Work

Parameter identification of a diesel oxidation catalyst model based on segmented adaptive variable forgetting factor recursive least squares

Year: 2026

Type: article

Abstract: Controlling outlet temperature during active diesel particulate filter regeneration requires accurate online identification of diesel oxidation catalyst parameters. The strong thermal inertia, nonlinear heat-transfer and reaction characteristics, and asymmetric dynamic responses of the diesel oxidation catalyst during heating and cooling make accurate model-parameter identification challenging. Fixed-forgetting-factor algorithm cannot balance rapid transient tracking and stable steady-state estimation, whereas standard variable-forgetting-factor algorithm use a single cycle wide adaptation law, neglecting regime-dependent thermodynamics. In this study, we develop a physics-constrained, control-oriented model of a diesel oxidation catalyst and propose a segmented adaptive variable-forgetting-factor recursive least-squares algorithm guided by catalyst thermodynamics. The outlet temperature derivative defines heating, cooling, and steady regimes; K-means clustering and coefficient-of-variation analysis quantitatively determine their thresholds. Regime-specific forgetting-factor ranges and prediction-error-based online adaptation balance transient tracking and steady regime noise suppression. Evaluation using engine bench data collected over the non-road transient cycle shows that the proposed algorithm achieves a coefficient of determination of 0.9994 and a root mean square error of 1.480 K. Compared with conventional variable and fixed-forgetting-factor algorithms, it reduces the root mean square error by 40.3% and 16.3%, respectively, and exhibits greater robustness to measurement noise. These results demonstrate that thermodynamically informed, regime-specific adaptation improves identification accuracy and transient tracking performance, providing a practical parameter-identification framework for model-based diesel engine aftertreatment control.

Source: Applied Thermal Engineering

DOI: 10.1016/j.applthermaleng.2026.133635