An intelligent eco-heating control strategy for heat-pump air conditioning system of electric vehicles. (5th November 2022)

• Record Type: Journal Article
• Authors: Xie, Yi; Ou, Jingzhi; Li, Wei; Li, Kuining; Liu, Jiangyan; Liu, Zhaoming; Zhou, Degen; Li, Jingyuan
• Is Part Of: Applied thermal engineering. Volume 216(2022)
• Publication Date: 2022-11-05
• Subjects: Electric vehicles -- Secondary-loop heat pump system -- Model predictive control -- Temperature and air quality control -- Energy consumption; Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2022.119126
• Languages: English
• ISSNs: 1359-4311

Abstract

Highlights: A dynamic model is development for the SLHP-cabin system. A PID-based ventilation strategy is built to control the CO2 concentration. An intelligent algorithm is built to predict the thermal habit of passengers. A comprehensive control strategy based on MPC is proposed for the SLHP system. The MPC is proven to have better performance than the traditional controllers. Abstract: An intelligent eco-heating control strategy was proposed in this work for the secondary-loop heat pump (SLHP) systems of electric vehicles (EVs) for improving the thermal comfort of the passenger, saving energy and controlling the CO2 concentration inside the cabin. The developed control strategy was based on the model predictive control algorithm, while the dynamic model of the SLHP-cabin system, a predictor of the passenger thermal habit and the control strategy of CO2 concentration in the cabin are integrated. According to the extracted verification results, the maximum root mean square error of the cabin temperature that was predicted by the dynamic model was below 1.5 °C. Moreover, the prediction algorithm of the passenger's thermal preference can (model predictive control) precisely describe the thermal habit, whereas the prediction error of the average predicted mean vote (PMV) was smaller than 0.03. Unlike the traditional rule-based control strategies, the MPC can not only control the temperature and the CO2 concentration of the cabin well, but also minimize the energy cost of the SLHP system. According to the provided comparison results with the on-off and PID controllers, the standard deviation between the real-time cabin temperature and the target cabin temperature achieved by the MPC was 0.4 °C, which was 20 % lower than that of the PID and 50 % lower than that of the on-off controller. In addition, the energy cost of the SLHP system was 0.98 kW⋅h for the MPC, which was 5.8 % lower than that of the PID and 16.2 % lower than that of the on-off controller.