IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i9p3183-d803126.html
   My bibliography  Save this article

Research on Energy Saving of PHEV Air Conditioning System Based on Reducing Air Backflow in Underhood

Author

Listed:
  • Haibo Wu

    (School of Automotive Studies, Tongji University, Shanghai 200070, China
    SAIC Volkswagen Automotive Co., Ltd., Shanghai 201800, China)

  • Xingwang Tang

    (School of Automotive Studies, Tongji University, Shanghai 200070, China)

  • Sichuan Xu

    (School of Automotive Studies, Tongji University, Shanghai 200070, China)

  • Jiangbin Zhou

    (SAIC Volkswagen Automotive Co., Ltd., Shanghai 201800, China)

Abstract

A novel method characterizing the air backflow of the underhood in order to improve the thermal efficiency of the air conditioning system (ACS) and reduce the energy consumption of PHEV is proposed in this paper. In addition, a 1D model for analyzing air backflow occurring in the underhood is established and a CFD method for calculating air backflow rate and distribution is proposed. It is found that the decrease in the air backflow rate of the underhood helps to improve the refrigeration capacity of the ACS, and when the backflow ratio cannot be reduced below 10%, the air backflow should be distributed as evenly as possible at the front end of the condenser. Moreover, in order to eliminate the impact of backflow on the underhood of PHEV, the gap between the radiator and the bracket is sealed and the gap around the air guide is reduced. Compared with the original structure, the backflow rate of the optimized structure is reduced from 32.7% to 9.3% and the cabin temperature can be reduced by 3–5 °C.

Suggested Citation

  • Haibo Wu & Xingwang Tang & Sichuan Xu & Jiangbin Zhou, 2022. "Research on Energy Saving of PHEV Air Conditioning System Based on Reducing Air Backflow in Underhood," Energies, MDPI, vol. 15(9), pages 1-15, April.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3183-:d:803126
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/9/3183/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/9/3183/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Mahmoodi-k, Mehdi & Montazeri, Morteza & Madanipour, Vahid, 2021. "Simultaneous multi-objective optimization of a PHEV power management system and component sizing in real world traffic condition," Energy, Elsevier, vol. 233(C).
    3. Peng, Jiankun & He, Hongwen & Xiong, Rui, 2017. "Rule based energy management strategy for a series–parallel plug-in hybrid electric bus optimized by dynamic programming," Applied Energy, Elsevier, vol. 185(P2), pages 1633-1643.
    4. Hemmati, S. & Doshi, N. & Hanover, D. & Morgan, C. & Shahbakhti, M., 2021. "Integrated cabin heating and powertrain thermal energy management for a connected hybrid electric vehicle," Applied Energy, Elsevier, vol. 283(C).
    5. Chen, Zeyu & Xiong, Rui & Cao, Jiayi, 2016. "Particle swarm optimization-based optimal power management of plug-in hybrid electric vehicles considering uncertain driving conditions," Energy, Elsevier, vol. 96(C), pages 197-208.
    6. Pengxiang Song & Yulong Lei & Yao Fu, 2020. "Multi-Objective Optimization and Matching of Power Source for PHEV Based on Genetic Algorithm," Energies, MDPI, vol. 13(5), pages 1-20, March.
    7. Solmaz, Hamit & Ardebili, Seyed Mohammad Safieddin & Calam, Alper & Yılmaz, Emre & İpci, Duygu, 2021. "Prediction of performance and exhaust emissions of a CI engine fueled with multi-wall carbon nanotube doped biodiesel-diesel blends using response surface method," Energy, Elsevier, vol. 227(C).
    Full references (including those not matched with items on IDEAS)

    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. Zeyu Chen & Jiahuan Lu & Bo Liu & Nan Zhou & Shijie Li, 2020. "Optimal Energy Management of Plug-In Hybrid Electric Vehicles Concerning the Entire Lifespan of Lithium-Ion Batteries," Energies, MDPI, vol. 13(10), pages 1-15, May.
    2. Wang, Yue & Zeng, Xiaohua & Song, Dafeng & Yang, Nannan, 2019. "Optimal rule design methodology for energy management strategy of a power-split hybrid electric bus," Energy, Elsevier, vol. 185(C), pages 1086-1099.
    3. Li, Xiaolong & Xie, Changjun & Quan, Shuhai & Huang, Liang & Fang, Wei, 2018. "Energy management strategy of thermoelectric generation for localized air conditioners in commercial vehicles based on 48 V electrical system," Applied Energy, Elsevier, vol. 231(C), pages 887-900.
    4. Mahmoodi-k, Mehdi & Montazeri, Morteza & Madanipour, Vahid, 2021. "Simultaneous multi-objective optimization of a PHEV power management system and component sizing in real world traffic condition," Energy, Elsevier, vol. 233(C).
    5. Liu, Hui & Li, Xunming & Wang, Weida & Han, Lijin & Xiang, Changle, 2018. "Markov velocity predictor and radial basis function neural network-based real-time energy management strategy for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 152(C), pages 427-444.
    6. Vamsi Krishna Reddy, Aala Kalananda & Venkata Lakshmi Narayana, Komanapalli, 2022. "Meta-heuristics optimization in electric vehicles -an extensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    7. Ahmed M. Ali & Dirk Söffker, 2018. "Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions," Energies, MDPI, vol. 11(3), pages 1-24, February.
    8. Nie, Zhigen & Jia, Yuan & Wang, Wanqiong & Chen, Zheng & Outbib, Rachid, 2022. "Co-optimization of speed planning and energy management for intelligent fuel cell hybrid vehicle considering complex traffic conditions," Energy, Elsevier, vol. 247(C).
    9. Yongjian Zhou & Rong Yang & Song Zhang & Kejun Lan & Wei Huang, 2023. "Optimization of Power-System Parameters and Energy-Management Strategy Research on Hybrid Heavy-Duty Trucks," Energies, MDPI, vol. 16(17), pages 1-21, August.
    10. Liu, Hanwu & Lei, Yulong & Fu, Yao & Li, Xingzhong, 2022. "A novel hybrid-point-line energy management strategy based on multi-objective optimization for range-extended electric vehicle," Energy, Elsevier, vol. 247(C).
    11. Farmann, Alexander & Waag, Wladislaw & Sauer, Dirk Uwe, 2016. "Application-specific electrical characterization of high power batteries with lithium titanate anodes for electric vehicles," Energy, Elsevier, vol. 112(C), pages 294-306.
    12. Bizon, Nicu, 2019. "Real-time optimization strategies of Fuel Cell Hybrid Power Systems based on Load-following control: A new strategy, and a comparative study of topologies and fuel economy obtained," Applied Energy, Elsevier, vol. 241(C), pages 444-460.
    13. Xiong, Rui & Duan, Yanzhou & Cao, Jiayi & Yu, Quanqing, 2018. "Battery and ultracapacitor in-the-loop approach to validate a real-time power management method for an all-climate electric vehicle," Applied Energy, Elsevier, vol. 217(C), pages 153-165.
    14. Bizon, Nicu, 2019. "Efficient fuel economy strategies for the Fuel Cell Hybrid Power Systems under variable renewable/load power profile," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    15. Li, Shuangqi & Zhao, Pengfei & Gu, Chenghong & Huo, Da & Zeng, Xianwu & Pei, Xiaoze & Cheng, Shuang & Li, Jianwei, 2022. "Online battery-protective vehicle to grid behavior management," Energy, Elsevier, vol. 243(C).
    16. Yang, Jibin & Xu, Xiaohui & Peng, Yiqiang & Zhang, Jiye & Song, Pengyun, 2019. "Modeling and optimal energy management strategy for a catenary-battery-ultracapacitor based hybrid tramway," Energy, Elsevier, vol. 183(C), pages 1123-1135.
    17. Hannan, M.A. & Ali, Jamal A. & Mohamed, Azah & Hussain, Aini, 2018. "Optimization techniques to enhance the performance of induction motor drives: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1611-1626.
    18. Du, Guodong & Zou, Yuan & Zhang, Xudong & Kong, Zehui & Wu, Jinlong & He, Dingbo, 2019. "Intelligent energy management for hybrid electric tracked vehicles using online reinforcement learning," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    19. Vitale, F. & Rispoli, N. & Sorrentino, M. & Rosen, M.A. & Pianese, C., 2021. "On the use of dynamic programming for optimal energy management of grid-connected reversible solid oxide cell-based renewable microgrids," Energy, Elsevier, vol. 225(C).
    20. Zhu, Tao & Wills, Richard G.A. & Lot, Roberto & Ruan, Haijun & Jiang, Zhihao, 2021. "Adaptive energy management of a battery-supercapacitor energy storage system for electric vehicles based on flexible perception and neural network fitting," Applied Energy, Elsevier, vol. 292(C).

    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:gam:jeners:v:15:y:2022:i:9:p:3183-:d:803126. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.