Research on real-time hierarchical energy management systems for hydrogen fuel cell drones
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摘要:
针对如何提升无人机续航的问题设计了基于燃料电池-锂电池-超级电容的氢燃料无人机混合动力系统拓扑结构，提出了一种多时间尺度分层能量管理框架，能够实现无人机混合动力系统的决策实时性与燃料最低消耗的要求。在短时间尺度层面，设计了一种适用于无人机混合动力系统的自适应下垂控制算法，能够解决传统下垂控制的精度不足和母线电压降的问题；在长时间尺度层面，设计了一种基于等效氢耗量最小的模型预测控制实时分层能量管理策略，使其能够根据系统的瞬时状态和需求进行动态调整，并在系统内部对不同类型的储能装置进行分级管理和优化协调，实现系统的最优能量分配。通过仿真试验计算分析来验证该系统的整体效能和可靠性，结果表明该策略与状态机控制策略相比可以减少5.2%的氢气消耗，并能使燃料电池的平均工作效率提高约10%，峰谷功率波动减少了92.8%。
Abstract:A hybrid power system topology based on fuel cells, lithium batteries, and supercapacitors for hydrogen-fueled unmanned aerial vehicles (UAVs) has been designed to address the challenge of enhancing UAV endurance. A multi-time-scale hierarchical energy management framework is proposed, which can achieve the requirements of decision-making real-time performance and minimal fuel consumption for UAV hybrid power systems. At the short-time scale, an adaptive droop control algorithm suitable for UAV hybrid power systems is designed, solving the issues of insufficient accuracy in traditional droop control and bus voltage drop. For the long-time scale, a real-time hierarchical energy management strategy based on the minimum equivalent hydrogen consumption using model predictive control is designed. This strategy dynamically adjusts according to the instantaneous state and demands of the system, and performs graded management and optimized coordination among different types of energy storage devices internally, achieving optimal energy distribution of the system. The efficacy and reliability of the system were verified through simulation experiments and computational analysis. The results indicate that the strategy can reduce hydrogen consumption by 5.2% and increase the average working efficiency of the fuel cell by approximately 10% compared to state machine control strategies, while also reducing peak-to-valley power fluctuations by 92.8%.
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图 4 基于自适应卡尔曼滤波的SOC估计系统框图
Figure 4. System block of SOC estimation based on adaptive kalman filtering
图 10 基于下垂控制的DC/DC变换器并联系统等效电路
Figure 10. Equivalent circuit of DC/DC converter parallel system based on droop control
图 12 基于等效氢耗量最小的模型预测控制框图
Figure 12. Block diagram of model predictive control based on minimizing equivalent hydrogen consumption
图 13 传统下垂控制策略与基于输出电压补偿的下垂控制策略母线电压仿真波形
Figure 13. Simulated waveforms of bus voltage based on droop control strategy and conventional droop control strategy with output voltage compensation
图 14 基于输出电压补偿的下垂控制策略的输出电流仿真波形
Figure 14. Simulated waveforms of output current for droop control strategy based on output voltage compensation
图 15 自适应下垂控制策略的输出电流仿真波形
Figure 15. Simulated waveforms of output current for adaptive droop control strategy
图 16 自适应下垂控制策略的输出电流和母线电压仿真波形
Figure 16. Simulated waveforms of output current and bus voltage for adaptive droop control strategy
图 17 基于规则状态机的能量管理策略仿真结果
Figure 17. Results of energy management strategy based on rule-based state machine
图 18 基于MPC+Droop能量管理策略仿真结果
Figure 18. Simulation results of the energy management strategy based on MPC+Droop
图 19 不同策略下的燃料电池输出功率对比图
Figure 19. Comparison of fuel cell output power under different strategies
图 20 不同策略下的系统等效氢耗量
Figure 20. System of equivalent hydrogen consumption under different strategies
图 21 不同策略下的实时等效氢消耗
Figure 21. Real-time equivalent hydrogen consumption under different strategies
表 1 固定等效因子与经惩罚函数修正的等效因子氢耗量仿真结果
Table 1. Simulation results of hydrogen consumption under fixed equivalent factors and penalty-function-corrected equivalent factors
锂电池SOC初始值 超级电容SOC初始值 固定等效因子氢耗量/kg 经惩罚函数修正的等效因子氢耗量/kg 0.4 0.5 0.3337 0.3306 0.5 0.7 0.3326 0.3281 0.65 0.8 0.3316 0.3243 
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表 2 不同策略下的仿真对比结果
Table 2. Comparison results of simulation with different strategies
能量管理策略 等效氢消耗量/kg 最大实时氢耗量/（g/s） 燃料电池峰谷功率差$ \Delta {p} $/kW 燃料电池平均效率/% 基于规则状态机 0.346 2.95 86.57 53.99 基于MPC+Droop 0.328 2.59 6.22 64.4
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