This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Use of Predictive Engine and Emission Model for Diesel Engine Model Based Calibration
Technical Paper
2019-01-2227
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Language:
English
Abstract
The GHG and emissions regulations are becoming more and more stringent every year. To fulfill legislation requirements and potential future challenges, increasing number of technologies and actuators have been developed and implemented into powertrain systems. This trend poses new challenges on engine development process by harmonizing early stage technology implementation, hardware selection and performance evaluation with late stage calibration and validation works. Frontloading feedbacks to design and development team enable better decision making, hardware selection and calibration optimization. Seamless powertrain simulation toolchains can realize such frontloading tasks to reduce development cost and provide late stage information at early development period.
However, frontloading virtualized development remains a large challenge for model developers with limited data during early phase of development. For various usages of simulations and models, especially robust calibration usage purpose, the models need to have high level of accuracy, reasonable simulation runtime and predictability over wide range of operating conditions at the same time; meanwhile there is limited quantity of test data available to generate data driven and statistical models and to perform optimizations. Therefore, this paper would focus on the flexibility and predictability of GT-Suite DI-Pulse predictive engine models and their capability as virtual testing cell by demonstrating late stage altitude calibration application with engine models developed with relative small amount of early stage sea-level data .
Detailed phenomenological based combustion model, air path model and emission models have been developed for a turbocharged 4 cylinder diesel engine using steady state points over entire operating area; such data could be collected at early phase of engine development and there are many use cases of similar models for hardware evaluation and selection. The air path was later reduced to simplified geometries and crank-angle based combustion calculation step became coarser to achieve a fast runtime model (FRM). The major pressure pulsations within the systems were well captured, which is mandatory to determine volumetric efficiency, turbocharger operation and exhaust gas recirculation (EGR) distribution. The predictive combustion model remained similar level of accuracy on engine efficiency and exhaust gas temperature. After model validations, design of experiments (DoEs) of control and actuator variables were carried out with FRM model to generate virtual testing data and to make optimized altitude calibration maps. Finally, altitude engine dyno tests were conducted to verify both calibration and model prediction capability at altitude conditions. The work has demonstrated high accuracy predictive models developed with limited data and the frontloading capabilities to reduce calibration work and provide late stage information at very beginning of projects.
Recommended Content
Authors
Citation
Wei, Y., Uppalapati, L., and Vernham, B., "Use of Predictive Engine and Emission Model for Diesel Engine Model Based Calibration," SAE Technical Paper 2019-01-2227, 2019, https://doi.org/10.4271/2019-01-2227.Data Sets - Support Documents
| Title | Description | Download |
|---|---|---|
| Unnamed Dataset 1 | ||
| Unnamed Dataset 2 | ||
| Unnamed Dataset 3 | ||
| Unnamed Dataset 4 | ||
| Unnamed Dataset 5 | ||
| Unnamed Dataset 6 | ||
| Table 7 |
Also In
References
- Atkinson , C. and Mott , G. Dynamic Model-Based Calibration Optimization: An Introduction and Application to Diesel Engines 2005-01-0026 2005
- De Nola , F. , Giardiello , G. , Gimelli , A. , Molteni , A. et al. A Model-Based Computer Aided Calibration Methodology Enhancing Accuracy, Time and Experimental Effort Savings Through Regression Techniques and Neural Networks SAE Technical Paper 2017-24-0054 2017 10.4271/2017-24-0054
- Ropke , Rosenek , and Fischer Practical Application of DoE Methods in the Development of Production Internal Combustion Engines London: International conference on statistics and analytical methods in automotive engineering,IMechE September 2002
- Nishio , Y. , Murata , Y. , Yamaya , Y. et al. Sci. China Inf. Sci. 2018 61 https://doi.org/10.1007/s11432-017-9381-6
- Atkinson , C. Fuel Efficiency Optimization using Rapid Transient Engine Calibration SAE Technical Paper 2014-01-2359 2014 10.4271/2014-01-2359
- Lakshmidhar , U. 2017
- Faghani , E. , Andric , J. , and Sjoblom , J. Toward an Effective Virtual Powertrain Calibration System SAE Technical Paper 2018-01-0007 2018 10.4271/2018-01-0007
- Cosadia , I. , Silvestri , J. , Papadimitriou , I. , Maroteaux , D. et al. Traversing the V-Cycle with a Single Simulation - Application to the Renault 1.5 dCi Passenger Car Diesel Engine SAE Technical Paper 2013-01-1120 2013
- Daya , R. , Desai , C. , and Vernham , B. Emiss. Control Sci. Technol. 2018 4 143 https://doi.org/10.1007/s40825-018-0095-5
- Daya , R. , Desai , C. , and Vernham , B. Emiss. Control Sci. Technol. 2018 4 172 https://doi.org/10.1007/s40825-018-0094-6
- Heywood , John B. Internal Combustion Engine Fundamentals New York McGraw-Hill 1988
- Piano , A. , Millo , F. , Boccardo , G. , Rafigh , M. et al. Assessment of the Predictive Capabilities of a Combustion Model for a Modern Common Rail Automotive Diesel Engine SAE Technical Paper 2016-01-0547 2016
- Schwarzmeier , M. 1992
- Reulein , C. 1998
- Xia , F. , Lee , S.-Y. , Andert , J. , Kampmeier , A. et al. Crank-Angle Resolved Real-Time Engine Modelling A Seamless Transfer from Concept Design to HiL Testing SAE Technical Paper 2018-01-1245 2018 10.4271/2018-01-1245
- Andric , J. , Schimmel , D. , Sediako , A.D. , Sjoblom , J. et al. Development and Calibration of One Dimensional Engine Model for Hardware-In-The-Loop Applications SAE Technical Paper 2018-01-0874 2018 10.4271/2018-01-0874