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

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.