Short-term and long-term adaptation algorithm for low-pressure exhaust gas recirculation estimation in spark-ignition engines

Low-pressure exhaust gas recirculation systems are capable of increasing fuel efficiency of spark-ignition engines; however, they introduce control challenges. The low available pressure differential that drives exhaust gas recirculation flow, along with the significant pressure pulsations in the exhaust environment of a turbocharged engine hamper the accuracy of feed-forward estimation models. For that reason, feedback measurements are required in an effort to increase prediction accuracy. Additionally, the accumulation of deposits in the exhaust gas recirculation system and the aging of the valve, change the flow characteristics over time. Under these considerations, an adaptation algorithm is developed which handles both short-term (operating-point-dependent errors) and long-term (system aging) corrections for exhaust gas recirculation flow estimation. The algorithm is based on an extended Kalman filter for joint state and parameter estimation and uses the output of an intake oxygen sensor to adjust the feed-forward prediction by creating an online adaptation map. Two different exhaust gas recirculation estimation models are developed and coupled with the adaptation algorithm. The performance of the algorithm for both estimation models is evaluated in real-time through transient experiments with a turbocharged spark-ignition engine. It is demonstrated that this methodology is capable of creating an adaptation map which captures system aging, while also reduces the estimation bias by more than four times resulting in a prediction error of less than 1%. Finally, this approach proves to be a valuable tool that can significantly reduce offline calibration efforts for such models.


  • English

Media Info

  • Media Type: Web
  • Features: References;
  • Pagination: pp 424-440
  • Serial:

Subject/Index Terms

Filing Info

  • Accession Number: 01709379
  • Record Type: Publication
  • Files: TRIS
  • Created Date: May 17 2019 3:03PM