This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Virtual Cylinder Pressure Sensor (VCPS) with Individual Variable-Oriented Independent Estimators
Technical Paper
2005-01-0059
ISSN: 0148-7191, e-ISSN: 2688-3627
Annotation ability available
Sector:
Language:
English
Abstract
Tremendous amount of useful information can be extracted from the cylinder pressure signal for engine combustion control. However, the physical cylinder pressure sensors are undesirably expensive and their health need to be monitored for fault diagnostic purpose as well. This paper presents the results of the development of a virtual cylinder pressure sensor (VCPS) with individual variable-oriented independent estimators. Two neural network-based independent cylinder pressure related variable estimators were developed and verified at steady state. The results show that these models can predict the variables correctly compared with the extracted variables from the measured physical cylinder pressure sensor signal. Good generalization capabilities of the developed models are observed in the sense that the models work well not only for the training data set but also for the new inputs that they have never been exposed to before. Different neural network structures and input variable combinations were explored and compared in terms of performance. The results of this paper clearly show that the VCPS with individual variable-oriented independent estimators can greatly reduce the modeling task as well as improve the robustness and accuracy of the models. The VCPS for transient conditions are suggested.
Recommended Content
Authors
Topic
Citation
Wang, J., Roecker, R., and Roberts, C., "Virtual Cylinder Pressure Sensor (VCPS) with Individual Variable-Oriented Independent Estimators," SAE Technical Paper 2005-01-0059, 2005, https://doi.org/10.4271/2005-01-0059.Also In
References
- Shiao Yaojung Moskwa, J.J. “Cylinder Pressure and Combustion Heat Release Estimation for SI Engine Diagnostics Using Nonlinear Sliding Observers,” IEEE Transactions on Control Systems Technology 70 78 3 March 1995
- Traver Michael L. Atkinson Richard J. Atkinson Christopher M. “Neural Network-Based Diesel Engine Emissions Prediction Using In-Cylinder Combustion Pressure,” SAE Paper 1999-01-1532
- Sellnau Mark C. Matekunas Frederic A. Battiston Paul A. Chang Chen-Fang “Cylinder- Pressure-Based Engine Control Using Pressure-Ratio-Management and Low-Cost Non-Intrusive Cylinder Pressure Sensors,” SAE Paper 2000-01-0932
- Eriksson Lars Andersson Ingemar “An Analytic Model for Cylinder Pressure in a Four Stroke SI Engine,” SAE Paper 2002-01-0371
- Dudek, K.P. Sain, M.K. “A Control-Oriented Model for Cylinder Pressure in Internal Combustion Engines” IEEE Transactions on Automatic Control 386 397 34 April 1989
- Haykin Simon “Neural Networks: A Comprehensive Foundation,” Prentice Hall 1999