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An energy-saving set-point optimizer with a sliding mode controller for automotive air-conditioning/refrigeration systems

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  • Huang, Yanjun
  • Khajepour, Amir
  • Ding, Haitao
  • Bagheri, Farshid
  • Bahrami, Majid

Abstract

This paper presents an energy-saving controller for automotive air-conditioning/refrigeration (A/C-R) systems. With their extensive application in homes, industry, and vehicles, A/C-R systems are consuming considerable amounts of energy. The proposed controller consists of two different time-scale layers. The outer or the slow time-scale layer called a set-point optimizer is used to find the set points related to energy efficiency by using the steady state model; whereas, the inner or the fast time-scale layer is used to track the obtained set points. In the inner loop, thanks to its robustness, a sliding mode controller (SMC) is utilized to track the set point of the cargo temperature. The currently used on/off controller is presented and employed as a basis for comparison to the proposed controller. More importantly, the real experimental results under several disturbed scenarios are analysed to demonstrate how the proposed controller can improve performance while reducing the energy consumption by 9% comparing with the on/off controller. The controller is suitable for any type of A/C-R system even though it is applied to an automotive A/C-R system in this paper.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:188:y:2017:i:c:p:576-585
    DOI: 10.1016/j.apenergy.2016.12.033
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    References listed on IDEAS

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    Cited by:

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    7. Ali Alahmer & Rania M. Ghoniem, 2023. "Improving Automotive Air Conditioning System Performance Using Composite Nano-Lubricants and Fuzzy Modeling Optimization," Sustainability, MDPI, vol. 15(12), pages 1-16, June.
    8. Sebastian Angermeier & Christian Karcher, 2020. "Model-Based Condenser Fan Speed Optimization of Vapor Compression Systems," Energies, MDPI, vol. 13(22), pages 1-26, November.
    9. Xu Hu & Yisong Chen & Zhensen Ding & Liang Gu, 2019. "Vehicle Optimal Control Design to Meet the 1.5 °C Target: A Control Design Framework for Vehicle Subsystems," Energies, MDPI, vol. 12(16), pages 1-21, August.
    10. Fard, Soheil Mohagheghi & Huang, Yanjun & Khazraee, Milad & Khajepour, Amir, 2017. "A novel anti-idling system for service vehicles," Energy, Elsevier, vol. 127(C), pages 650-659.
    11. Huang, Yanjun & Wang, Hong & Khajepour, Amir & Li, Bin & Ji, Jie & Zhao, Kegang & Hu, Chuan, 2018. "A review of power management strategies and component sizing methods for hybrid vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 132-144.
    12. Huang, Xianghui & Li, Kuining & Xie, Yi & Liu, Bin & Liu, Jiangyan & Liu, Zhaoming & Mou, Lunjie, 2022. "A novel multistage constant compressor speed control strategy of electric vehicle air conditioning system based on genetic algorithm," Energy, Elsevier, vol. 241(C).
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    14. Frank Florez & Pedro Fernández de Córdoba & José Luis Higón & Gerard Olivar & John Taborda, 2019. "Modeling, Simulation, and Temperature Control of a Thermal Zone with Sliding Modes Strategy," Mathematics, MDPI, vol. 7(6), pages 1-13, June.

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