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Online Adaptive Residual Mass Estimation in a Multicylinder Recompression HCCI Engine

[+] Author Affiliations
Jacob Larimore, Shyam Jade, Erik Hellström, Anna G. Stefanopoulou

University of Michigan, Ann Arbor, MI

Julien Vanier, Li Jiang

Robert Bosch LLC, Farmington Hills, MI

Paper No. DSCC2013-3984, pp. V003T41A005; 9 pages
doi:10.1115/DSCC2013-3984
From:
  • ASME 2013 Dynamic Systems and Control Conference
  • Volume 3: Nonlinear Estimation and Control; Optimization and Optimal Control; Piezoelectric Actuation and Nanoscale Control; Robotics and Manipulators; Sensing; System Identification (Estimation for Automotive Applications, Modeling, Therapeutic Control in Bio-Systems); Variable Structure/Sliding-Mode Control; Vehicles and Human Robotics; Vehicle Dynamics and Control; Vehicle Path Planning and Collision Avoidance; Vibrational and Mechanical Systems; Wind Energy Systems and Control
  • Palo Alto, California, USA, October 21–23, 2013
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5614-7
  • Copyright © 2013 by ASME

abstract

This work presents two advances to the estimation of homogeneous charge compression ignition (HCCI) dynamics. Combustion phasing prediction in control-oriented models has been achieved by modeling the in-cylinder temperature and composition dynamics, which are dictated by the large mass of residuals trapped between cycles. As such, an accurate prediction of the residual gas fraction as a function of the variable valve timing is desired. Energy and mass conservation laws applied during the exhaust valve opening period are complemented with online in-cylinder pressure measurements to predict the trapped residual mass in real time. In addition, an adaptive parameter estimation scheme uses measured combustion phasing to adjust the residual mass prediction. Experimental results on a multicylinder gasoline HCCI engine demonstrate the closed loop residual estimation’s ability to compensate for modeling errors, cylinder to cylinder variations, and engine wear. Additionally it is shown that using the adaptive parameter estimation reduces the model parameterization effort for a multicylinder engine.

Copyright © 2013 by ASME

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