Abstract
As a major energy-consuming subsystem in electric vehicles, the air-conditioning heat pump (ACHP) system suffers from degraded heating performance and reduced driving range under low-temperature conditions. Carbon dioxide (CO2), characterized by low global warming potential (GWP) and high heating capacity, has emerged as a promising alternative refrigerant for ACHP systems. However, its practical application remains constrained by safety concerns, particularly the high operating pressures and elevated discharge temperatures associated with CO2 scroll compressors. In this study, a comprehensive thermodynamic model incorporating detailed heat transfer and leakage sub-models is developed to investigate the operating characteristics of a CO2 scroll compressor. Experimental measurements were conducted to obtain key thermal parameters and compressor performance across a range of operating conditions. The results indicate that increasing compressor speed and expansion valve opening significantly enhances the mass flow rate and heating capacity, albeit at the expense of higher discharge temperatures. Furthermore, an increase in pressure ratio leads to higher compression work and a consequent reduction in the heating coefficient of performance (COP). Key empirical coefficients in the leakage and heat transfer models—namely Cd-suc = 0.425, Cd-dis = 0.35, and K = 1.20 for CO2—were identified and calibrated against experimental data. The proposed model demonstrates strong predictive capability, with a maximum deviation of 7.8% across all evaluated performance parameters. Notably, the prediction error for heating capacity is reduced from 5.49% to 3.26% compared with a conventional R134a scroll compressor operating at 6000 r/min.
| Original language | English |
|---|---|
| Article number | 141659 |
| Number of pages | 16 |
| Journal | Energy |
| Volume | 360 |
| Early online date | 18 Jun 2026 |
| DOIs | |
| Publication status | E-pub ahead of print - 18 Jun 2026 |
Bibliographical note
Publisher Copyright:© 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
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