针对电驱车间空调系统运行能耗高和工作效率低的问题，基于IBK-IPS算法提出了一种考虑空调系统各设备相互约束的动态节能优化方法。首先，分析空调系统各设备之间的影响机理，建立各设备的能耗和约束条件数学模型，构建系统运行能耗优化目标函数；接着，提出了一种基于改进黑翅鸢与粒子群（IBK-IPS）的算法，对空调系统各设备的水温、流量和风量等运行参数进行优化，以提高空调系统运行参数控制的精度和效果；然后，利用Simulink平台建立空调系统冷却水系统、冷冻水系统的能耗仿真模型，并通过仿真实验来验证运行参数优化的效果和准确性；最后，将该方法在某电驱车间进行实际应用，以验证所提方法的实际效果和可行性。仿真实验及实际应用测试结果表明：系统的运行能耗得到有效降低，节能率达到11.23%~34.68%；系统的运行能效得到有效优化，运行能效提升了11.53%~41.75%；相较于PS、BK、BK-PS算法，IBK-IPS算法的节能效果最优，且收敛速度分别提升了27.27%、61.90%、69.23%；在实际应用测试中，优化后系统在5种不同负荷下的节能率分别为22.61%、17.24%、7.48%、14.97%、12.64%。综上所述，该文提出的节能优化方法能够有效地解决电驱车间空调系统运行能耗高和工作效率低的问题，具有良好的节能效果和实用性，可为空调系统节能优化研究提供新的思路。

To address the problems of high operational energy consumption and low efficiency in air-conditioning system for electric drive workshops, this paper proposed a dynamic energy-saving optimization method based on the IBK-IPS algorithm, taking into account the mutual constraints of each equipment of the air-conditioning system. Firstly, the influence mechanism among system components were analyzed, and mathematical models of energy consumption and constraints conditions for each device were established to an objective function for system energy consumption. Then, an improved Black Kite-Particle Swarm (IBK-IPS) algorithm was introduced to optimize operational parameters such as water temperature, flow rate, and air volume, thereby improving the accuracy and effectiveness of system parameter control. Subsequently, a simulation model of the cooling water system and chilled water system of the air-conditioning system is developed using the Simulink platform to evaluate the performance and accuracy of the parameter optimization. Finally, the method is practically applied in an electric drive workshop to verify the practical effect and feasibility of the proposed method. The results of simulation experiments and practical application tests show that: the operational energy consumption of the system is effectively reduced, achieving an energy-saving rate of 11.23%~34.68%, and improves operational energy efficiency by 11.53%~41.75%. Compared with the PS, BK, and BK-PS algorithms, the IBK-IPS algorithm delivers superior energy-saving performance, with convergence speeds improved by 27.27%, 61.90%, and 69.23%, respectively. In real-world testing under five different load conditions, the optimized system achieved energy-saving rates of 22.61%, 17.24%, 7.48%, 14.97%, and 12.64%, respectively. In summary, the energy-saving optimization method proposed in this paper can effectively solve the problem of high energy consumption and low efficiency of the air-conditioning system operation in the electric drive workshop, which has good energy-saving effect and practicability, and can provide new ideas for the research of energy-saving optimization of air-conditioning system.