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摘 要： 本文探讨了制冷系统仿真的一种新方法。该方法在系统层面，采用传统的基于质量守恒、能量守恒、动量守恒的建模思路，保持模型的通用性。在部件层面，采用人工神经网络技术，建立无内部迭代的多输入、多输出的部件神经网络模型，使得部件模型具备鲁棒性和快速性。以房间空调器为例，演示了这种混合仿真方法的基本实现步骤和仿真效果。 关键词： 制冷系统；模型；仿真；神经网络 Hybrid Modeling Of Refrigeration System Based On Neural Network Component Models Zhang Chunlu1, Zhao Lingxiao2 1 Tongji University, 2 Carrier China Rnbsp; Abstract : This paper presents a new hybrid modeling approach of refrigeration systems. At the system level, it follows the first principles to keep the conservation of mass, energy, and momentum, respectively. At the component level, the physics-based component models are entirely or partially replaced with neural networks. Component neural networks can be trained using the data from laboratory or from the well-validated physics-based component models. Numerical comparisons between the system model consisting of component neural networks and that consisting of physics-based component models indicate that the two system modeling approaches give very close predictions in a wide range of operating conditions. With the proposed hybrid modeling approach, robustness and speed of refrigeration system modeling can be significantly improved. Keywords: Refrigerating system; Model; Simulation; Neural network