A two-layered eco-cooling control strategy for electric car air conditioning systems with integration of dynamic programming and fuzzy PID. (5th July 2022)

• Record Type: Journal Article
• Authors: Xie, Yi; Yang, Peng; Qian, Yuping; Zhang, Yangjun; Li, Kuining; Zhou, Yi
• Is Part Of: Applied thermal engineering. Volume 211(2022)
• Publication Date: 2022-07-05
• Subjects: Air conditioning system -- Cabin temperature -- Dynamic programming algorithm -- Velocity planning -- Passenger's thermal comfort -- Energy saving; Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2022.118488
• Languages: English
• ISSNs: 1359-4311

Abstract

Highlights: A two layered control strategy integrating DP and fuzzy PID is built. PPTC can accurately describe the thermal habit of passenger. DP integrates the PPTC, VVP and WIR and achieves a low energy cost. Fuzzy PID well responses the requirement of refrigeration capacity from DP. Two layered strategy raises comfort and control precision and saves energy. Abstract: A two-layered control strategy is proposed for the air conditioning (AC) systems of electric vehicles. Unlike traditional rule-based controllers such as the on–off controller and proportion-integral-derivative (PID) controller, this strategy includes a decision layer and a control strategy. The core algorithm in the decision layer is the dynamic programming (DP), which integrates information from the thermal habit predictor of the passenger, vehicle velocity planner, and weather information receiver. The DP optimises the development of the cabin temperature to minimise the energy consumption of the AC system and sends the planned temperature to the control layer. The control layer uses a fuzzy PID algorithm to adjust the compressor speed based on the planned temperature profile, such that the real-world cabin temperature approaches the planned temperature. This two-layered control strategy is applied to a car whose AC-cabin system was verified by test data, and the results are compared with those obtained by the on–off controller and PID. When the target cabin temperature is not manually adjusted, the energy cost of the proposed strategy is 28.2% and 5.4% lower than those of the on–off controller and PID, respectively, at the ambient temperature profile of Environment 1 (described herein), and its maximum fluctuation of the cabin temperature is 92.8% and 68.2% smaller than those of the on–off controller and PID, respectively. At the ambient temperature of Environment 2 (described herein, lower than that of Environment 1), the energy cost of the proposed strategy is 37.1% and 5.9% lower, and the maximum fluctuation of the cabin temperature is 96.8% and 86.4% smaller, compared to the on–off controller and PID, respectively. When the target temperature is repeatedly set for the on–off controller and PID (first to 20 °C, then to 24.3 °C), the AC system consumes extra energy, leading to poor thermal comfort. Because the proposed strategy automatically sets the cabin temperature to the temperature preferred by the passenger, there is no extra adjustment of the target and the thermal environment inside the cabin is optimal for the passenger. Under this condition, the developed strategy can produce energy savings of 30.2% and 12.4%, compared to the on–off strategy and PID, respectively. Thus, the two-layered strategy can control the cabin temperature precisely, provide the passenger with a good thermal environment, and produce energy savings for the AC system.