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

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

Ghanaati, Ali, 1979 (author): Chalmers tekniska högskola,Chalmers University of Technology

Sjöblom, Jonas, 1968 (author): Chalmers tekniska högskola,Chalmers University of Technology

Faghani, Ethan (author): Volvo Cars

(creator_code:org_t)

2020-09-30
2021
English.
In: Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering. - : SAGE Publications. - 2041-2991 .- 0954-4070. ; 235:2-3, s. 436-445

Related links:: https://research.cha...; show more...; https://doi.org/10.1...; show less...

Journal article (peer-reviewed)

Abstract Subject headings

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.

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 -- Inbäddad systemteknik (hsv//swe)
ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Embedded Systems (hsv//eng)
TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)

Keyword

Virtual test cell
self-organized system
artificial neural network
hardware-in-the-loop
group method of data handling

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ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Mechanical Engin ...; and Other Mechanical ...

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Electrical Engin ...; and Embedded Systems

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Electrical Engin ...; and Computer Systems

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By the university: Chalmers University of Technology

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