Comparative analysis of different engine operating parameters for on-board fuel octane number classification

Ghanaati, Ali; Said, Mohd Farid Muhamad; Darus, Intan Zaurah Mat

doi:10.1016/j.applthermaleng.2017.06.013

Cited by 4 publications

(1 citation statement)

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“…The literature consists of numerous studies that have effectively applied ANN models to predict engine parameters such as volumetric efficiency, 9 after-treatment system performance, 10–12 fuel quality, 13,14 and engine performance quality regarding the combustion of substitute fuels. 15–17 ANNs have been used to describe non-linear relationships between input and output parameters such as the air/fuel ratio, 18 ignition timing, 19 and actuator control system.…”

Section: Introductionmentioning

confidence: 99%

Design of a virtual test cell based on GMDH-type neural network for a heavy-duty diesel engine

Ghanaati

Sjöblom

Faghani

2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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The engine development process faces big challenges from new strict emission regulations in addition to the need for fuel efficiency improvements. The Software-in-the-Loop (SiL) and Hardware-in-the-Loop (HiL) environments decreases the required time during engine development, calibration, verification, and validation of the product. An accurate and easy to build dyno-engine model with real-time operational ability is required for this purpose. Artificial Neural Networks (ANN) have shown ability to model dynamic and complex systems like internal combustion engines. In this paper, the Group Method of Data Handling (GMDH) algorithm was utilized to build an ANN model of a heavy-duty diesel engine. One objective is to reduce the amount of manual labor on the results during the ANN model development process. The GMDH algorithm is a self-organizing process that will find the system laws and optimize the model structure automatically in one iteration. The GMDH model results were compared with a model developed by Levenberg-Marquardt Backpropagation (LM-BP) algorithm. The ANN models used actuator signals from an Engine Management System (EMS) to simulate the engine operation parameters. As revealed by the simulation results, the ANN models successfully predicted engine torque, fuel flow, and NOx concentration. The GMDH model as a self-organized model reduced lead time, had slightly higher transient cycle accuracy, had fewer inconsistent predictions, and demonstrated better extrapolation capability. The prediction accuracy for transient operation was improved by shifting the predicted value by calculating time delay and a decrease of 76.66% for fuel flow and 66.51% for NOX concentration in model accuracy were achieved. The GMDH dyno-engine model can be effectively applied as a virtual test cell instrument for testing, calibration, and optimization purposes.

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