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Model-Based Condenser Fan Speed Optimization of Vapor Compression Systems

Author

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  • Sebastian Angermeier

    (Institute of Thermodynamics and Fluid Mechanics, Technische Universität Ilmenau, 98684 Ilmenau, Germany
    MAHLE GmbH, Pragstr. 26–46, 70376 Stuttgart, Germany)

  • Christian Karcher

    (Institute of Thermodynamics and Fluid Mechanics, Technische Universität Ilmenau, 98684 Ilmenau, Germany)

Abstract

Vapor compression systems (VCS) cover a wide range of applications and consume large amounts of energy. In this context, previous research identified the optimization of the condenser fans speed as a promising measure to improve the energy efficiency of VCS. The present paper introduces a steady-state modeling approach of an air-cooled VCS to predict the ideal condenser fan speed. The model consists of a hybrid characterization of the main components of a VCS and the optimization problem is formulated as minimizing the total energy consumption by respectively adjusting the condenser fan and compressor speed. In contrast to optimization strategies found in the literature, the proposed model does not relay on algorithms, but provides a single optimization term to predict the ideal fan speed. A detailed experimental validation demonstrates the feasibility of the model approach and further suggests that the ideal condenser fan speed can be calculated with sufficient precision, assuming constant evaporating pressure, compressor efficiency, subcooling, and superheating, respectively. In addition, a control strategy based on the developed model is presented, which is able to drive the VCS to its optimal operation. Therefore, the study provides a crucial input for set-point optimization and steady-state modeling of air-cooled vapor compression systems.

Suggested Citation

  • Sebastian Angermeier & Christian Karcher, 2020. "Model-Based Condenser Fan Speed Optimization of Vapor Compression Systems," Energies, MDPI, vol. 13(22), pages 1-26, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:22:p:6012-:d:446675
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    References listed on IDEAS

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    1. Farinaz Behrooz & Norman Mariun & Mohammad Hamiruce Marhaban & Mohd Amran Mohd Radzi & Abdul Rahman Ramli, 2018. "Review of Control Techniques for HVAC Systems—Nonlinearity Approaches Based on Fuzzy Cognitive Maps," Energies, MDPI, vol. 11(3), pages 1-41, February.
    2. Ji-Hyun Shin & Yong-In Kim & Young-Hum Cho, 2019. "Development of Operating Method of Multi-Geothermal Heat Pump Systems Using Variable Water Flow Rate Control and a COP Prediction Model Based on ANN," Energies, MDPI, vol. 12(20), pages 1-18, October.
    3. Zhao, Lei & Cai, Wenjian & Ding, Xudong & Chang, Weichung, 2013. "Model-based optimization for vapor compression refrigeration cycle," Energy, Elsevier, vol. 55(C), pages 392-402.
    4. Huang, Yanjun & Khajepour, Amir & Ding, Haitao & Bagheri, Farshid & Bahrami, Majid, 2017. "An energy-saving set-point optimizer with a sliding mode controller for automotive air-conditioning/refrigeration systems," Applied Energy, Elsevier, vol. 188(C), pages 576-585.
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    Cited by:

    1. Ma, Jing & Sun, Yongfei & Zhang, Shiang, 2023. "Experimental investigation on energy consumption of power battery integrated thermal management system," Energy, Elsevier, vol. 270(C).

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