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A novel multistage constant compressor speed control strategy of electric vehicle air conditioning system based on genetic algorithm

Author

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  • Huang, Xianghui
  • Li, Kuining
  • Xie, Yi
  • Liu, Bin
  • Liu, Jiangyan
  • Liu, Zhaoming
  • Mou, Lunjie

Abstract

This study establishes a passenger cabin-coupled air conditioning model that the heat exchangers adopted in the air conditioning model, are calibrated by testing, during which the parameters for establishing the passenger cabin model are measured. The cabin temperature simulation results of the air conditioning model proposed in this paper fit well with the test results, with a maximum difference of 3 °C. A genetic algorithm (GA) optimization-based multistage constant-compressor speed (MCCS) air conditioning system control strategy is proposed. This control strategy sets the cabin temperature as the input control factor and the compressor speed as the output factor, and different cabin temperature ranges correspond to the MCCSs, which are optimized by the GA. The presented strategy is contrasted with the most commonly used on/off controllers and the proportional integral derivative (PID) controller, and an engineering-applied (EA) air conditioning control strategy. The proposed controller can maintain passenger cabin thermal comfort and save energy simultaneously, and it can be easily applied in engineering. Based on the simulation results, the MCCS controller can save 17.5, 7.5, and 5.8% more energy consumption than the on/off, PID, and EA controllers. Moreover, it can improve the coefficient of performance of the air conditioning system by 5.3 and 3.9% more than the PID and EA controllers. Therefore, the proposed MCCS controller can increase the operation efficiency of electric vehicles AC system.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:241:y:2022:i:c:s0360544221031522
    DOI: 10.1016/j.energy.2021.122903
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    References listed on IDEAS

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    Cited by:

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    2. Shekaina Justin & Wafaa Saleh & Maha M. A. Lashin & Hind Mohammed Albalawi, 2023. "Modeling of Artificial Intelligence-Based Automated Climate Control with Energy Consumption Using Optimal Ensemble Learning on a Pixel Non-Uniformity Metro System," Sustainability, MDPI, vol. 15(18), pages 1-18, September.
    3. 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.
    4. Xiaoxiao Ding & Weirong Zhang & Zhen Yang & Jiajun Wang & Lingtao Liu & Dalong Gao & Dongdong Guo & Jianyin Xiong, 2022. "Effect of Open-Window Gaps on the Thermal Environment inside Vehicles Exposed to Solar Radiation," Energies, MDPI, vol. 15(17), pages 1-18, September.
    5. Qu, Ke & Barreto, Germilly & Iten, Muriel & Wang, Yuhao & Riffat, Saffa, 2023. "Energy and thermal performance of optimised hollow fibre liquid desiccant cooling and dehumidification systems in mediterranean regions: Modelling, validation and case study," Energy, Elsevier, vol. 263(PC).
    6. Hailong Yang & Yonghong Xu & Hongguang Zhang & Jian Zhang & Fubin Yang & Yan Wang & Yuting Wu, 2023. "Experimental Investigation on the Performance of Compressors for Small-Scale Compressed Air Energy Storage in Parallel Mode," Sustainability, MDPI, vol. 15(17), pages 1-29, September.
    7. Dan Dan & Yihang Zhao & Mingshan Wei & Xuehui Wang, 2023. "Review of Thermal Management Technology for Electric Vehicles," Energies, MDPI, vol. 16(12), pages 1-38, June.

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