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A Phenomenological Model for Predicting the Combustion Phasing and Variability of Spark Assisted Compression Ignition (SACI) Engines

[+] Author Affiliations
Niket Prakash, Jason B. Martz, Anna G. Stefanopoulou

University of Michigan, Ann Arbor, MI

Paper No. DSCC2015-9883, pp. V001T11A004; 8 pages
doi:10.1115/DSCC2015-9883
From:
  • ASME 2015 Dynamic Systems and Control Conference
  • Volume 1: Adaptive and Intelligent Systems Control; Advances in Control Design Methods; Advances in Non-Linear and Optimal Control; Advances in Robotics; Advances in Wind Energy Systems; Aerospace Applications; Aerospace Power Optimization; Assistive Robotics; Automotive 2: Hybrid Electric Vehicles; Automotive 3: Internal Combustion Engines; Automotive Engine Control; Battery Management; Bio Engineering Applications; Biomed and Neural Systems; Connected Vehicles; Control of Robotic Systems
  • Columbus, Ohio, USA, October 28–30, 2015
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5724-3
  • Copyright © 2015 by ASME

abstract

An advanced combustion mode, Spark Assisted Compression Ignition (SACI) has shown the ability to extend loads relative to Homogenous Charge Compression Ignition (HCCI) combustion but at reduced fuel conversion efficiency. SACI combustion is initiated by a spark, with flame propagation followed by a rapid autoignition of the remaining end-gas fuel fraction. Extending upon previous work [1,2], the Wiebe function coefficients used to fit the two combustion phases are regressed here as functions of the air path variables and actuator settings. The parameterized regression model enables mean-value modeling and model-based combustion phasing control. SACI combustion however, exhibits high cyclic variability with random characteristics. Thus, combustion phasing feedback control needs to account for the cyclic variability to correctly filter the phasing data. This paper documents the success in regressing the cyclic variability (defined as the standard deviation in combustion phasing) at various operating conditions, again as a function of air path variables and actuator settings. The combination of the regressed mean and standard deviation models is a breakthrough in predicting the mean-value engine behavior and the random statistics of the cycle-to-cycle variability.

Copyright © 2015 by ASME

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