Data-driven model predictive control of transcritical CO2 systems for cabin thermal management in cooling mode

• Published in: Applied thermal engineering Vol. 235; p. 121337
• Main Authors: Wang, Haidan, Wang, Wenyi, Song, Yulong, Yang, Xu, Valdiserri, Paolo, Rossi di Schio, Eugenia, Yu, Gangxu, Cao, Feng
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 25-11-2023
• Subjects: Air conditioning system; Dynamic thermal model; Energy conservation; Model predictive control; Transcritical CO2 system; Model predictive control; Dynamic thermal model; Air conditioning system; Transcritical CO2 system; Energy conservation
• ISSN: 1359-4311
• DOI: 10.1016/j.applthermaleng.2023.121337

•A data-driven dynamic thermal model for a transcritical CO2 system is proposed.•Proposed model predictive control for energy efficiency and passenger comfort.•Real-time control optimizes discharge pressure in transcritical CO2 systems.•The energy-saving results prove the effectiveness of the proposed strategy.
The transcritical CO2 cabin thermal management system has gained significant attention in the field of electric vehicles due to its outstanding heating performance and environmental advantages. However, ensuring its optimal operation in real-time during vehicle operation poses a challenge. Amongst these challenges, controlling the optimal discharge pressure is particularly difficult. In this paper, we propose a novel model predictive controller that focuses on the cabin cooling mode. The controller utilizes a high-fidelity data-driven dynamic model of the transcritical CO2 system, coupled with a dynamic thermal model of the cabin. By simultaneously controlling the compressor, electronic expansion valve, and indoor fan, the proposed controller enables the cabin thermal management system to operate in real-time at the optimal discharge pressure while ensuring passenger comfort, thereby minimizing the total power consumption of the system. Additionally, two model predictive control strategies, focused on comfort and energy-saving, respectively, are introduced. Through simulations under various conditions over a 6-hour period, comparing the PI controller, the comfort priority model predictive controller reduces energy consumption by 13.33%, and the energy-saving priority model predictive controller achieves a 20.27% reduction. The proposed novel model predictive controller exhibits energy-saving advantages.