基于MPC的精密冷却策略（PCS）用于汽车空调系统的高效热管理
MPC-Based Precision Cooling Strategy (PCS) for Efficient Thermal Management of Automotive Air Conditioning System
AI总结：
本文提出一种基于MPC的精密冷却策略（PCS），利用车速预览精确跟踪冷却功率轨迹，在SC03循环下相比福特基准节能4.9%，且仅使车厢温度略升，并能进一步降低能耗。
摘要：
本文提出了一种基于模型预测控制（MPC）的精密冷却策略（PCS），用于汽车空调（A/C）系统的节能热管理。所提出的PCS能够精确跟踪随时间变化的冷却功率轨迹，该轨迹假定与乘客舒适性需求相匹配。此外，通过利用新兴的网联自动驾驶车辆（CAVs）技术，车辆速度预览可被纳入我们的空调热管理策略中，以进一步提高能源效率。该空调热管理策略是基于福特汽车公司为电动化动力总成车辆提供的基于物理的空调系统模型（ACSim）开发和评估的。在与福特基准案例在SC03循环上的比较中，在跟踪相同冷却功率轨迹的情况下，所提出的PCS实现了4.9%的节能，代价是车厢温度略有升高（小于1摄氏度）。研究还表明，通过与未来车速协调并转移空调功率负载，空调能耗可进一步降低。
英文摘要：
In this paper, we propose an MPC-based precision cooling strategy (PCS) for energy efficient thermal management of automotive air conditioning (A/C) system. The proposed PCS is able to provide precise tracking of the time-varying cooling power trajectory, which is assumed to match the passenger comfort requirements. In addition, by leveraging the emerging connected and automated vehicles (CAVs) technology, vehicle speed preview can be incorporated in our A/C thermal management strategy for further energy efficiency improvement. This proposed A/C thermal management strategy is developed and evaluated based on a physics-based A/C system model (ACSim) from Ford Motor Company for the vehicles with electrified powertrains. In a comparison with Ford benchmark case over SC03 cycle, for tracking the same cooling power trajectory, the proposed PCS provides 4.9% energy saving at the cost of a slight increase in the cabin temperature (less than 1$^oC$). It is also demonstrated that by coordinating with future vehicle speed and shifting the A/C power load, the A/C energy consumption can be further reduced.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
