Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE credits
This report present the work to develop a misfire detection algorithm for onboard
diagnostics on a spark ignited combustion engine. The work is based on
a previous developed model-based detection algorithm, created to meet more
stringent future legislation and reduce the cost of calibration. In the existing approach
a simplified engine model is used to estimate the torque from the flywheel
angular velocity, and the algorithm can detect misfires in various conditions.
The main contribution in this work, is further development of the misfire detection
algorithm with focus on improving the handling of disturbances and variations
between different vehicles. The resulting detection algorithm can be automatically
calibrated with training data and manage disturbances such as manufacturing
errors on the flywheel and torsional vibrations in the crankshaft occurring
after a misfire. Furthermore a robustness analysis with different engine
configurations is carried out, and the algorithm is evaluated with the Kullback-
Leibler divergence correlated to the diagnosis requirements.
In the validation, data from vehicles with four cylinder engines are used and the
algorithm show good performance with few false alarms and missed detections.
2014. , 56 p.