The need to deliver fast-in-market and right-first-time design for ultra-low-emission vehicles at a reasonable cost is driving the automotive industries to invest significant manpower in computer-aided design and optimization of exhaust after-treatment systems. To serve the above goals, an already developed engineering model for the three-way catalytic converter kinetic behaviour is linked with a genetic algorithm optimization procedure, for fast and accurate estimation of the set of tuneable kinetic parameters that describe the chemical behaviour of each specific washcoat formulation. The genetic-algorithm-based optimization procedure utilizes a purpose-designed performance measure that allows an objective assessment of model prediction accuracy against a set of experimental data that represent the behaviour of the specific washcoat formulation over a typical legislated test procedure.The identification methodology is tested on a demanding case study, and the best-fit parameters demonstrate high accuracy in matching the measured test data. The results are definitely more accurate than those usually obtained by manual or gradient-based tuning of the parameters. Moreover, the set of parameters identified by the genetic algorithm methodology is proven to describe in a valid way the chemical kinetic behaviour of the specific catalyst, and this is tested by the successful prediction of the performance of a smaller-size converter.The parameter estimation methodology developed fits in an integrated computer-aided engineering methodology assisting the design optimization of catalytic exhaust systems that extends all the way through from model development to parameter estimation, and quality assurance of test data.

The competition to deliver ultra low emitting vehicles at a reasonable cost is driving the automotive industry to invest significant manpower and test lab resources in the design optimization of increasingly complex exhaust aftertreatment systems. Optimization can no longer be based on traditional approaches, which are intensive in hardware use and lab testing. This paper discusses the extents and limitations of applicability of state-of-the-art mathematical models of catalytic converter performance. In-house software from the authors’ lab, already in use during the last decade in design optimization studies, updated with recent, important model improvements, is employed as a reference in this discussion. Emphasis is on the engineering methodology of the computational tools and their application, which covers quality assurance of input data, advanced parameter estimation procedures, and a suggested performance measure that drives the parameter estimation code to optimum results and also allows a less subjective assessment of model prediction accuracy. Extensive comparisons between measured and computed instantaneous emissions over full cycles are presented, aiming to give a good picture of the capabilities of state of the art engineering models of automotive catalytic converter systems.

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Increasingly stringent diesel particulate emissions standards have reestablished international interest in diesel filters, whose first series application dates back to 1985. Modern diesel engine technology, with computerized engine management systems and advanced, common rail injection systems, needs to be fully exploited to support efficient and durable diesel filter systems with catalytic aids, as standard equipment in passenger cars. Efficient system and components’ optimization requires the use of mathematical models of diesel filter performance. The three-dimensional model for the regeneration of the diesel particulate filter presented in this paper has been developed as an engineering tool for the detailed design optimization of SiC diesel filters of modular structure. The 3-D modeling is achieved by interfacing an existing 1-D model to commercial finite element method software for the computation of the 3-D temperature field within the whole filter assembly, including the adhesive of the filter blocks, the insulation mat, and the metal canning. The 3-D model is applied to real-world component optimization studies of diesel filter systems.

In this paper, an experimental validation procedure is applied to an improved one-dimensional model of fuel additive assisted regeneration of a diesel particulate filter. Full-scale tests on an engine bench of the regeneration behaviour of a diesel filter fitted to a modern diesel engine run on catalystdoped fuel are employed for this purpose. The main objectives of the validation procedure concern the ability of the model to predict the effects of exhaust mass flowrate, initial soot loading mass, volatile organic fraction of the soot and additive concentration in the fuel. The results of the validation procedure are intended to demonstrate the scope and extent of applicability of models of this type to real-world design and optimization studies with diesel filters.