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<p>The key to the advancement of thermal management technology lies in the optimization of control strategies. Building upon the traditional PID and fuzzy PID control, this paper presents a fuzzy PID control based on the Snow Geese Algorithm (SGA), aiming to study the refrigeration performance of the battery and the cabin under summer high-temperature conditions and the optimal cabin temperature control. The results show that when the environmental temperature was at 35&#xa0;°C and 40&#xa0;°C, the power battery temperature dropped to the target value of 25&#xa0;°C around 1540 and 2000&#xa0;s, respectively. Based on different temperature control strategies, under the environmental temperatures of 35&#xa0;°C and 40&#xa0;°C, the cabin temperature controlled by SGA-fuzzy PID was respectively stabilized at 25.07&#xa0;°C and 24.97&#xa0;°C, and the system response was superior to that of traditional PID control and fuzzy PID control. In comparison with the traditional PID control, the SGA-fuzzy PID control can increase the performance coefficient by 2.77 and 2.14%, respectively, and reduce the compressor power consumption by 0.82 and 1.51%, respectively. In comparison with the fuzzy PID control, the SGA-fuzzy PID control can increase the performance coefficient by 2.59 and 2.45%, respectively, and reduce the compressor power consumption by 0.48 and 1.39%, respectively.</p>

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Research on thermal management control strategies for batteries and air conditioning systems in pure electric vehicles

  • 期刊论文

  • 发表日期：

    2026年1月27日

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  • Zhaoju Qin,
  • Chenyang Yin,
  • Weihong Weng,
  • Weizheng Zhang,
  • Dong Liu,
  • Zhiao Zhang,
  • Zhen Han

摘要

The key to the advancement of thermal management technology lies in the optimization of control strategies. Building upon the traditional PID and fuzzy PID control, this paper presents a fuzzy PID control based on the Snow Geese Algorithm (SGA), aiming to study the refrigeration performance of the battery and the cabin under summer high-temperature conditions and the optimal cabin temperature control. The results show that when the environmental temperature was at 35 °C and 40 °C, the power battery temperature dropped to the target value of 25 °C around 1540 and 2000 s, respectively. Based on different temperature control strategies, under the environmental temperatures of 35 °C and 40 °C, the cabin temperature controlled by SGA-fuzzy PID was respectively stabilized at 25.07 °C and 24.97 °C, and the system response was superior to that of traditional PID control and fuzzy PID control. In comparison with the traditional PID control, the SGA-fuzzy PID control can increase the performance coefficient by 2.77 and 2.14%, respectively, and reduce the compressor power consumption by 0.82 and 1.51%, respectively. In comparison with the fuzzy PID control, the SGA-fuzzy PID control can increase the performance coefficient by 2.59 and 2.45%, respectively, and reduce the compressor power consumption by 0.48 and 1.39%, respectively.