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| Energy Management Strategy for Fuel Cell Unmanned Aerial Vehicles Integrating Dynamic Programming with Model Predictive Control |
| Quan Rui1, Liu Dazhi1, Guan Xin1, Zhang Guoguang1, Quan Jin2 |
1. Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Hubei University of Technology Wuhan 430068 China;
2. Wuhan Hyvitech Co. Ltd Wuhan 430000 China |
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Abstract With the low altitude economy being incorporated into the national strategic emerging industry system, fuel cell unmanned aerial vehicles (UAVs) have become the core technology direction to break through the bottleneck of lithium battery range due to their high energy density, zero carbon emission, and long-rangecharacteristics. However, the existing energy management strategies (EMSs) have contradictions between real-time and global optimization performance: rule-based methods (e.g., PID) are difficult to adapt to dynamic working conditions, the UAV′s arithmetic resources limit deep reinforcement learning strategies, and optimization-based methods (e.g., dynamic programming) are globally optimal, but with high computational complexity, and cannot be applied in real-time. There is an urgent need for an EMSs that strike a balance between real-time performance and economic efficiency. Aiming at the challenge of fuel cell UAVs, such as high running cost and short fuel cell life, a real-time EMS integrating dynamic programming (DP) and model predictive control (MPC) is proposed, which aims to reduce the equivalent hydrogen consumption, extend the life of the powertrain system, and improve the operation time through multi-objective optimization.
Firstly, a Markov predictor that can predict the future demand power is formed by cyclic indexing and updating the constructed state transfer matrix (STM) row by row, using data from typical flight conditions of the UAV. Then, it predicts the UAV demand power in real time according to the constructed Markov predictor and rationally allocates the energy of the lithium battery and fuel cell in combination with the powertrain configuration of the hydrogen-energy UAV. During system operation, the proposedcost function is used to quantify the running cost of each time step within the prediction step. The DP algorithm is used to obtain the short-term optimal power allocation sequence, and then the first element is applied to the fuel cell UAV powertrain. Rolling optimization is carried out according to the same method within the next prediction step, aimingto achieve the lowest running cost in the entire operation process.The following conclusions can be drawn from the results:
(1) After comparing the state transfer probability distribution characteristics of different step lengths, it is found that with the increase of the prediction step length, when the prediction step length is less than 3, the state probability distribution shows more obvious diagonal distribution characteristics, when the prediction step length is more than 5, the probability distribution under the state transfer matrix no longer shows obvious diagonal aggregation characteristics, and finally Np = 5 is selected as the prediction step length.
(2) By analyzing the probability distribution of fuel cell output power, the DP-MPC strategy has the highest percentage of operating points in the efficient interval of the fuel cell of 85.43%, which is 11.2%, 61.5% and 49.9% higher than MPC, ECMS, and SMC. Meanwhile, the DP-MPC strategy has the highest entire efficiency of 83.46%, which is improved by 13.41%, 16.74% and 12.23% compared to MPC, ECMS, and SMC, respectively.
(3) Based on the cost analysis, it is found that the DP-MPC-based EMS proposed in this paper can effectively reduce the total running cost of the UAV by 0.71%, 6.12% and 1.01% compared to MPC, ECMS, and SMC, respectively, and the operation time is improved by 5.39%, 13.62% and 3.05%.
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Received: 30 June 2025
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[1] 高锋阳, 苏红宇, 查鹏堂, 等. 基于工况预测和动力源寿命衰减协同的燃料电池有轨电车能量管理策略[J]. 电工技术学报, 2025, 40(13): 4316-4329.
Gao Fengyang, Su Hongyu, Zha pengtang, et al. Energy management strategy for fuel cell hybrid tram system based on driving cycle prediction and power source lifespan decay synergy[J]. Transactions of China Electrotechnical Society, 2025, 40(13): 4316-4329.
[2] 陈冲, 贾利民, 赵天宇, 等. 光伏和储能植入铁路牵引供电系统的拓扑架构与控制策略研究综述[J]. 电工技术学报, 2024, 39(24): 7874-7901.
Chen Chong, Jia Limin, Zhao Tianyu, et al.Research review on topology and control strategy of PV and energy storage connected to railway traction power supply systems[J]. Transactions of China Electro-technical Society, 2024, 39(24): 7874-7901.
[3] 高锋阳, 宋志翔, 高建宁, 等. 计及光伏和储能接入的牵引供电系统能量管理策略[J]. 电工技术学报, 2024, 39(3): 745-757.
Gao Fengyang, Song Zhixiang, Gao Jianning, et al.Energy management strategies for traction power systems with PV and energy storage access[J]. Transactions of China Electrotechnical Society, 2024, 39(3): 745-757.
[4] Lei Tao, Yang Zhou, Lin Zicun, et al.State of art on energy management strategy for hybrid-powered unmanned aerial vehicle[J]. Chinese Journal of Aeronautics, 2019, 32(6): 1488-1503.
[5] 胡春明, 闫丁洋, 刘娜, 等. 油电混合动力无人机能量管理策略的对比仿真研究[J]. 内燃机工程, 2022, 43(4): 74-83.
Hu Chunming, Yan Dingyang, Liu Na, et al.Comparison and simulation research on energy management strategies of oil-electric hybrid unmanned aerial vehicle[J]. Chinese Internal Combustion Engine Engineering, 2022, 43(4): 74-83.
[6] 陈伟, 刘建宏, 任毅, 等. 基于模糊逻辑的增程式无人机能量管理策略研究[J]. 重庆交通大学学报(自然科学版), 2024, 43(1): 99-106, 124.
Chen Wei, Liu Jianhong, Ren Yi, et al.Energy management strategy of extended range UAV based on fuzzy logic[J]. Journal of Chongqing Jiaotong University (Natural Science), 2024, 43(1): 99-106, 124.
[7] Zhou Yujie, Huang Yin, Mao Xuping, et al.Research on energy management strategy of fuel cell hybrid power via an improved TD3 deep reinforcement learning[J]. Energy, 2024, 293: 130564.
[8] Song Ke, Chen Huan, Wen Peimin, et al.A comprehensive evaluation framework to evaluate energy management strategies of fuel cell electric vehicles[J]. Electrochimica Acta, 2018, 292: 960-973.
[9] Liu Chang, Wang Yujie, Wang Li, et al.Load-adaptive real-time energy management strategy for battery/ ultracapacitor hybrid energy storage system using dynamic programming optimization[J]. Journal of Power Sources, 2019, 438: 227024.
[10] Zhou Shiyao, Chen Ziqiang, Huang Deyang, et al.Model prediction and rule based energy management strategy for a plug-in hybrid electric vehicle with hybrid energy storage system[J]. IEEE Transactions on Power Electronics, 2021, 36(5): 5926-5940.
[11] He Hongwen, Quan Shengwei, Sun Fengchun, et al.Model predictive control with lifetime constraints based energy management strategy for proton exchange membrane fuel cell hybrid power systems[J]. IEEE Transactions on Industrial Electronics, 2020, 67(10): 9012-9023.
[12] Zhou Lanqi, Yang Dongpo, Zeng Xiaohua, et al.Multi-objective real-time energy management for series-parallel hybrid electric vehicles considering battery life[J]. Energy Conversion and Management, 2023, 290: 117234.
[13] Meiler M, Schmid O, Schudy M, et al.Dynamic fuel cell stack model for real-time simulation based on system identification[J]. Journal of Power Sources, 2008, 176(2): 523-528.
[14] Quan Rui, Wu Fan, Wang Chengji, et al.Fault diagnosis in a current sensor and its application to fault-tolerant control for an air supply subsystem of a 50 kW-Grade fuel cell engine[J]. RSC Advances, 2020, 10(9): 5163-5172.
[15] Gong Chengyuan, Xing Lu, Liang Cong, et al.Modeling and dynamic characteristic simulation of air-cooled proton exchange membrane fuel cell stack for unmanned aerial vehicle[J]. Renewable Energy, 2022, 188: 1094-1104.
[16] 李建林, 彭禹宸, 王茜, 等. 锂离子电池建模研究现状与展望[J]. 发电技术, 2025, 46(5): 857-871.
Li Jianlin, Peng Yuchen, Wang Qian, et al.Research status and prospect of lithium-ion battery modelling[J]. Power Generation Technology, 2025, 46(5): 857-871.
[17] 赵珈卉, 田立亭, 程林. 锂离子电池状态估计与剩余寿命预测方法综述[J]. 发电技术, 2023, 44(1): 1-17.
Zhao Jiahui, Tian Liting, Cheng Lin, et al.Review on state estimation and remaining useful life prediction methods for lithium-ion battery[J]. Power Generation Technology, 2023, 44(1): 1-17.
[18] Sellali M, Betka A, Djerdir A, et al.A novel energy management strategy in electric vehicle based on H∞ self-gain scheduled for linear parameter varying systems[J]. IEEE Transactions on Energy Conversion, 2021, 36(2): 767-778.
[19] Mariano-Hernández D, Hernández-Callejo L, Zorita-Lamadrid A, et al.A review of strategies for building energy management system: model predictive control, demand side management, optimization, and fault detect & diagnosis[J]. Journal of Building Engineering, 2021, 33: 101692.
[20] Quan Rui, Li Zhongxin, Liu Pin, et al.Minimum hydrogen consumption‐based energy management strategy for hybrid fuel cell unmanned aerial vehicles using direction prediction optimal foraging algorithm[J]. Fuel Cells, 2023, 23(2): 221-236.
[21] 王辉, 严欢, 张晓滨, 等. 基于分数阶的锂电池SOC和SOH联合在线估计[J]. 电源学报, 2025, 23(2): 256-265.
Wang Hui, Yan Huan, Zhang Xiaobin, et al.Joint online estimation of soc and soh for lithium batteries based on fractional-order models[J]. Journal of Power Supply, 2025, 23(2): 256-265.
[22] Lin Xinyou, Zhou Qiang, Tu Jiayi, et al.Self-learning Markov prediction algorithm based aging-oriented gradient drop power control strategy for the transient modes of fuel cell hybrid electric vehicles[J]. Applied Energy, 2024, 376: 124198.
[23] 吴字强, 王伟, 黄亮, 等. 燃料电池稳态输出功率与效率的双目标优化[J]. 电源学报, 2019, 17(2): 32-39.
Wu Ziqiang, Wang Wei, Huang Liang, et al.Bi-objective optimization for steady-state output power and efficiency of fuel cell[J]. Journal of Power Supply, 2019, 17(2): 32-39.
[24] Chen Huicui, Pei Pucheng, Song Mancun.Lifetime prediction and the economic lifetime of proton exchange membrane fuel cells[J]. Applied Energy, 2015, 142: 154-163. |
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2026, Vol. 41 
