Economic Model Predictive Control of HVAC System in Electric Vehicles⁎The authors from the University of Michigan-Dearborn would like to acknowledge the financial and technical support from Hyundai-Kia Motor Company. Issue 20 (2021)

• Record Type: Journal Article
• Authors: Kwak, Kyoung Hyun; Kim, Jaewoong; Chen, Youyi; Kim, Youngki; Jung, Dewey
• Is Part Of: IFAC-PapersOnLine. Volume 54:Issue 20(2021)
• Page Start: 852
• Page End: 857
• Publication Date: 2021
• Subjects: heating -- ventilation -- air conditioning (HVAC) control -- vehicle thermal management -- economic model predictive control -- electric vehicles; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2021.11.278
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: This paper presents an economic model predictive control (EMPC)-based strategy for heating, ventilation, and air conditioning (HVAC) system control to improve the driving range of a battery electric vehicle (BEV). The energy consumption of an HVAC system can be directly minimized through an EMPC framework; however, the complex nonlinear dynamics of the HVAC system (e.g., the vapor-compression cycle) hinders the application of EMPC. In addition, heater operation in a BEV relies on the battery power, and cooperation of the heater and the air conditioning increases the model complexity. To resolve such modeling issues, this paper proposes a control-oriented reduced-order HVAC system model based on the assumption of a quasi-steady ideal vapor-compression cycle. The developed model is validated against a high-fidelity physics-based model and is used to develop an EMPC-based strategy for HVAC control. The efficacy of the control strategy is demonstrated with various performance metrics. The simulation results show that, compared to the rule-based controller, the proposed EMPC strategy provides (i) a 4.4-7.5 % reduction in energy consumption and (ii) a 70-86 s faster cabin cool-down time without constraint violations.