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Data-driven model predictive control of transcritical CO2 systems for cabin thermal management in cooling mode by Haidan Wang; Wenyi Wang; Yulong Song; Xu Yang; Paolo Valdiserri; Eugenia Rossi di Schio; Gangxu Yu; Feng Cao is a Engineering article available to read on EtoBox.
What is Data-driven model predictive control of transcritical CO2 systems for cabin thermal management in cooling mode about?
The transcritical CO 2 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 CO 2 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 p
Who reads Data-driven model predictive control of transcritical CO2 systems for cabin thermal management in cooling mode?
It is typically read by researchers, students, and practitioners in Engineering.
- Author
- Haidan Wang; Wenyi Wang; Yulong Song; Xu Yang; Paolo Valdiserri; Eugenia Rossi di Schio; Gangxu Yu; Feng Cao
- Publisher
- Elsevier BV
- Published
- 2023
- Language
- EN
- Field
- Engineering (Physical Sciences)