汽车工程
空调
能源消耗
行驶循环
电动汽车
高效能源利用
能量(信号处理)
工程类
模拟
汽车工业
控制系统
冷负荷
燃料效率
计算机科学
功率(物理)
航空航天工程
机械工程
统计
物理
电气工程
数学
量子力学
作者
Hao Wang,Mohammad Reza Amini,Qiuhao Hu,Ilya Kolmanovsky,Jing Sun
标识
DOI:10.1109/tcst.2020.3038746
摘要
The operation of the air-conditioning (A/C) system can significantly increase the energy consumption of passenger vehicles. In this article, aiming at reducing the vehicle-level energy consumption in hot weather, an optimization-based energy-efficient control strategy for the A/C system, which is referred to as the eco-cooling, is developed and experimentally validated. The proposed eco-cooling strategy leverages the A/C system efficiency sensitivity to the vehicle speed and the thermal storage of the passenger cabin to coordinate the A/C operation with vehicle speed profile by actively shifting the A/C thermal load toward the more efficient region at higher vehicle speeds. The proposed strategy exploits model predictive control and incorporates speed preview information while enforcing constraints. The effectiveness of the control strategy is first demonstrated on a high-fidelity simulation model and then implemented experimentally on a hybrid electric vehicle. Repeatable vehicle tests show that, over a real-world city driving cycle, an average energy saving of 5.7% can be achieved at the vehicle level using the proposed eco-cooling strategy compared with a baseline A/C control strategy that runs A/C with a constant setting. This energy-saving is achieved, while the proposed eco-cooling strategy delivers a similar amount of cooling energy to the cabin compared with that of the baseline strategy with a 2.7% difference on average.
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