IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v188y2017icp576-585.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261916317962
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2016.12.033?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chua, K.J. & Chou, S.K. & Yang, W.M. & Yan, J., 2013. "Achieving better energy-efficient air conditioning – A review of technologies and strategies," Applied Energy, Elsevier, vol. 104(C), pages 87-104.
    2. Hu, Xiaosong & Li, Shengbo Eben & Jia, Zhenzhong & Egardt, Bo, 2014. "Enhanced sample entropy-based health management of Li-ion battery for electrified vehicles," Energy, Elsevier, vol. 64(C), pages 953-960.
    3. Mohanraj, M. & Jayaraj, S. & Muraleedharan, C., 2012. "Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1340-1358.
    4. Lygouras, J.N. & Kodogiannis, V.S. & Pachidis, Th. & Tarchanidis, K.N. & Koukourlis, C.S., 2008. "Variable structure TITO fuzzy-logic controller implementation for a solar air-conditioning system," Applied Energy, Elsevier, vol. 85(4), pages 190-203, April.
    5. Huang, Yanjun & Khajepour, Amir & Bagheri, Farshid & Bahrami, Majid, 2016. "Optimal energy-efficient predictive controllers in automotive air-conditioning/refrigeration systems," Applied Energy, Elsevier, vol. 184(C), pages 605-618.
    6. Oh, Myoung Su & Ahn, Jae Hwan & Kim, Dong Woo & Jang, Dong Soo & Kim, Yongchan, 2014. "Thermal comfort and energy saving in a vehicle compartment using a localized air-conditioning system," Applied Energy, Elsevier, vol. 133(C), pages 14-21.
    7. Huang, Yanjun & Khajepour, Amir & Wang, Hong, 2016. "A predictive power management controller for service vehicle anti-idling systems without a priori information," Applied Energy, Elsevier, vol. 182(C), pages 548-557.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. He, Hongwen & Yan, Mei & Sun, Chao & Peng, Jiankun & Li, Menglin & Jia, Hui, 2018. "Predictive air-conditioner control for electric buses with passenger amount variation forecast☆," Applied Energy, Elsevier, vol. 227(C), pages 249-261.
    2. 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.
    3. Barambones, Oscar & Cortajarena, Jose A. & Calvo, Isidro & Gonzalez de Durana, Jose M. & Alkorta, Patxi & Karami-Mollaee, A., 2019. "Variable speed wind turbine control scheme using a robust wind torque estimation," Renewable Energy, Elsevier, vol. 133(C), pages 354-366.
    4. 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.
    5. Cui, Taowen & Zhao, Wanzhong & Wang, Chunyan, 2019. "Design optimization of vehicle EHPS system based on multi-objective genetic algorithm," Energy, Elsevier, vol. 179(C), pages 100-110.
    6. Ono, Hitoi & Ohtani, Yuichi & Matsuo, Minoru & Yamaguchi, Toru & Yokoyama, Ryohei, 2021. "Optimal operation of heat source and air conditioning system with thermal storage tank using nonlinear programming," Energy, Elsevier, vol. 222(C).
    7. Bie, Yiming & Liu, Yajun & Li, Shiwu & Wang, Linhong, 2022. "HVAC operation planning for electric bus trips based on chance-constrained programming," Energy, Elsevier, vol. 258(C).
    8. 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).
    9. Jieun Lee & Seokkwon Jeong, 2021. "Robust Temperature Control of a Variable-Speed Refrigeration System Based on Sliding Mode Control with Optimal Parameters Derived Using the Genetic Algorithm," Energies, MDPI, vol. 14(19), pages 1-18, October.
    10. Cheng, Fanyong & Cui, Can & Cai, Wenjian & Zhang, Xin & Ge, Yuan & Li, Bingxu, 2022. "A novel data-driven air balancing method with energy-saving constraint strategy to minimize the energy consumption of ventilation system," Energy, Elsevier, vol. 239(PB).
    11. 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.
    12. 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.
    13. Sebastian Angermeier & Christian Karcher, 2020. "Model-Based Condenser Fan Speed Optimization of Vapor Compression Systems," Energies, MDPI, vol. 13(22), pages 1-26, November.
    14. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Huang, Yanjun & Khajepour, Amir & Bagheri, Farshid & Bahrami, Majid, 2016. "Optimal energy-efficient predictive controllers in automotive air-conditioning/refrigeration systems," Applied Energy, Elsevier, vol. 184(C), pages 605-618.
    2. Tatchell-Evans, Morgan & Kapur, Nik & Summers, Jonathan & Thompson, Harvey & Oldham, Dan, 2017. "An experimental and theoretical investigation of the extent of bypass air within data centres employing aisle containment, and its impact on power consumption," Applied Energy, Elsevier, vol. 186(P3), pages 457-469.
    3. Abdul Mujeebu, Muhammad & Alshamrani, Othman Subhi, 2016. "Prospects of energy conservation and management in buildings – The Saudi Arabian scenario versus global trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1647-1663.
    4. Tong, Zheming & Chen, Yujiao & Malkawi, Ali, 2016. "Defining the Influence Region in neighborhood-scale CFD simulations for natural ventilation design," Applied Energy, Elsevier, vol. 182(C), pages 625-633.
    5. Han, Youhua & Liu, Yang & Lu, Shixiang & Basalike, Pie & Zhang, Jili, 2021. "Electrical performance and power prediction of a roll-bond photovoltaic thermal array under dewing and frosting conditions," Energy, Elsevier, vol. 237(C).
    6. Tong, Zheming & Chen, Yujiao & Malkawi, Ali, 2017. "Estimating natural ventilation potential for high-rise buildings considering boundary layer meteorology," Applied Energy, Elsevier, vol. 193(C), pages 276-286.
    7. Whei-Min Lin & Chung-Yuen Yang & Ming-Tang Tsai & Hong-Jey Gow, 2019. "The Optimized Energy Saving of a Refrigerating Chamber," Energies, MDPI, vol. 12(10), pages 1-16, May.
    8. Jing, Gang & Cai, Wenjian & Zhang, Xin & Cui, Can & Yin, Xiaohong & Xian, Huacai, 2019. "An energy-saving oriented air balancing strategy for multi-zone demand-controlled ventilation system," Energy, Elsevier, vol. 172(C), pages 1053-1065.
    9. Ren, Hongbin & Zhao, Yuzhuang & Chen, Sizhong & Wang, Taipeng, 2019. "Design and implementation of a battery management system with active charge balance based on the SOC and SOH online estimation," Energy, Elsevier, vol. 166(C), pages 908-917.
    10. Niamsuwan, Sathit & Kittisupakorn, Paisan & Suwatthikul, Ajaree, 2015. "Enhancement of energy efficiency in a paint curing oven via CFD approach: Case study in an air-conditioning plant," Applied Energy, Elsevier, vol. 156(C), pages 465-477.
    11. Svetlana Ratner & Yuri Chepurko & Larisa Drobyshecskaya & Anna Petrovskaya, 2018. "Management of Energy Enterprises: Energy-efficiency Approach in Solar Collectors Industry: The Case of Russia," International Journal of Energy Economics and Policy, Econjournals, vol. 8(4), pages 237-243.
    12. Yang, Xiaohu & Yu, Jiabang & Guo, Zengxu & Jin, Liwen & He, Ya-Ling, 2019. "Role of porous metal foam on the heat transfer enhancement for a thermal energy storage tube," Applied Energy, Elsevier, vol. 239(C), pages 142-156.
    13. Wang, Wenqing & Kolditz, Olaf & Nagel, Thomas, 2017. "Parallel finite element modelling of multi-physical processes in thermochemical energy storage devices," Applied Energy, Elsevier, vol. 185(P2), pages 1954-1964.
    14. Yang, Shiyu & Wan, Man Pun & Ng, Bing Feng & Dubey, Swapnil & Henze, Gregor P. & Chen, Wanyu & Baskaran, Krishnamoorthy, 2020. "Experimental study of model predictive control for an air-conditioning system with dedicated outdoor air system," Applied Energy, Elsevier, vol. 257(C).
    15. Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    16. Buratti, Cinzia & Barelli, Linda & Moretti, Elisa, 2012. "Application of artificial neural network to predict thermal transmittance of wooden windows," Applied Energy, Elsevier, vol. 98(C), pages 425-432.
    17. Berecibar, M. & Gandiaga, I. & Villarreal, I. & Omar, N. & Van Mierlo, J. & Van den Bossche, P., 2016. "Critical review of state of health estimation methods of Li-ion batteries for real applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 572-587.
    18. Jani, D.B. & Mishra, Manish & Sahoo, P.K., 2017. "Application of artificial neural network for predicting performance of solid desiccant cooling systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 352-366.
    19. Yuan, Zhipeng & Liu, Qi & Luo, Baojun & Li, Zhenming & Fu, Jianqin & Chen, Jingwei, 2018. "Thermodynamic analysis of different oil flooded compression enhanced vapor injection cycles," Energy, Elsevier, vol. 154(C), pages 553-560.
    20. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:188:y:2017:i:c:p:576-585. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.