A Methodology to Predict Mobile Air-Conditioning System (MAC) Performance for Low GWP Drop-In Refrigerant Using 1D CAE Simulation Tool

Abstract

<div class="section abstract"><div class="htmlview paragraph">In developing nations, most passenger vehicles are equipped with mobile air conditioning (MAC) systems that work on Hydro Fluoro Carbons (HFC) based refrigerants. These refrigerants have a high global warming potential (GWP) and hence adversely affect the environment. According to the Kigali amendment to Montreal Protocol, Article-5 Group-2 countries including India must start phasing down HFCs from 2028 and replace them with low Global Warming Potential (GWP) refrigerants. One such class of low GWP refrigerant is Hydro Fluoro Olefins (HFO)</div><div class="htmlview paragraph">In order to replace HFCs with HFOs in existing MAC systems, the various system performance parameters with the new refrigerant are required to be evaluated. Performance evaluation of MAC system is rendered quicker and cost-effective by deploying a digital simulation tool. There is good correlation and confidence established for MAC performance prediction with HFCs through 1D CAE. Further, to enable AC performance simulation with drop-in refrigerant through 1D CAE, a simulation methodology needs to be formulated to build correlation with physical test.</div><div class="htmlview paragraph">This work comprises generating the physical test data by replacing the R-134a refrigerant in a test vehicle with low GWP R-1234yf drop-in refrigerant. The MAC system performance is validated at severe ambient condition (&gt;40°C) and then compared with baseline performance with R-134a refrigerant. Preliminary work comprises performing first-cut simulation by replacing R-134a in the correlated model with R-1234yf and analyzing the gap between physical test data and 1D CAE outcome. A sensitivity analysis is carried out to understand the impact of different parameters like warm-up temperatures, duct heat gain values etc. on MAC performance. Simulation results obtained by tuning these parameters are found to correlate with physical test data by &gt; 95% accuracy. With the correlated model, this simulation methodology is deployed for another vehicle to predict the MAC performance with drop-in refrigerant.</div><div class="htmlview paragraph">The proposed methodology will help to understand the impact of drop-in refrigerants on present HFC-based MAC systems and enable us to provide feasible recommendations to meet the target MAC performance for intended climatic usage conditions well before prototyping and physical validation.</div></div>