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<p>This study adopted a combined strategy of Response Surface Methodology (RSM) and Multi-Objective Particle Swarm Optimization (MOPSO) to optimize the energy efficiency of an indirect liquid cooling system for electric construction vehicle air conditioning. First, an experimental setup of the cooling system was constructed to evaluate alternating current performance. Subsequently, a PSO-SVR prediction model was proposed to reduce the testing period and lay the foundation for establishing semi-empirical relationship. Following this, a response surface model was developed based on Box-Behnken design and RSM, incorporating input parameters (compressor speed, electronic valve opening, coolant inlet temperature, and pump speed) and objective functions (refrigeration capacity and power consumption). The model of accuracy was validated through Analysis of Variance (ANOVA). Finally, MOPSO was implemented based on the response model to maximize refrigeration capacity while minimize power consumption, and the optimization results were verified experimentally. The results demonstrated that the response surface model for refrigeration capacity and power consumption exhibited excellent performance, with maximum deviations between predicted and experimental values within 3%. Compared with the original scheme, the OS1 showed a slight increase in refrigeration capacity and a 4.42% decrease in power consumption. This approach might strengthen energy efficiency in cooling systems and the solutions to engineering applications.</p>

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Performance optimization of air conditioning in electric construction vehicle based on RSM and MOPSO algorithm

  • 期刊论文

  • 发表日期：

    2026年5月8日

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  • Jinchuan Song,
  • Jiaxin Liu,
  • Baozhong Wang,
  • Tianci Zhang,
  • Martin Kreschel

摘要

This study adopted a combined strategy of Response Surface Methodology (RSM) and Multi-Objective Particle Swarm Optimization (MOPSO) to optimize the energy efficiency of an indirect liquid cooling system for electric construction vehicle air conditioning. First, an experimental setup of the cooling system was constructed to evaluate alternating current performance. Subsequently, a PSO-SVR prediction model was proposed to reduce the testing period and lay the foundation for establishing semi-empirical relationship. Following this, a response surface model was developed based on Box-Behnken design and RSM, incorporating input parameters (compressor speed, electronic valve opening, coolant inlet temperature, and pump speed) and objective functions (refrigeration capacity and power consumption). The model of accuracy was validated through Analysis of Variance (ANOVA). Finally, MOPSO was implemented based on the response model to maximize refrigeration capacity while minimize power consumption, and the optimization results were verified experimentally. The results demonstrated that the response surface model for refrigeration capacity and power consumption exhibited excellent performance, with maximum deviations between predicted and experimental values within 3%. Compared with the original scheme, the OS1 showed a slight increase in refrigeration capacity and a 4.42% decrease in power consumption. This approach might strengthen energy efficiency in cooling systems and the solutions to engineering applications.