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Toward an Effective Virtual Powertrain Calibration System
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 03, 2018 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Due to stricter emission regulations and more environmental awareness, the powertrain systems are moving toward higher fuel efficiency and lower emissions. In response to these pressing needs, new technologies have been designed and implemented by manufacturers. As a result of increasing complexity of the powertrain systems, their control and optimization become more and more challenging. Virtual powertrain calibration, also known as model-based calibration, has been introduced to transfer a part of test bench testing into a virtual environment, and hence considerably reduce time and cost of product development process while increasing the product quality. Nevertheless, virtual calibration has not yet reached its full potential in industrial applications. Volvo Penta has recently developed a virtual test cell named VIRTEC, which is used in an ongoing pilot project to meet the Stage V emission standards. The integrated powertrain system includes engine, Exhaust Aftertreatment System (EATS), and Engine Management System (EMS). The objective of this paper is to describe the essential aspects required to increase the contribution of virtual testing in powertrain calibration activities. These aspects comprise the following: Hardware-in-the-Loop (HiL) system, simulation models, and working process for joint virtual and physical testing to facilitate efficient powertrain development process. The current paper describes the design, test and verification of a calibration platform based on the requirements of the project. The future phases in the current project (Virtual Calibration at Volvo Penta) will cover validation of the platform by performing calibrations in industrial scales on the virtual system.
|Technical Paper||Evaluation of Parallel Executions on Multiple Virtual ECU Systems|
|Technical Paper||Automatic Calibrations Generation for Powertrain Controllers Using MapleSim|
CitationFaghani, E., Andric, J., and Sjoblom, J., "Toward an Effective Virtual Powertrain Calibration System," SAE Technical Paper 2018-01-0007, 2018, https://doi.org/10.4271/2018-01-0007.
Data Sets - Support Documents
|Unnamed Dataset 1|
- Atkinson , C. and Mott , G. Dynamic Model-Based Calibration Optimization: An Introduction and Application to Diesel Engines SAE Technical Paper 2005-01-0026 2005 10.4271/2005-01-0026
- Damji , N. , Dresser , D. , Bellavoine , J. , and Swaminathan , M. Automated Model-Based Calibration for Drivability Using a Virtual Engine Test Cell SAE Technical Paper 2015-01-1628 2015 10.4271/2015-01-1628
- Friedrich , C. , Auer , M. , and Stiesch , G. Model Based Calibration Techniques for Medium Speed Engine Optimization: Investigations on Common Modeling Approaches for Modeling of Selected Steady State Engine Outputs SAE Int. J. Engines 9 4 1989 1998 2016 10.4271/2016-01-2156
- Lang , K. , Kropinski , M. , and Foster , T. Virtual Powertrain Calibration at GM Becomes a Reality SAE Technical Paper 2010-01-2323 2010 10.4271/2010-01-2323
- ETAS https://www.etas.com/download-center-files/company/Calibration_Consulting_Flyer_EN.pdf 2017
- Jaikamal , V. Model-Based ECU Development-An Integrated MiL-SiL-HiL Approach SAE Technical Paper 2009-01-0153 2009 10.4271/2009-01-0153
- Francois , G. New Approaches in Virtualization of ECU Software Development SAE Technical Paper 2013-01-0429 2013 10.4271/2013-01-0429
- Schlosser , A. , Kinoo , B. , Salber , W. , Werner , S. et al. Accelerated Powertrain Development Through Model Based Calibration SAE Technical Paper 2006-01-0858 2006 10.4271/2006-01-0858
- 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
- Sediako , A.D. , Andric , J. , Sjoblom , J. , and Faghani , E. Heavy Duty Diesel Engine Modeling with Modular Layered Artificial Neural Network Structures SAE Technical Paper 2018-01-0870 2018 10.4271/2018-01-0870 2018
- https://se.mathworks.com/products/statistics.html 2017
- Gutjahr , T. Performance Analysis of Data-Driven Plant Models on Embedded Systems SAE Technical Paper 2016-32-0086 2016 10.4271/2016-32-0086