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Control trajectory optimisation and optimal control of an electric vehicle HVAC system for favourable efficiency and thermal comfort

机译:控制轨迹优化,电动汽车HVAC系统的优化控制,实现效率和热舒适性

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摘要

In order to increase the driving range of battery electric vehicles, while maintaining a high level of thermal comfort inside the passenger cabin, it is necessary to design an energy management system which optimally synthesizes multiple control actions of heating, ventilation and air-conditioning (HVAC) system. To gain an insight into optimal control actions and set a control benchmark, the paper first proposes an algorithm of dynamic programming (DP)-based optimisation of HVAC control variables, which minimises the conflicting criteria of passenger thermal comfort and HVAC efficiency. Next, a hierarchical structure of thermal comfort control system is proposed, which consists of optimised low-level feedback controllers, optimisation-based control allocation algorithm that sets references for the low-level controllers, and a superimposed cabin temperature controller that commands the cooling capacity to the allocation algorithm. Finally, the overall control system is verified by simulation for cool-down scenario, and the simulation results are compared with the DP benchmark. The results show that the control system behaviour can approach the DP benchmark if the superimposed controller bandwidth is tuned along with the allocation cost function weighting coefficients, where a fast controller tuning relates to better thermal comfort while a slow tuning results in improved efficiency.
机译:为了增加电池电动车的驾驶范围,同时保持客舱内部的高水平热舒适度,有必要设计一种能源管理系统,该能量管理系统最佳地合成加热,通风和空调的多种控制作用(HVAC ) 系统。为了深入了解最佳控制动作并设置控制基准,本文首先提出了一种动态编程(DP)算法 - 基于HVAC控制变量的优化,这最大限度地减少了乘客热舒适度和HVAC效率的矛盾标准。接下来,提出了一种热舒适控制系统的层次结构,由优化的低电平反馈控制器,基于优化的控制分配算法组成,该算法为低级控制器设置引用,以及命令冷却能力的叠加舱温度控制器到分配算法。最后,通过用于冷却场景的模拟来验证整体控制系统,并将模拟结果与DP基准进行比较。结果表明,如果叠加的控制器带宽与分配成本函数加权系数一起调谐,则控制系统行为可以接近DP基准,其中快速控制器调谐涉及更好的热舒适性,而慢速调整导致提高效率。

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