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Approach to Model AC Compressor Cycling in 1D CAE with Enhanced Accuracy of Cabin Cooldown Performance Prediction
ISSN: 0148-7191, e-ISSN: 2688-3627
Published September 22, 2021 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
In previous work, AC Compressor Cycling (ACC) was modeled by incorporating evaporator thermal inertia in Mobile Air Conditioning (MAC) performance simulation. Prediction accuracy of >95% in average cabin air temperature has been achieved at moderate ambient condition, however the number of ACC events in 1D CAE simulation were higher as compared to physical test .
This paper documents the systematic approach followed to address the challenges in simulation model in order to bridge the gap between physical and digital. In physical phenomenon, during cabin cooldown, after meeting the set/ target cooling of a cabin, the ACC takes place. During ACC, gradual heat transfer takes place between cold evaporator surface and air flowing over it because of evaporator thermal inertia.
In earlier work, the ‘evaporator exit air temperature’ has been used to model ACC, whereas in the current work, the ‘evaporator exit air temperature’ is replaced by ‘point mass exit air temperature’ to simulate gradual heat transfer. Further, vehicle cabin and vents are modeled as point masses, which enables calibration of the cabin and AC vents independently with physical test results and capture rise/ fall in temperature precisely. Also, overall heat transfer coefficient, surface area and heat capacity impact are captured during correlation studies.
With this approach, the target accuracy of >97% in average cabin air temperature and >90% in ACC frequency prediction has been achieved, which confirms the robustness of the simulation model. In proposed 1D CAE simulation model, AC compressor discharge and suction pressure correlation have limitation due to absence of point masses in digital model; being a software limitation, further work is required to address this gap in order to improve refrigerant pressure prediction accuracy.
- Shridhar Diliprao Kulkarni - Tata Motors Ltd
- Shridhar Diliprao Kulkarni - Tata Motors, Ltd.
- Kiran Kadam - Tata Motors Ltd
- Kiran Kadam - Tata Motors, Ltd.
- Santosh Venu - Tata Motors Ltd
- Santosh Venu - Tata Motors, Ltd.
- Mohit Varma - Tata Technologies Ltd
- Sambhaji Jaybhay - Tata Motors Ltd
- Sambhaji Jaybhay - Tata Motors, Ltd.
- Sangeet Kapoor - Tata Motors Ltd
- Sangeet Kapoor - Tata Motors, Ltd.
Data Sets - Support Documents
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- Cipollone , R. and Di Battista , D. High Efficiency Air Conditioning Model based Analysis for the Automotive Sector International Journal of Refrigeration 2016 http://dx.doi.org/doi:10.1016/j.ijrefrig.2015.12.014
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- Ilic , S.M. , Hrnjak , P.S. , and Bullard , C.W. Experimental Comparison of Continuous vs. Pulsed Flow Modulation in Vapor Compression Systems 2002 International Refrigeration and Air Conditioning Conference, Paper 541 http://docs.lib.purdue.edu/iracc/541
- KULI Software Tool for Energy Management Optimization, MAGNA POWERTRAIN - Engineering Center Steyr GmbH & Co KG, Austria www.KULI.at