Digital Automotive AC Pulldown Prediction in a Real Driving Condition 2019-01-5090
Automotive Original Equipment Manufacturers (OEMs) are always striving to deliver fast Air-Conditioning (AC) pulldown performance with consistent distribution of cabin temperature to meet customer expectations. The ultimate test is the OEM standard, called “AC Pull Down,” conducted at high ambient temperature and solar load conditions with a prescribed vehicle drive cycle. To determine whether the AC system in the vehicle has the capacity to cool the cabin, throughout the drive cycle test, cabin temperature measurements are evaluated against the vehicle target. If the measured cabin temperatures are equal or lower than the required temperatures, the AC system is deemed conventional for customer usage.
In this paper, numerical predictions of the cabin temperatures to replicate the AC pulldown test are presented. The AC pulldown scenario is carried out in a digital Climatic Wind Tunnel simulation. The solution used in this study is based on a coupled approach. With this method, convection is solved using PowerFLOW, a Lattice Boltzmann Method (LBM)-based flow solver, while conduction/radiation are solved using PowerTHERM thermal solver. Cooling loads, reproducing a drive cycle with changing vehicle speeds and compressor speeds, are obtained from the integrated modeling of PowerTHERM with a system modeling tool. Cabin air and surface temperatures corresponding to hours of physical soak, driven by natural convection followed by AC pull down with forced convection, are captured accurately. Results of temperatures from simulation show strong correlation with the experimental results and give a very good insight on design and operating changes that can improve cabin temperature requirements.
Citation: Nagarajan, V., Chang, C., Bhambare, K., Mann, A. et al., "Digital Automotive AC Pulldown Prediction in a Real Driving Condition," SAE Technical Paper 2019-01-5090, 2019. Download Citation
Vijaisri Nagarajan, Chin-Wei Chang, Kamalesh Bhambare, Adrien Mann, Edward Tate, Abdelhakim Aissaoui, Varun Ranadive, Akella Sarma, Matthew Garrisi
Dassault Systemes Simulia, Dassault Systemes, Simulia Corp, Mahindra Automotive, North America, Mahindra Research Valley