Cabin heating requirement and engine efficiency degradation in cold weather lead to considerable increase in fuel consumption of hybrid electric vehicles (HEVs). This article focuses on the development of a real-time control framework for integrated power and thermal management (i-PTM) of a connected HEV through incorporating vehicle speed preview informed by traffic connectivity. To evaluate the ceiling of the fuel economy improvement via i-PTM, forward dynamic programming (FDP) is adopted as a benchmark study to find the global optimization solution and then implemented into a centralized model predictive controller (CMPC). Regarding the slow response associated with the coupled electric-thermal dynamics in the HEV, a two-layer hierarchical MPC (HMPC) framework is developed, which exploits vehicle speed preview predictions over short and long prediction horizons to optimize fuel consumption while satisfying power and cabin heating demand. The simulation results show that the proposed HMPC provides near-optimal solutions in reference to the benchmark while reduces up to 79% computation burden compared with CMPC. Additionally, compared to a baseline power management (PM) controller, up to 5.46% improvement on fuel economy can be achieved by HMPC for congested driving scenarios. Finally, the real-time feasibility of the HMPC is assessed in a dSPACE rapid prototyping system.

中文翻译：

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基于分层模型预测控制的互联混合动力汽车的实时集成功率和热管理


寒冷天气下的驾驶室加热需求和发动机效率下降导致混合动力电动汽车 (HEV) 的燃油消耗大幅增加。本文重点介绍通过结合交通连接通知的车速预览，开发用于互联混合动力汽车的集成电源和热管理 (i-PTM) 的实时控制框架。为了评估通过 i-PTM 提高燃油经济性的上限，采用正向动态规划（FDP）作为基准研究来寻找全局优化解决方案，然后将其实现到集中式模型预测控制器（CMPC）中。针对混合动力汽车中与电热耦合动力学相关的缓慢响应，开发了一个两层分层 MPC（HMPC）框架，该框架利用短期和长期预测范围内的车辆速度预览预测来优化燃油消耗，同时满足动力和驾驶室的要求供暖需求。仿真结果表明，所提出的 HMPC 参考基准提供了接近最优的解决方案，同时与 CMPC 相比减少了高达 79% 的计算负担。此外，与基准电源管理 (PM) 控制器相比，HMPC 在拥堵驾驶场景下可将燃油经济性提高高达 5.46%。最后，在 dSPACE 快速原型系统中评估 HMPC 的实时可行性。