Model-based multi-critical optimisation of combustion engine fuel consumption and emissions

Wang, Xiaoming, Zhang, Shujun ORCID: 0000-0001-5699-2676 and Bechkoum, Kamal ORCID: 0000-0001-5857-2763 (2019) Model-based multi-critical optimisation of combustion engine fuel consumption and emissions. IOP Conference Series: Materials Science and Engineering, 517 (1). 012005. doi:10.1088/1757-899X/517/1/012005

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Abstract

The combustion engine is a typical nonlinear multi-input multi-output (MIMO) system with strong couplings, actuator constraints, and fast dynamics This paper addresses a model-based multi-critical optimisation approach in diesel engines, which allows to improve emission performance and to provide a reference for the design and optimisation of the diesel engine system. The first part of this paper introduces a data based modelling method that appears particularly suitable for emission modelling. The Design of Experiments (DoE) method helps to generate and collect the required measurement for data-based modelling in a short time, despite the increasing number of manipulated variables. The second part establishes a new model-based multi-critical optimisation approach that supports the optimisation of fuel consumption and emissions based on engine models. This proposed model-based framework consists of system identification and multi-critical optimisation. This framework has the ability to achieve the fast and precise solving of multi-critical optimisation problem and is suitable for implementation in the engine control unit. The experiment results illustrate that the model-based multi-critical optimisation significantly improves the engine exhaust emissions and fuel consumption against the original ECU.

Item Type:	Article
Article Type:	Article
Uncontrolled Keywords:	Dual-process models; Exercise psychology; Head-mounted display; Immersion; Physical activity; Virtual reality
Related URLs:	Publisher
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Schools and Research Institutes > School of Business, Computing and Social Sciences
Research Priority Areas:	Applied Business & Technology
Depositing User:	Susan Turner
Date Deposited:	01 Aug 2019 08:11
Last Modified:	31 Aug 2023 08:01
URI:	https://eprints.glos.ac.uk/id/eprint/7077

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