Tongwon Lee
Daecheon College
Choongsik Bae
Korea Advanced Institute of Science and Technology
University of Michigan
Stanislav V. Bohac and Dennis N. Assanis

Wall wetting of injected fuel onto the intake manifold and cylinder wall causes unpredictable transient behavior of air-fuel mixing which results in a significant emission of unburned hydrocarbon (HC) emission during cold start operation. Heated exhaust gas oxygen (HEGO) sensors cannot measure the air-fuel ratio (A/F) of exhaust gas during cold start condition. Precise and fast estimation of air/fuel ratio of the exhaust gas is required to elucidate the wall wetting phenomena and subsequent HC formation. Refined A/F estimation can enable the control of fuel injection minimizing HC emissions during cold start conditions so that HC emissions can be minimized.

A new estimator for A/F of the exhaust gas has been developed. The A/F estimator described in this study utilizes measured exhaust gas temperature and general engine parameters such as engine speed, airflow, coolant temperature, etc. A fast response, fine-wire thermocouple was used to measure exhaust gas temperatures and a fast response flame ionization detector was used to measure HC emissions during the cold start period. A Generalized Regression Neural Network Function Approximation (GRNN) was used to estimate the A/F of exhaust gas. The A/F traces generated by the GRNN algorithm agree very well with measurements.