine maps of real-world driving.…”

Analysis of the Effect of Vehicle, Driving and Road Parameters on the Transient Performance and Emissions of a Turbocharged Truck

“…As was the case with other similar mappingbased approaches developed in the past [10][11][12][13][14][18][19][20][21][22][23], the context of quasi-linear modeling [24] is, in general, followed.…”

A review of soot sensors considered for on-board diagnostics application

“…The soot emission values at points within a free of data space in the torque–speed map are interpolated by various mathematical models including the local linear regression and the neuro-fuzzy models. 27,28 Under a transient engine operating condition, the soot emission predicted from the map for a steady-state condition has to be modified, so that the spiky higher soot emission due to a short-term oxygen deficit could be taken into account.…”