An improved intelligent model predictive controller for cooling system of electric vehicle. (5th January 2021)

• Record Type: Journal Article
• Authors: Xie, Yi; Liu, Zhaoming; Li, Kuining; Liu, Jiangyan; Zhang, Yangjun; Dan, Dan; Wu, Cunxue; Wang, Pingzhong; Wang, Xiaobo
• Is Part Of: Applied thermal engineering. Volume 182(2021)
• Publication Date: 2021-01-05
• Subjects: Air conditioning system -- Cabin temperature -- Intelligent model predict control -- Thermal comfort of passenger -- Energy conservation -- Suppression of evaporator frosting; Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2020.116084
• Languages: English
• ISSNs: 1359-4311

Abstract

Highlights: A dynamic thermal model is for the AC system built and validated. A vehicle speed previewer is established for AC system in this paper. A PMV adaptive algorithm is designed to adapt the thermal habits of occupants. The IMPC including submodels of VSP and SAPTC is established. MPC is shown to satisfy the thermal habit, save energy and avoid frosting. Abstract: This paper establishes a dynamic thermal model for the Air Conditioning (AC)-cabin coupled system that includes the influences of vehicle speed and external environment on the heat exchange with the cabin. An Intelligent Model Predictive Control strategy (IMPC strategy) integrating the vehicle speed previewer and the self-adaptor of passenger's thermal comfort, is proposed and applied to the AC-cabin system. This strategy can predict both the car speed and the preferred predicted mean vote of passengers by learning the historical car speed and the passenger's comfort temperature. With their help, the IMPC has a more dynamic response of compressor speed to the car speed change and can automatically adjust cabin temperature, making it satisfy the thermal preference of the passenger with a little control error of PMV and cabin temperature. In aspect of energy conservation, the IMPC strategy saves more energy than the other control strategies researched in this paper. Its energy consumption is 4.32% less than the traditional MPC strategy, 40.4% less than the on-off controller, and 25.6% less than the PID controller. Moreover, the IMPC algorithm can keep the surface temperature of evaporator above 0 °C by setting the restricted condition in the MPC strategy, which can avoid the frosting on the evaporator wall and make the AC system work efficiently.