Identification of catalytic converter kinetic model using a genetic algorithm approach

Abstract

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.

A Physics-Based Integrated Model of a Spark Ignition Engine and a Three-Way Catalyst

“…Voltz et al (1973) appear to have formed the basis for most developments in the modeling of the chemical behavior. Their model has since been extended with updated kinetics and more comprehensive reaction mechanisms (e.g., Auckenthaler, 2005;Holder et al, 2006;Oh and Cavendish, 1982;Pattas et al, 1994;Pontikakis and Stamatelos, 2004;Subramaniam and Varma, 1985). One-dimensional physicsbased catalyst models (e.g., Heck et al, 1976;Oh and Cavendish, 1982;Pontikakis and Stamatelos, 2004) typically consider energy and mass conservation equations, which are closely linked with chemical kinetic schemes of various complexity.…”

The Development and Application of an Evolutionary Algorithm for the Determination of Kinetic Parameters in Automotive Aftertreatment Models

“…7 and 8 and Table 4 demonstrates that the Hybrid optimiser is slightly more accurate in the THC light-off curve, whereas the nPSO algorithm matches the NO peak with a slightly greater accuracy. The best input variables reported by both these optimisers, Table 5, are similar to the input objective variables, indicating that the best solutions found are within the area of the global optima, any difference being attributed to the known convergence issues of these optimisers [12,34].…”

“…Genetic algorithms have previously been used to optimise complex mathematical systems, such as reaction kinetic problems [12,[17][18][19][20][21][22], grouping problems [23], and the mathematical travelling salesman problem [24]. GAs are inspired by nature and mimic the biological process of natural selection [23].…”

“…Pontikakis and Stamelos [12] programmed a genetic algorithm approach to optimise a set of ten reactions in a three-way catalyst model, showing a high level of accuracy when applied to the new European drive cycle (NEDC).…”

“…The GA approach has been selected as it has demonstrated a high level of accuracy in previous studies [12,16]. The GA algorithm has been shown to be accurate across multiple test scenarios, including automotive modelling problems.…”