Cold-Start Modeling and On-Line Optimal Control of the Three-Way Catalyst
Journal article, 2021

We present a three-way catalyst (TWC) cold-start model, calibrate the model based on experimental data from multiple operating points, and use the model to generate a Pareto-optimal cold-start controller suitable for implementation in standard engine control unit hardware. The TWC model is an extension of a previously presented physics-based model that predicts carbon monoxide, hydrocarbon, and nitrogen oxides tailpipe emissions. The model axially and radially resolves the temperatures in the monolith using very few state variables, thus allowing for use with control-policy based optimal control methods. In this paper, we extend the model to allow for variable axial discretization lengths, include the heat of reaction from hydrogen gas generated from the combustion engine, and reformulate the model parameters to be expressed in conventional units. We experimentally measured the temperature and emission evolution for cold-starts with ten different engine load points, which was subsequently used to tune the model parameters (e.g. chemical reaction rates, specific heats, and thermal resistances). The simulated cumulative tailpipe emission modeling error was found to be typically − 20% to + 80% of the measured emissions. We have constructed and simulated the performance of a Pareto-optimal controller using this model that balances fuel efficiency and the cumulative emissions of each individual species. A benchmark of the optimal controller with a conventional cold-start strategy shows the potential for reducing the cold-start emissions.

Nonlinear state feedback

Optimal Control

Three-way catalyst

Real-time control

Author

Jonathan Lock

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering, Signal Processing

Kristoffer Clasén

Chalmers, Mechanics and Maritime Sciences, Combustion and Propulsion Systems, Engines and Propulsion Systems

Jonas Sjöblom

Chalmers, Mechanics and Maritime Sciences, Combustion and Propulsion Systems, Engines and Propulsion Systems

Tomas McKelvey

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering, Signal Processing

Emission Control Science and Technology

2199-3629 (ISSN) 2199-3637 (eISSN)

Vol. In press

Driving Forces

Sustainable development

Areas of Advance

Transport

Energy

Subject Categories

Energy Engineering

Control Engineering

Signal Processing

DOI

10.1007/s40825-021-00199-x

More information

Latest update

10/28/2021