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.

为了增加电池电动汽车的行驶里程，同时保持客舱内部的高水平热舒适性，有必要设计一种能量管理系统，以最佳方式综合加热，通风和空调（HVAC）的多个控制动作）系统。为了深入了解最佳控制措施并设定控制基准，本文首先提出了一种基于动态规划（DP）的HVAC控制变量优化算法，该算法最大程度地减少了乘客热舒适度和HVAC效率的冲突标准。接下来，提出了一种热舒适控制系统的分层结构，该结构由优化的低级反馈控制器，基于优化的控制分配算法（为低级控制器设置参考）组成，以及一个叠加的机舱温度控制器，该控制器将冷却能力控制在分配算法中。最后，通过仿真验证了整个控制系统的冷却情况，并将仿真结果与DP基准进行了比较。结果表明，如果对叠加的控制器带宽和分配成本函数加权系数进行调整，则控制系统的行为可以达到DP基准，其中快速控制器调整与更好的热舒适性相关，而慢速调整与提高效率有关。