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Accurate Mean Value Process Models for Model-Based Engine Control Concepts by Means of Hybrid Modeling
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 02, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Advanced powertrains for modern vehicles require the optimization of conventional combustion engines in combination with tailored electrification and vehicle connectivity strategies. The resulting systems and their control devices feature many degrees of freedom with a large number of available adjustment parameters. This obviously presents major challenges to the development of the corresponding powertrain control logics. Hence, the identification of an optimal system calibration is a non-trivial task.
To address this situation, physics-based control approaches are evolving and successively replacing conventional map-based control strategies in order to handle more complex powertrain topologies. Physics-based control approaches enable a significant reduction in calibration effort, and also improve the control robustness. However, due to the requirement of real-time capability, physical models have to be formulated via simplified mean value approaches, which in turn limits the control accuracy.
To eliminate the constraints of a purely physics-based control approach, the underlying physical process model can be augmented by an additional data-driven model. For this purpose, an artificial neural network or a Gaussian Process model can be considered amongst others. Data driven models can provide high model accuracy, but they usually show a poor predictive robustness in regions which have not extensively been trained with data beforehand. Consequently, an alternative modeling strategy is utilized, where the general process tendencies are estimated by a physical model, while the data-driven model corrects the estimation in well-known operation regions to maximize the overall process model accuracy. This modeling approach is commonly referred to as “Hybrid semi-parametric modeling” or “Hybrid Modeling Technique” (HMT). It follows the general idea of combining advantageous attributes of a physical model (predictive robustness and low calibration effort) with the benefits of a data-driven model (high model accuracy). Besides the achievement of an improved process model accuracy, HMT further enables model updates by re-training of the data-driven model, when the process behavior changes as a consequence of e.g. hardware drifts, aging or different process boundary conditions.
To evaluate the performance of the given HMT, the approach is exemplarily applied to derive an adaptive, real-time capable Diesel ignition delay and a NOx raw emission model. Available physics-based models are used as a baseline. In both application examples, HMT achieves an increased model accuracy in standard conditions by means of an artificial neural network and a Gaussian process model respectively. In parallel, thanks to the contribution of the physics-based model, a high predictive robustness is maintained in operating conditions that have not been considered during model training.
CitationJoerg, C., Lee, S., Reuber, C., Schaub, J. et al., "Accurate Mean Value Process Models for Model-Based Engine Control Concepts by Means of Hybrid Modeling," SAE Technical Paper 2019-01-1178, 2019, https://doi.org/10.4271/2019-01-1178.
Data Sets - Support Documents
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- Ayala, F., Gerty, M., and Heywood, J., “Effects of Combustion Phasing, Relative Air-fuel Ratio, Compression Ratio, and Load on SI Engine Efficiency,” SAE Technical Paper 2006-01-0229, 2006, doi:10.4271/2006-01-0229.
- Finesso, R., “Spessa: Ignition Delay Prediction of Multiple Injections in Diesel Engines,” Volume 119, 170-190, 2014, 10.1016/j.fuel.2013.11.040.
- Pischinger, S., Internal Combustion Engines II (RWTH University Aachen, 2017).
- Heywood, J., Internal Combustion Engine Fundamentals, 1988
- Fieweger, K., Blumenthal, R., and Adomeit, G., “Self-Ignition of SI Engine Model Fuels: A Shock Tube Investigation at High Pressure,” Combustion and Flame 109(4):599-619, 1997.
- Peters, N., Technical Combustion (RWTH University Aachen, 2013).
- Carr, M.A. et al., “An Experimental and Modeling-Based Study into the Ignition Delay Characteristics of Diesel Surrogate Binary Blend Fuels,” Journal of Engineering for Gas Turbines and Power 134(7):072803, 2012.
- Hiroyasu, H. and Arai, M., “Structures of Fuel Sprays in Diesel Engines,” SAE Technical Paper 900475, 1990, doi:10.4271/900475.
- Elkotb, M.M., “Fuel Atomization for Spray Modelling,” Progress in Energy and Combustion Science 8(1):61-91, 1982.
- Berner, H.-J., Reinhardt, H., and Bargende, M., Modellierung der Zündverzugszeit für mehrfacheinspritzende Dieselmotoren (RWTH University Aachen, 2008).
- Turns, S.R., An Introduction to Combustion: Concepts and Applications Second Edition (McGraw-Hill, 2000).
- Thattaradiyil, R.X., “Critical Crank Angle Ranges and Strategic Training Algorithm for an Artificial Neural Network Based Ignition Delay Model for Multi-Injection Diesel Combustion,” Fachhoschule Aachen, 2016.
- Barber, S., “AI: Neural Network for Beginners,” http://www.codeproject.com, 01.11.2018.
- Beale, M., Hagan, M., and Demuth, H., “Neural Network Toolbox User’s Guide, Version 8.2, Mathworks Inc., 2014.
- Young, J., Doubt and Certainty in Science: A Biologist’s Reflections on the Brain (Oxford University Press, 1950).
- Leverington, D., A Basic Introduction to Feedforward Backpropagation Neural Networks (Department of Geosciences, 2009).
- Hagan, M., Demuth, H., and Beale, M., Neural Network Design (Boston, MA: PWS Publishing, 1996).
- Katrašnik, T., Trence, F., and Oprešnik, S., “A New Criterion to Determine the Start of Combustion in Diesel Engines,” Journal of Engineering for Gas Turbines and Power 128:928-933, 2005.
- Jörg, C., “Development of a Combustion Rate Shaping Controller for Transient Engine Operation on a Direct Injection Compression Ignition Engine,” Ph.D. thesis, RWTH Aachen University, 2018.
- Guzzella, L. and Onder, C.H., Introduction to Modeling and Control of Internal Combustion Engine Systems 2nd Edition (Springer-Verlag, 2010). ISBN:978-3-642-10774-0.
- Quérel, C., Grondin, O., and Letellier, C., “Semi-Physical Mean-Value NOx Model for Diesel Engine Control,” Control Engineering Practice 40:27-44, 2015.
- Zeldovich, Y.B., Selected Works of Yakov Borisovich Zeldovich. Vol. Volume I (Chemical Physics and Hydrodynamics, Princeton Legacy Library, 1992).
- Thewes, S., Lange-Hegermann, M., Reuber, C., and Beck, R., “Advanced Gaussian Process Modeling Techniques,” . In: Design of Experiments (DoE) in Engine Development. (Expert Verlag, 2015).
- Rasmussen, C. and Williams, C., Gaussian Processes for Machine Learning (MIT Press, 2006).
- Lee, S.-Y., Andert, J., Neumann, D., Querel, C. et al., Hardware-in-the-Loop Based Virtual Calibration Approach to Meet Real Driving Emissions Requirements (WCX World Congress Experience, 2018), SAE Technical Paper Series.
- von Stosch, M. et al.: Hybrid Semi-Parametric Modeling in Process Systems Engineering: Past, Present and Future; Computers & Chemical Engineering; Vol. 60; p. 86-101 (2014)
- Bohlin, T. and Graebe, S.F., “Issues in Nonlinear Stochastic Grey Box Identification,” International Journal of Adaptive Control and Signal Processing 9:465-490, 1995.
- Jorgensen, S.B. and Hangos, K.M., “Grey Box Modelling for Control: Qualitative Models as a Unifying Framework,” International Journal of Adaptive Control and Signal Processing 9:547-562, 1995.
- Tulleken, H.J., “Grey-Box Modelling and Identification Using Physical Knowledge and Bayesian Techniques,” Automatica 29:285-308, 1993.
- Psichogios, D.C. and Ungar, L.H., “A Hybrid Neural Network-First Principles Approach to Process Modeling,” AIChE Journal 38:1499-1511, 1992.
- Kramer, M.A., Thompson, M.L., and Bhagat, P.M., “Embedding Theoretical Models in Neural Networks,” in American Control Conference, 1992, 475-479
- Johansen, T.A. and Foss, B.A., “Representing and Learning Unmodeled Dynamics with Neural Network Memories,” in American Control Conference, 1992, 3037-3043
- Xia, F., Lee, S., Andert, J., Kampmeier, A. et al., “Crank-Angle Resolved Real-Time Engine Modelling A Seamless Toolchain from Concept Design to HiL Testing,” SAE Technical Paper 2018-01-1245, 2018, doi:10.4271/2018-01-1245.