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Air Charge and Residual Gas Fraction Estimation for a Spark-Ignition Engine Using In-Cylinder Pressure
Technical Paper
2017-01-0527
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
An accurate estimation of cycle-by-cycle in-cylinder mass and the composition of the cylinder charge is required for spark-ignition engine transient control strategies to obtain required torque, Air-Fuel-Ratio (AFR) and meet engine pollution regulations. Mass Air Flow (MAF) and Manifold Absolute Pressure (MAP) sensors have been utilized in different control strategies to achieve these targets; however, these sensors have response delay in transients. As an alternative to air flow metering, in-cylinder pressure sensors can be utilized to directly measure cylinder pressure, based on which, the amount of air charge can be estimated without the requirement to model the dynamics of the manifold. In this work, an air charge estimation algorithm is proposed, which uses the dynamic cylinder pressure trace data at discrete events in the cycle, and by applying thermodynamics and heat transfer relationships, estimates individual cylinder air charge for each cycle at different engine operating points. In addition, a residual gas mass estimator is incorporated in the algorithm. Estimator output is calibrated based on experimentally determined air charge, which is calculated from the amount of injected fuel in each cylinder and individual wide-band sensor data.
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Authors
- Chris Glugla - Ford Motor Company
- Stephen Cooper - Ford Motor Company
- Douglas McEwan - Ford Motor Company
- Garlan Huberts - Ford Motor Company
- Arya Yazdani - Michigan Technological University
- Jeffrey Naber - Michigan Technological University
- Mahdi Shahbakhti - Michigan Technological University
- Paul Dice - Michigan Technological University
Topic
Citation
Yazdani, A., Naber, J., Shahbakhti, M., Dice, P. et al., "Air Charge and Residual Gas Fraction Estimation for a Spark-Ignition Engine Using In-Cylinder Pressure," SAE Technical Paper 2017-01-0527, 2017, https://doi.org/10.4271/2017-01-0527.Data Sets - Support Documents
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