LSTM-Based Hybrid Modeling Approach for Control Application of Evaporator Involving Phase Transition

Heat exchangers in vapor compression cycles (VCCs) typically involve phase transitions of the internal refrigerant. While numerous studies have focused on modeling these systems, existing techniques require high computational burden or model switching depending on the refrigerant phase at the heat exchanger outlet. These kinds of problems make each model less suitable for control applications. To overcome these challenges, this study proposes a hybrid model that combines conventional modeling methods with data-driven time series analysis to obtain unified model of heat exchanger with refrigerant phase transition, especially the evaporator in the VCC. The proposed model is fundamentally based on the moving boundary (MB) method, and integrates long short-term memory (LSTM) networks to predict unknown parameters arising from the phase transition. The proposed hybrid model shows high accuracy with the Simulink-Simscape simulation result, and yields higher accuracy compared to a fully data-driven LSTM black-box model, which is another approach to make an unified model. This hybrid approach creates a model that improves accuracy compared to the black-box model and eliminates the need for model switching, ultimately facilitating the design of advanced control.