This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Development of a Fully Physical Vehicle Model for Off-Line Powertrain Optimization: A Virtual Approach to Engine Calibration
Technical Paper
2021-24-0004
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Language:
English
Abstract
Nowadays control system development in the automotive industry is evolving rapidly due to several factors. On the one hand legislation tightening is asking for simultaneous emission reduction and efficiency increase, on the other hand the complexity of the powertrain is increasing due to the spreading of electrification. Those factors are pushing for strong design parallelization and frontloading, thus requiring engine calibration to be moved much earlier in the V-Cycle. In this context, this paper shows how, coupling well known physical 1D engine models featuring predictive combustion and emission models with a fully physical aftertreatment system model and longitudinal vehicle model, a powerful virtual test rig can be built. This virtual test rig can be used for powertrain virtual calibration activities with reduced requirement in terms of experimental data.
This work moved from an already developed and validated powertrain and vehicle model featuring a 1.6-liter diesel engine Fast Running Model (FRM) with DIPulse predictive combustion and emissions model. On one hand, the engine model was calibrated on 29 steady state operating points and validated on a full engine map, showing a maximum error below 5% on Brake Specific Fuel Consumption (BSFC) and an average error around 20% for NOx emissions. On the other hand, the vehicle and aftertreatment model, composed by Diesel Oxidation Catalyst (DOC) and Selective Catalyst Reduction on Filter (SCRoF), was validated on the Worldwide Harmonized Light Duty Vehicles Test Cycle (WLTC) in terms of fuel consumption, engine-out and tailpipe NOx emissions.
This virtual test rig was then used to optimize the engine calibration in a fully automated way, exploiting NSGA-III (Non dominated Sorting Genetic Algorithm) and strong parallelization capabilities. The optimization considered different design constraints, including also the combustion noise and was performed over a set of Key Points (KPs) representative of the engine operating conditions along WLTC, RTS95, US06 and FTP75. 10 independent variables were considered including both fuel injection and air management control variables. Output of the optimization was the Pareto front BSFC-Noise-NOx per each operating point. This intermediate result could directly be used by calibration engineers to select the most appropriate calibration set. Moreover, the Pareto fronts were used in an additional optimization loop to develop various calibration sets, each of which with a different weight for NOx emissions and engine fuel consumption.
Finally, the optimized engine calibrations were assessed over the WLTC. The fully virtual approach was so demonstrated to be capable to achieve comparable results with respect to traditional experimental engine calibration methods at a fraction of time and cost and before any vehicle experimental activity.
Authors
Topic
Citation
Millo, F., Piano, A., Zanelli, A., Boccardo, G. et al., "Development of a Fully Physical Vehicle Model for Off-Line Powertrain Optimization: A Virtual Approach to Engine Calibration," SAE Technical Paper 2021-24-0004, 2021, https://doi.org/10.4271/2021-24-0004.Data Sets - Support Documents
| Title | Description | Download |
|---|---|---|
| Unnamed Dataset 1 | ||
| Unnamed Dataset 2 | ||
| Unnamed Dataset 3 | ||
| Unnamed Dataset 4 | ||
| Unnamed Dataset 5 |
Also In
References
- Millo , F. , Boccardo , G. , Piano , A. , Arnone , L. et al. Numerical Simulation of the Combustion Process of a High EGR, High Injection Pressure, Heavy Duty Diesel Engine SAE Technical Paper 2017-24-0009 2017 https://doi.org/10.4271/2017-24-0009
- Röpke , K. , Baumann , W. , Köhler , B.U. , Schaum , S. , et al. 2012 https://doi.org/10.1007/978-1-4471-2221-0_10
- Denny , M. , Holst , F. , Helmantel , A. , Persson , H. et al. Impact of Closely-coupled Triple-Pilot and Conventional Double-Pilot Injection Strategies in a LD Diesel Engine Fuel 246 2019 141 148 https://doi.org/10.1016/j.fuel.2019.02.101
- O'Connor , J. and Musculus , M. Post Injections for Soot Reduction in Diesel Engines: A Review of Current Understanding SAE Int. J. Engines 6 1 2013 400 421 https://doi.org/10.4271/2013-01-0917
- Fortuna , T. , Koegeler , H.M. , Kordon , M. et al. DoE and Beyond — Evolution of the Model-based Development Approach ATZ Worldw 117 2015 30 35 10.1007/s38311-015-0161-3
- Pinamonti , S. , Brancale , D. , Meister , G. , and Mendoza , P. A Correlation Methodology between AVL Mean Value Engine Model and Measurements with Concept Analysis of Mean Value Representation for Engine Transient Tests SAE Technical Paper 2017-24-0053 2017 https://doi.org/10.4271/2017-24-0053
- Fang , X.H. , Papaioannou , N. , Leach , F. , and Davy , M.H. On the Application of Artificial Neural Networks for the Prediction of NOx Emissions from a High-Speed Direct Injection Diesel Engine International Journal of Engine Research 2020 https://doi.org/10.1177/1468087420929768
- Sharma , S. Sun , Y. , and Vernham , B. Predictive Semi-Empirical NOx Model for Diesel Engine International Journal of Energy and Power Engineering 13 5 365 376 2019 https://doi.org/10.5281/zenodo.3298854
- Sediako , A. , Andric , J. , Sjoblom , J. , and Faghani , E. Heavy Duty Diesel Engine Modeling with Layered Artificial Neural Network Structures SAE Technical Paper 2018-01-0870 2018 https://doi.org/10.4271/2018-01-0870
- Daniels , C. , Mcalpine , R.O.B. , and Roggendorf , K. 2020
- Mirzaeian , M. and Langridge , S. Creating a Virtual Test Bed Using a Dynamic Engine Model with Integrated Controls to Support in-the-Loop Hardware and Software Optimization and Calibration Energies 14 3 2021 652 https://doi.org/10.3390/en14030652
- Özgül , E. , Şimşek , M. , and Bedir , H. Use of Thermodynamical Models with Predictive Combustion and Emission Capability in Virtual Calibration of Heavy Duty Engines Fuel 264 2020 https://doi.org/10.1016/j.fuel.2019.116744
- Lee , S. , Andert , P.J. , Vka , V. , Quérel , C. , et al. 2017 https://doi.org/10.1007/978-3-658-20828-8_4
- Teodosio , L. , Tufano , D. , and Bozza , F. Development of a Virtual Calibration Methodology for a Downsized SI Engine by Using Advanced Valve Strategies Energy Procedia 126 https://doi.org/10.1016/j.egypro.2017.08.164
- Millo , F. , Zanelli , A. , Rolando , L. , and Fuso , R. Development of a Comprehensive Model for the Concurrent Minimization of CO 2 and NOx Emissions of a 48 V Mild-Hybrid Diesel Car SAE Int. J. Elect. Veh. 10 2 2021 https://doi.org/10.4271/14-10-02-0014
- 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 https://doi.org/10.4271/2016-01-0547
- Piano , A. , Millo , F. , Di Nunno , D. , and Gallone , A. Numerical Analysis on the Potential of Different Variable Valve Actuation Strategies on a Light Duty Diesel Engine for Improving Exhaust System Warm Up SAE Technical Paper 2017-24-0024 2017 https://doi.org/10.4271/2017-24-0024
- Piano , A. , Millo , F. , Di Nunno , D. , and Gallone , A. Numerical Assessment of the CO2 Reduction Potential of Variable Valve Actuation on a Light Duty Diesel Engine SAE Technical Paper 2018-37-0006 2018 https://doi.org/10.4271/2018-37-0006
- Boccardo , G. , Servetto , E. , Seebooa , A. , Sammut , G. , et al. Development of a Real Time GT-POWER xRT Model for Virtual Calibration Global GT Conference 2020
- GT-SUITE 2020
- Heywood , J.B. Internal Combustion Engine Fundementals 1988
- Hiroyasu , H. and Kadota , T. Development and Use of a Spray Combustion Modeling to Predict Diesel Engine Efficiency and Pollutant Emissions Bulletin of the JSME 26 1983 214
- Sapio , F. , Piano , A. , Millo , F. , and Pesce , F. Digital Shaping and Optimization of Fuel Injection Pattern for a Common Rail Automotive Diesel Engine through Numerical Simulation SAE Technical Paper 2017-24-0025 2017 https://doi.org/10.4271/2017-24-0025
- Piano , A. , Millo , F. , Sapio , F. , and Pesce , F.C. Multi-Objective Optimization of Fuel Injection Pattern for a Light-Duty Diesel Engine through Numerical Simulation SAE Int. J. Engines 11 6 2018 1093 1107 https://doi.org/10.4271/2018-01-1124
- Russell , M. Diesel Engine Noise: Control at Source SAE Technical Paper 820238 1982 https://doi.org/10.4271/820238
- Russell , M. and Haworth , R. Combustion Noise from High Speed Direct Injection Diesel Engines SAE Technical Paper 850973 1985 https://doi.org/10.4271/850973
- AVL 1986
- Castagne , M. , Bentolila , Y. , Halle , A. , Nicolas , F. , et al. 2007
- Arya , P. , Millo , F. , and Mallamo , F. A Fully Automated Smooth Calibration Generation Methodology for Optimization of Latest Generation of Automotive Diesel Engines Energy 178 2019 334 343 https://doi.org/10.1016/j.energy.2019.04.122
- GT-SUITE 2020