|
|
|
| Fuzzy energy management strategy for fuel cell buses based on improved particle swarm optimization |
| PENG Ziqi1, ZHU Zhiying1,2, XU Zheng1, YANG Pinhai1, YANG Hangdong1 |
1. School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167;
2. Jiangsu Collaborative Innovation Center for Smart Distribution Network, Nanjing 211167 |
|
|
|
|
Abstract Introducing a power battery as an auxiliary energy source in fuel cell systems can optimize power distribution and enhance overall vehicle economy. For hybrid electric vehicles equipped with both fuel cells and power batteries, conventional fuzzy energy management strategies exhibit drawbacks such as high subjectivity and poor adaptability. These limitations often lead to severe fuel cell power fluctuations and reduced lifespan. Therefore, this study proposes an improved particle swarm optimization based on fuzzy logic control to optimize the membership functions, with the objective of minimizing hydrogen consumption. A simulation model is established using the Matlab/Simulink and AVL Cruise M platforms, and simulation experiments are conducted under typical driving cycles, namely the urban bus driving cycle and the heavy-duty commercial vehicle driving cycle. The results demonstrate that the proposed strategy achieves smoother fuel cell output power, effectively reduces hydrogen consumption under both driving cycles, and improves overall vehicle economy.
|
|
Received: 16 June 2025
|
|
|
|
| Cite this article: |
|
PENG Ziqi,ZHU Zhiying,XU Zheng等. Fuzzy energy management strategy for fuel cell buses based on improved particle swarm optimization[J]. Electrical Engineering, 2026, 27(1): 57-63.
|
|
|
|
| URL: |
|
https://dqjs.cesmedia.cn/EN/Y2026/V27/I1/57
|
[1] 王晓姬, 王道涵, 王柄东, 等. 电动汽车驱动/充电一体化系统及其控制策略综述[J]. 电工技术学报, 2023, 38(22): 5940-5958.
[2] 刘偲艳, 葛庆. 氢燃料汽车混合动力系统能量管理优化策略[J]. 电气技术, 2024, 25(9): 22-26.
[3] 于仲安, 马静瑶. 含风电耦合制氢的主从博弈多区域综合能源系统协调调度策略[J]. 电气技术, 2023, 24(7): 1-10.
[4] 严陈希, 耿文冉, 黄明宇, 等. 基于工况识别的混合动力汽车能量管理策略[J]. 机械设计与制造, 2022(3): 24-29.
[5] SHEN Di, LIM C C, SHI Peng.Fuzzy model based control for energy management and optimization in fuel cell vehicles[J]. IEEE Transactions on Vehicular Technology, 2020, 69(12): 14674-14688.
[6] 周健豪, 顾诚, 刘军, 等. 基于IGWO的燃料电池汽车模糊控制能量管理策略[J]. 重庆理工大学学报(自然科学), 2021, 35(5): 33-41.
[7] HOU Shengyan, YIN Hai, PLA B, et al.Real-time energy management strategy of a fuel cell electric vehicle with global optimal learning[J]. IEEE Transa- ctions on Transportation Electrification, 2023, 9(4): 5085-5097.
[8] 王志福, 徐崧, 罗崴. 基于动态规划的燃料电池车能量管理策略研究[J]. 太阳能学报, 2023, 44(10): 550-556.
[9] 王金环, 宋占伟. 基于模型预测框架的燃料电池汽车混合动力系统能量管理[J]. 车用发动机, 2024(5): 61-70.
[10] 高锋阳, 张浩然. 氢燃料电池混合动力有轨电车的自适应瞬时等效能耗优化[J]. 机械工程学报, 2023, 59(6): 226-238.
[11] 何宋杰, 吕学勤. 基于混合深度神经网络的燃料电池混合动力汽车能量管理优化研究[J]. 可再生能源, 2024, 42(8): 1127-1136.
[12] 陈薇玉, 姜莉. 氢燃料电池混合动力汽车能量管理策略研究综述[J]. 黑龙江工程学院学报, 2024, 38(4): 1-8.
[13] 陈志跃, 陈家伟, 敖文杰, 等. 含脉冲负载燃料电池-锂电池-超级电容混合供电系统大信号稳定性分析及提升方法[J/OL]. 电工技术学报, 1-13(2025-06- 09)[2025-07-29]. https://doi.org/10.19595/j.cnki.1000-6753.tces.250546.
[14] LIN Xinyou, HUANG Hao, XU Xinhao, et al.Dynamic programming solutions extracted SOC- trajectory online learning generation algorithm based approximate global optimization control strategy for a fuel cell hybrid electric vehicle[J]. Energy, 2024, 295: 130728.
[15] 罗慧友, 刘胜永. 基于CSSA优化模糊控制能量管理策略研究[J]. 广西科技大学学报, 2024, 35(4): 67-75.
[16] 蒋毅, 廖看秋, 朱跃欧, 等. 基于粒子群优化算法的磁浮列车自抗扰悬浮控制器研究[J]. 电气技术, 2024, 25(7): 39-44, 49. |
|
|
|
2026, Vol. 27 
