New Simulation Model Enhances Accuracy of EV Heat Pump Compressor Analysis
As electric vehicles (EVs) continue to dominate the automotive industry’s future roadmap, thermal management systems have emerged as a critical technological frontier. Among the core components of EV climate control, the vapor injection scroll compressor plays a pivotal role in ensuring efficient heating performance, especially under extreme winter conditions. However, traditional thermodynamic models used to predict compressor behavior often fall short of real-world performance due to oversimplified assumptions. A groundbreaking study published in Fluid Machinery introduces a refined one-dimensional simulation model that significantly improves the accuracy of predicting the operational behavior of vapor injection scroll compressors in electric vehicles.
Conducted by Hu Jiayun, Miao Yanming, Tan Mingfei, Zhang Bin, Li Chao, He Qize, Wang Jiayun, and Li Kang from the University of Shanghai for Science and Technology, along with collaborators from Shanghai Investigation Design & Research Institute Co., Ltd. and the Shanghai Fire Research Institute under the Ministry of Emergency Management, this research addresses long-standing discrepancies between theoretical calculations and actual compressor performance. The team’s work focuses on enhancing the fidelity of thermodynamic simulations by incorporating two critical real-world phenomena: internal leakage and heat transfer.
For years, engineers have relied on isentropic models to estimate the performance of scroll compressors. These models assume ideal, reversible, and adiabatic compression processes—conditions that rarely exist in practical applications. While convenient and computationally efficient, such simplifications overlook key physical realities. In actual operation, minute gaps between the orbiting and stationary scroll wraps create leakage paths, allowing refrigerant to bypass intended compression stages. Simultaneously, heat exchange occurs between the working fluid and surrounding components, influencing temperature, pressure, and overall system efficiency. Ignoring these factors leads to overestimations in performance metrics such as heating capacity, coefficient of performance (COP), and underestimations in discharge temperature, which can compromise system reliability and durability.
The research team recognized that to optimize EV thermal systems, especially in cold climates where heat pump efficiency drops significantly, a more realistic simulation framework was essential. Their solution was the development of a comprehensive one-dimensional mathematical model that dynamically accounts for mass and energy conservation throughout the compression cycle. Unlike traditional isentropic approaches, this new model integrates time-varying internal leakage and convective heat transfer between the refrigerant and scroll surfaces as fundamental components of the simulation.
The model treats the compression process as a series of discrete angular increments, tracking changes in chamber volume, pressure, temperature, and enthalpy as the compressor’s main shaft rotates. At each increment, the simulation evaluates potential leakage between adjacent working chambers based on pressure differentials and geometric clearances. The leakage is modeled using orifice flow principles, distinguishing between choked and subsonic flow regimes to accurately capture mass transfer across axial and radial gaps. This approach acknowledges that leakage is not constant but fluctuates with operating conditions, compressor speed, and chamber position.
Equally important is the inclusion of heat transfer effects. The researchers adopted a well-established convective heat transfer correlation, adapted for the unique geometry of scroll compressors. By estimating the local heat transfer coefficient based on fluid properties, flow velocity, and surface characteristics, the model calculates the rate of thermal exchange between the refrigerant and the scroll walls. This dynamic heat loss or gain directly affects the gas temperature within each chamber, thereby influencing the compression trajectory and final discharge conditions.
To validate their model, the team conducted extensive experimental testing on a short-wrap vapor injection scroll compressor specifically designed for electric vehicle applications. The test rig was built using the secondary refrigerant calorimeter method, a highly accurate technique for measuring compressor performance. The setup allowed precise control and measurement of suction pressure, discharge pressure, injection pressure, refrigerant mass flow rates, power consumption, heating capacity, and discharge temperature under controlled thermal conditions.
The experimental campaign focused on low-temperature heating conditions, simulating a nominal heat pump operating scenario with an evaporation temperature of -15°C and a condensation temperature of 35°C—representative of harsh winter environments where EV heat pumps are most challenged. Tests were performed at two compressor speeds: 5,000 and 6,000 revolutions per minute (r/min), covering a range of injection pressures from 0.18 to 0.33 MPa, including a no-injection baseline. The refrigerant used was R134a, a common choice in automotive systems, with FVC68D lubricant ensuring proper mechanical function.
The results were compelling. When comparing the simulation outputs to experimental data, the new model demonstrated exceptional accuracy. Across all measured parameters—including total refrigerant mass flow, compressor power, heating COP, heating capacity, and discharge temperature—the maximum deviation between simulation and test results remained within 8%. Notably, at 5,000 r/min, errors in heating capacity and discharge temperature were as low as 4.3% and 2.1%, respectively. Even at the higher speed of 6,000 r/min, where dynamic effects are more pronounced, the errors did not exceed 6.1% for heating capacity and 2.8% for discharge temperature.
This level of precision marks a significant improvement over conventional isentropic models. When the same operating conditions were analyzed using the traditional approach, discrepancies with experimental data reached as high as 20%. The isentropic model overpredicted refrigerant mass flow due to the assumption of zero leakage and perfect volumetric efficiency. This artificially inflated flow rate led to underestimated discharge temperatures and overestimated heating performance, creating a misleading picture of system capability.
The research also uncovered important operational insights. For instance, the data revealed that increasing injection pressure does not always yield better cooling of the discharge gas. At higher injection pressures, the injected refrigerant’s temperature can exceed that of the partially compressed gas in the chamber, resulting in a reheating effect rather than cooling. This phenomenon explains the observed rebound in discharge temperature at elevated injection pressures, a critical consideration for system control strategies.
Another key finding was the impact of compressor speed on leakage and heat transfer dynamics. At higher rotational speeds, the duration of each compression cycle shortens, reducing the time available for heat exchange with the surroundings. This leads to a more adiabatic-like process, slightly increasing discharge temperature. However, higher speeds also increase leakage rates due to greater pressure gradients and fluid velocities, partially offsetting the thermal effect. The new model successfully captures these competing influences, providing a balanced and realistic prediction.
From an engineering design perspective, the implications of this research are profound. Accurate simulation tools enable faster and more reliable development of EV thermal systems. Designers can now evaluate different compressor geometries, injection strategies, and operating conditions with greater confidence, reducing the need for costly and time-consuming physical prototypes. The model can be used to optimize injection timing, port location, and scroll wrap design to minimize leakage and maximize heat transfer efficiency.
Moreover, the ability to predict discharge temperature accurately is crucial for preventing compressor overheating, a common failure mode in vapor injection systems. By simulating worst-case scenarios, engineers can design adequate cooling mechanisms and implement intelligent control algorithms that adjust injection pressure in real time to maintain safe operating temperatures.
The study also contributes to the broader goal of improving EV energy efficiency and extending driving range. Heating in cold weather is one of the largest auxiliary energy drains on an EV battery. By enabling more precise modeling of heat pump performance, this research supports the development of systems that deliver higher COP values, meaning more heat output per unit of electrical input. This translates directly into longer range and improved user satisfaction in winter conditions.
The interdisciplinary nature of the research team underscores the complexity of modern automotive thermal systems. Collaboration between experts in energy and power engineering, mechanical design, fire safety, and infrastructure planning brought diverse perspectives to the problem. The inclusion of researchers from the Shanghai Fire Research Institute, for example, highlights the importance of thermal safety in high-performance compressors, where excessive temperatures can pose risks to both system integrity and passenger safety.
Looking ahead, the framework developed in this study can be extended to other refrigerants, including next-generation low-global-warming-potential (GWP) alternatives such as R1234yf or CO₂ (R744). It can also be integrated into larger system-level simulations that include evaporators, condensers, expansion valves, and cabin thermal dynamics. Such holistic models would allow for co-optimization of the entire thermal management system, balancing heating, cooling, battery thermal regulation, and motor cooling demands.
In conclusion, the work by Hu Jiayun and colleagues represents a significant advancement in the thermodynamic modeling of vapor injection scroll compressors for electric vehicles. By moving beyond idealized assumptions and embracing the complexities of real-world operation, their one-dimensional simulation model delivers unprecedented accuracy in predicting compressor performance. The validation against experimental data under realistic low-temperature conditions confirms its reliability and practical utility.
This research not only enhances the theoretical understanding of scroll compressor dynamics but also provides a powerful tool for engineers striving to improve the efficiency, reliability, and performance of EV thermal systems. As the automotive industry continues its electrification journey, such innovations will be essential in overcoming the challenges of climate control and ensuring that electric vehicles remain comfortable, efficient, and dependable in all weather conditions.
Hu Jiayun, Miao Yanming, Tan Mingfei, Zhang Bin, Li Chao, He Qize, Wang Jiayun, Li Kang. Thermodynamic study of vapor injection scroll compressors for electric vehicles considering internal leakage and heat transfer. Fluid Machinery, 2024, 52(11): 97-104. doi:10.3969/j.issn.1005-0329.2024.11.013