A model-based approach to automotive three-way catalyst on-board monitoring

A model-based three-way automotive catalyst monitoring and fault detection strategy is presented in this work. The performance of the catalyst is inferred from the error between the post-catalyst exhaust gas oxygen sensor air fuel ratio measurement and the model predicted value. A simplified catalyst oxygen storage and reversible deactivation model is employed to predict the post-catalyst air fuel ratio. The model-based strategy is based on the use of a test statistic that is computed from a window of post-catalyst air fuel ratio prediction error data updated in real-time. A fault is assumed to be present when the value of this statistic exceeds a threshold determined from some specified confidence level. We illustrate several test statistics using engine operating data and conclude with an evaluation of this strategy.