Research on Energy Management and Hierarchical Optimization Control Strategy for Hybrid Electric Propulsion System
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摘要: 为了提高混合动力飞行器经济性并改善动力系统的动态性能, 提出一种混合动力分层控制的能量管理策略. 首先, 在顶层提出基于改进等效燃油消耗最小化的能量管理策略, 根据发电机组的燃油消耗特性、储能电池组的荷电状态以及等效惩罚因子动态调整发电机组的最优工作曲线, 从而获得最佳的燃油经济性. 在底层提出一种基于电流反馈的改进下垂控制策略, 负责管理电池组的充放电状态和维持直流母线电压的动态平衡, 同时实现飞行器的经济性与动态响应的协同控制, 达到对混合电推进飞行器能量的动态优化管理的目的. 最后, 通过基于RT-LAB的混合动力系统硬件在环实验平台验证该能量管理策略的有效性.Abstract: To enhance the economic efficiency and dynamic performance of hybrid aircraft, an energy management strategy based on hybrid-powered hierarchical control is proposed. At the upper layer, an advanced Energy Consumption Minimization Strategy is introduced, which dynamically adjusts the optimal operating curve of the generator set based on its fuel consumption characteristics, the state of charge (SOC) of the energy storage battery pack, and the equivalent penalty factor to achieve optimal fuel economy. At the lower layer, an enhanced droog control strategy incorporating current feedback is proposed. This strategy manages the charging and discharging states of the battery pack and maintains the dynamic balance of the DC bus voltage, thereby achieving cooperative control of economic efficiency and dynamic response. Consequently, it realizes dynamic optimization management of the energy in hybrid electric propulsion vehicles. Finally, the effectiveness of the proposed energy management strategy is validated through simulations and hardware-in-the-loop testing using an RT-LAB-based platform.
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图 9 混合动力系统硬件在环实验平台
Fig. 9 Hybrid power system hardware in the loop experiment platform
表 1 实验参数
Table 1 1 Experimental parameter
子系统 描述 值 储能电池组 储能电池额定电压 100 V 储能电池额定容量 36 Ah 储能电池组初始SOC 0.35、0.45;
0.5、0.6;
0.8、0.9发电机组 发电机组功率上界 15 kW 发电机组功率下界 5 kW 发电机组最优功率输出 10 kW 负载系统 母线电压 270 V 负载 9-15 kW 
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表 2 基于状态机的能量管理方法
Table 2 2 Energy management method based on state machine
状态 SOC 水平 特征 运行模式 1 Low $ {P_{load}} \le {{{P}}_{{{opt - effi}}}} $ $ {P_{{{en}}}}{{ = }}{{{P}}_{{{opt - effi}}}} $$ {P_{{{bat}}}}{{ = }}{{{P}}_{{{load}}}}{{ - }}{{{P}}_{{{opt - effi}}}} $ 2 Low $ {P_{load}} > {{{P}}_{{{opt - effi}}}} $ $ {P_{{{en}}}}{{ = }}{P_{load}} $
$ {P_{{{bat}}}}{{ = }}0 $3 Middle $ {P_{load}} \le {{{P}}_{{{opt - effi}}}} $ $ {P_{{{en}}}}{{ = }}{{{P}}_{{{opt - effi}}}} $
$ {P_{{{bat}}}}{{ = }}{{{P}}_{{{load}}}}{{ - }}{{{P}}_{{{opt - effi}}}} $4 Middle $ {P_{load}} > {{{P}}_{{{opt - effi}}}} $ $ {P_{{{en}}}}{{ = }}{{{P}}_{{{opt - effi}}}} $$ {P_{{{bat}}}}{{ = }}{{{P}}_{{{load}}}}{{ - }}{{{P}}_{{{opt - effi}}}} $ 5 High $ {P_{load}} \le {{{P}}_{{{opt - effi}}}} $ $ {P_{{{en}}}}{{ = }}{P_{load}} $
$ {P_{{{bat}}}}{{ = }}0 $6 High $ {P_{load}} > {{{P}}_{{{opt - effi}}}} $ $ {P_{{{en}}}}{{ = }}{{{P}}_{{{opt - effi}}}} $
$ {P_{{{bat}}}}{{ = }}{{{P}}_{{{load}}}}{{ - }}{{{P}}_{{{opt - effi}}}} $
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表 3 不同方法性能比较
Table 3 Performance comparison of different methods
方法 场景 燃油消耗/kg SOC1 SOC2 本文
所提1 4.78 0.31 0.335 2 4.35 0.33 0.365 3 4.18 0.38 0.41 ECMS 1 4.9 0.282 0.325 2 4.55 0.283 0.326 3 4.41 0.288 0.325 状态机 1 5.01 0.399 0.399 2 4.69 0.399 0.398 3 4.42 0.399 0.399
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