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

Author

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  • 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
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    References listed on IDEAS

    as
    1. 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).
    2. 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.
    3. 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).
    4. 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.
    5. 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.
    6. 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).
    7. 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.
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