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Neural Network Based Hybridized Dynamic Models for Connected Vehicles - A Case Study on Turbocharger Position Prediction
Technical Paper
2019-28-2443
ISSN: 0148-7191, e-ISSN: 2688-3627
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Event:
NuGen Summit
Language:
English
Abstract
Combustion engine driven vehicles operating in a connected and autonomous vehicle (CAVs) environment, the engine drive cycles are run in a regulated manner. This is due to synchronized movement of vehicles operating in connected environment. Hence, developing intelligent and faster control of airpath variable with smooth transient tracking, helps to achieve a synchronized drive cycle. With regards to this author discuss modeling of turbocharger. This is critical for airpath system variable calculation. Due to the hybridized nature of turbocharger models, predicting accurately the position of VTG without introduction of any sensing devices is key, as sensing device induces delay in action. Authors propose a model which improve the performance and capability of VTG position prediction. A neural network based supervised learning model is developed. This model is coupled with engine models which are in series application for performance evaluation. The model is trained and validated with field data. Based on performance analysis there is a strong correlation between the model and the data. The developed model is easily scaled up for various components and be used for virtualization of sensor in a vehicle due to improved response time. This forms an ideal basis for connected vehicles, as the control of powertrain components needs to synchronize with the commands coming from the adjacent vehicles. There is a harmonized transition of vehicle drive patterns due to V2V and V2X communication, which needs a synchronous action of powertrain components due to the communication request.
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Citation
Kamath, R., Venkobarao, V., Kopold, R., and Subramaniam, C., "Neural Network Based Hybridized Dynamic Models for Connected Vehicles - A Case Study on Turbocharger Position Prediction," SAE Technical Paper 2019-28-2443, 2019, https://doi.org/10.4271/2019-28-2443.Data Sets - Support Documents
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References
- Liu , J. , Kockelman , K. , and Nichols , A. Anticipating the Emissions Impacts of Smoother Driving by Connected and Autonomous Vehicles, Using the MOVES Model Transportation Research Board 96th Annual Meeting 2016 http://www.caee.utexas.edu/prof/kockelman/public_html/TRB17emissionsAVsmoothedcycle.pdf
- Puškár , M. , Fabian , M. , Kádárová , J. , Blišt’an , P. , and Kopas , M. Autonomous Vehicle with Internal Combustion Drive based on the Homogeneous Charge Compression Ignition Technology International Journal of Advanced Robotic Systems 2017 10.1177/1729881417736896
- Chasse , A. , Moulin , P. , Gautier , P. , Albrecht , A. et al. Double Stage Turbocharger Control Strategies Development SAE Int. J. Engines 1 1 636 646 2009 10.4271/2008-01-0988
- Canova , M. , Taburri , M. , Fiorentini , L. , Chiara , F. et al. Modeling and Analysis of a Turbocharged Diesel Engine with Variable Geometry Compressor System SAE Int. J. Engines 4 2 2405 2417 2011 10.4271/2011-24-0123
- Kamath , R. , Subramanian , C.K. , and Venkobarao , V. Simulation and Design of Decentralized PI Observer Based Controller for Nonlinear Interconnected Systems of the Diesel Engine Air-path Energy Procedia 117C 27 36 2017 10.1016/j.egypro.2017.05.103