Optimization-oriented high fidelity NFIR models for estimating indicated torque in diesel engines

Alcan, Gökhan and Aran, Volkan and Ünel, Mustafa and Yilmaz, Metin and Gurel, Cetin and Koprubasi, Kerem (2020) Optimization-oriented high fidelity NFIR models for estimating indicated torque in diesel engines. International Journal of Automotive Technology, 21 (3). pp. 729-737. ISSN 1229-9138 (Print) 1976-3832 (Online)

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In this paper, optimization-oriented high fidelity indicated torque models which cover the whole operating regions under both steady-state and transient cycles for heavy-duty vehicles are developed. Two different experiments are performed and their data are merged to be utilized in the training of the models. In the first experiment, all combustion input channels are excited by quadratic chirp signals with different sweeps in their frequency profiles. Different from the first experiment, the engine speed is excited by ramp-hold signals in the second experiment. The estimations of friction, pumping and inertia torques in addition to the torque measured from the engine dynamometer are utilized in the indicated torque calculations. In order to model the calculated indicated torque, a nonlinear finite impulse response (NFIR) model with a single layer sigmoid neural network has been designed. A sensitivity analysis is performed by generating several models with different number of input regressors and neurons. Experimental results show that the majority of the models in a selected wide range of the model parameters are validated with fit accuracies higher than 90 % and 85 % on the World Harmonized Stationary Cycle (WHSC) and the World Harmonic Transient Cycle (WHTC), respectively.
Item Type: Article
Uncontrolled Keywords: Diesel engine; Experiment design; Indicated torque; NFIR model; System identification
Divisions: Faculty of Engineering and Natural Sciences
Integrated Manufacturing Technologies Research and Application Center
Depositing User: Mustafa Ünel
Date Deposited: 29 Jul 2023 23:21
Last Modified: 29 Jul 2023 23:21
URI: https://research.sabanciuniv.edu/id/eprint/46472

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