基于蛇优化算法的PEMFC空气供给系统模糊自抗扰控制

doi:10.3969/j.issn.2097-0706.2025.06.007

综合智慧能源 ›› 2025, Vol. 47 ›› Issue (6): 57-73.doi: 10.3969/j.issn.2097-0706.2025.06.007

基于蛇优化算法的PEMFC空气供给系统模糊自抗扰控制

李晓宁^a(), 孙娜^a(), 黄阿敏^b(), 董海鹰^a^,^*()

a.兰州交通大学新能源与动力工程学院，兰州 730070
b.自动化与电气工程学院，兰州 730070

收稿日期:2025-02-05 修回日期:2025-03-20 出版日期:2025-06-25
通讯作者: 董海鹰^*（1966），男，二级教授，博士生导师，博士，从事复杂系统建模与智能控制理论、电力系统运行与优化控制、新能源发电控制与优化等方面的研究，hydong@mail.lzjtu.cn。
作者简介:李晓宁（1998），男，硕士生，从事氢燃料电池控制策略方面的研究，lxn210838@163.com；
孙娜（1977），女，副教授，硕士，从事清洁能源技术方面的工作，sunna@mail.lzjtu.cn；
黄阿敏（1991），女，助教，博士生，从事电气系统稳定性及控制、燃料电池故障诊断及寿命预测等方面的研究，ham_lzjtu@163.com。
基金资助:
甘肃省自然科学基金项目(23JRRA1692)

Fuzzy active disturbance rejection control of PEMFC air intake unit based on snake optimization algorithm

LI Xiaoning^a(), SUN Na^a(), HUANG Amin^b(), DONG Haiying^a^,^*()

a. School of New Energy and Power Engineering，Lanzhou Jiaotong University，Lanzhou 730070，China
b. School of Automation and Electrical Engineering，Lanzhou Jiaotong University，Lanzhou 730070，China

Received:2025-02-05 Revised:2025-03-20 Published:2025-06-25
Supported by:
Natural Science Foundation of Gansu Province(23JRRA1692)

摘要/Abstract

摘要：

质子交换膜燃料电池（PEMFC）的空气流量和压力之间耦合性强，导致电池系统输出稳定性差、响应速度慢等，针对此问题，基于空气供给系统前馈补偿解耦结构，提出采用蛇优化（SO）算法和模糊控制改进的自抗扰控制策略。基于前馈补偿解耦结构建立空气供给系统状态空间方程，推导流量、压力耦合矩阵，设计解耦矩阵消除二者的耦合性。设计改进自抗扰控制，将自抗扰控制中反馈控制律增益分为2部分，采用模糊控制对第1部分进行粗调；采用改进SO算法对自抗扰控制中状态观测器增益与反馈控制律增益第2部分参数精确整定。其中，改进SO算法融合了基于序列的正弦分段混沌映射（SPM）策略初始化种群，增强种群多样性；动态惯性权重和三角形游走策略解决了勘探阶段前期寻优速度慢等缺陷；透镜成像和贪婪组合策略扩大搜索范围，防止陷入局部最优解。搭建3 kW燃料电池系统，验证所提出控制策略的正确性和有效性。试验验证了该策略在解耦方面和目标值跟踪方面具有良好的控制效果，系统超调量、调节时间以及系统振荡显著减小，该策略提高了燃料电池系统的响应速度和动态性能。

关键词: 氢能, 质子交换膜燃料电池, 空气供给系统, 模糊前馈解耦, 自抗扰控制策略, 蛇优化算法, 可再生能源, 过氧比

Abstract:

The strong coupling between air flow and pressure in proton exchange membrane fuel cells （PEMFC） results in poor output stability and slow response speed of cell systems. To address these issues，an improved active disturbance rejection control （ADRC） strategy incorporating snake optimization algorithm and fuzzy control was proposed based on the feedforward compensation decoupling structure of the air supply system. The state-space equations of the air supply system were established based on the feedforward compensation decoupling structure. A coupling matrix between flow and pressure was derived which was designed to eliminate the coupling between air flow and pressure. For the improved ADRC design，the feedback control law gain was divided into two parts： the first part was coarsely adjusted using fuzzy control，and the parameters for the second part of state observer gain and feedback control law gain were precisely tuned using the improved snake optimization algorithm. The improved snake optimization algorithm incorporated the chaotic mapping strategy using sequence-based particle swarm optimization method （SPM） for population initialization，enhancing population diversity. Dynamic inertia weights and triangular walk strategy addressed limitations such as slow optimization speed during the early exploration phase. Lens imaging and greedy combination strategies expanded the search range and prevented the algorithm from falling into local optima. A 3 kW fuel cell system was built to verify the correctness and effectiveness of the proposed control strategy. The experimental results showed that the proposed strategy achieved excellent control performance in terms of parameter decoupling and setpoint tracking. The strategy significantly reduced the overshoot，settling time，and oscillations，improving the response speed and dynamic performance of fuel cell system.

Key words: hydrogen energy, proton exchange membrane fuel cell, air supply system, fuzzy feedforward decoupling, active disturbance rejection control, snake optimization algorithm, renewable energy, control strategy, oxygen excess ratio

中图分类号:

TM912

李晓宁, 孙娜, 黄阿敏, 董海鹰. 基于蛇优化算法的PEMFC空气供给系统模糊自抗扰控制[J]. 综合智慧能源, 2025, 47(6): 57-73.

LI Xiaoning, SUN Na, HUANG Amin, DONG Haiying. Fuzzy active disturbance rejection control of PEMFC air intake unit based on snake optimization algorithm[J]. Integrated Intelligent Energy, 2025, 47(6): 57-73.

图/表 33

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表2

图15

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表3

表4

基准测试函数

基准函数	维度（D）	仿真结果	曲线类型
$F 1 x = ∑ i = 1 D x i + 0.5 2$	30	[-100，100]	单峰
$F 2 x = i x i 4 + r a n d$	30	[-1.28，1.28]	单峰
$F 3 x = - 20 e x p - 0.2 1 D ∑ i = 1 D x i 2 - e x p - 0.2 1 D ∑ i = 1 D x i 2$	30	[-32，32]	多峰
$F 4 x = π D 10 s i n 2 π y 1 + ∑ i = 1 D y i - 1 2 ⋅ 1 + 10 s i n 2 π y i + 1 + y D - 1 2 + ∑ i = 1 D u x i, 10,100,4$	30	[-50，50]	多峰

表4

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图17

表6

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表7

图25

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参考文献 30

[1]	郝俊红, 冯晓龙, 杨云溪, 等. 分布式换能系统的概念、架构与规划分析[J]. 中国电机工程学报, 2024, 44(10): 3885-3897.
	HAO Junhong, FENG Xiaolong, YANG Yunxi, et al. Concept, architecture and planning analysis of distributed energy exchange system[J]. Chinese Journal of Electrical Engineering, 2024, 44(10): 3885-3897.
[2]	郑玉荣, 靳军宝, 白光祖, 等. 氢燃料电池关键技术发展态势研究[J]. 现代化工, 2024, 44(11): 12-17.
	ZHENG Yurong, JIN Junbao, BAI Guangzu, et al. Research on development trend of key technologies for hydrogen fuel cells[J]. Modern Chemical Industry, 2024, 44(11): 12-17.
[3]	刘涛, 郭家新, 韩莹, 等. 氢燃料电池公交研究文献综述及展望[J]. 交通运输系统工程与信息, 2024, 24(5):1-13,23.
	LIU Tao, GUO Jiaxin, HAN Ying, et al. Hydrogen fuel cell bus: A literature review and prospects[J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(5): 1-13, 23.
[4]	王丽, 李蓓, 张帆, 等. 基于增益调度模型预测控制的SOFC-GT混合动力系统温度控制[J]. 综合智慧能源, 2022, 44(10): 42-49.
	WANG Li, LI Bei, ZHANG Fan, et al. Temperature control for a SOFC-GT hybrid power system based on gain scheduling model predictive control[J]. Integrated Intelligent Energy, 2022, 44(10): 42-49.
[5]	胡翀, 赵袁, RAZA Ali. 基于单层电堆形式的质子交换膜燃料电池仿真模拟研究及优化[J]. 综合智慧能源, 2022, 44(8): 91-96.
	HU Chong, ZHAO Yuan, RAZA Ali, et al. Simulation and optimization for the PEMFC based on single-cell stack structure[J]. Integrated Intelligent Energy, 2022, 44(8): 91-96.
[6]	焦志筱, 姜琦, 熊树生, 等. 基于自抗扰的PEMFC空气供给系统流量和压力控制[J]. 现代机械, 2024(6): 5-11.
	JIAO Zhixiao, JIANG Qi, XIONG Shusheng, et al. Air flow and pressure control of PEMFC air supply system based on ADRC[J]. Modern Machinery, 2024(6): 5-11.
[7]	薛国庆, 袁裕鹏, 李娜, 等. PEMFC系统空气流量与压力前馈PID协同控制[J]. 武汉理工大学学报, 2024, 46(9): 127-135.
	XUE Guoqing, YUAN Yupeng, LI Na, et al. Co-control of air flow and pressure feed-forward PID for PEMFC system[J]. Journal of Wuhan University of Technology, 2024, 46(9): 127-135.
[8]	陈家城. 燃料电池空气供给系统建模与控制[J]. 机电技术, 2023, 46(3): 70-73, 91.
	CHEN Jiacheng. Modeling and control of fuel cell air supply system[J]. Mechanical & Electrical Technology, 2023, 46(3): 70-73, 91.
[9]	WANG Y L, WANG Y F. Pressure and oxygen excess ratio control of PEMFC air management system based on neural network and prescribed performance[J]. Engineering Applications of Artificial Intelligence, 2023, 121: 105850.
[10]	ZHAO D D, XIA L, DANG H B, et al. Design and control of air supply system for PEMFC UAV based on dynamic decoupling strategy[J]. Energy Conversion and Management, 2022, 253: 115159.
[11]	ZENG T, XIAO L, CHEN J R, et al. Feedforward-based decoupling control of air supply for vehicular fuel cell system: Methodology and experimental validation[J]. Applied Energy, 2023, 335: 120756.
[12]	孙田. 重卡用大功率燃料电池发动机空气供给控制策略研究[D]. 北京: 北京交通大学, 2020.
	SUN Tian. Research on air supply control strategy of high-power fuel cell engine for heavy trucks[D]. Beijing: Beijing Jiaotong University, 2020.
[13]	XU J H, ZHANG B X, YAN H Z, et al. A decoupling control of air supply for the PEM fuel cell with slide mode-active disturbance rejection controller[J]. Sustainable Energy Technologies and Assessments, 2024, 72: 104051.
[14]	ZHAO D D, LI F, MA R, et al. An unknown input nonlinear observer based fractional order PID control of fuel cell air supply system[J]. IEEE Transactions on Industry Applications, 2020, 56(5): 5523-5532.
[15]	MA L, ZHAO H, QU Y, et al. Reduced‐order active disturbance rejection control method for PEMFC air intake system based on the estimation of oxygen excess ratio[J]. IET Renewable Power Generation, 2023, 17(4): 951-963.
[16]	LI J W, YU T. Intelligent controller based on distributed deep reinforcement learning for PEMFC air supply system[J]. IEEE Access, 2021, 9: 7496-7507.
[17]	齐鲲鹏, 陈超帆, HassanAli. 质子交换膜燃料电池阴阳极恒压差控制策略研究[J]. 客车技术与研究, 2023, 45(4): 1-6.
	QI Kunpeng, CHEN Chaofan, HASSAN A. Study on control strategy of cathode and anode stable pressure difference in proton exchange membrane fuel cell[J]. Bus & Coach Technology and Research, 2023, 45(4): 1-6.
[18]	ZHU J, ZHANG P, LI X, et al. Robust oxygen excess ratio control of PEMFC systems using adaptive dynamic programming[J]. Energy Reports, 2022, 8: 2036-2044.
[19]	GHASEMI J, RAKHTALA S M, RASEKHI J, et al. PEM fuel cell system to extend the stack life based on a PID-PSO controller design[J]. Complex Engineering Systems, 2022, 2(4):20517.
[20]	YILDIRIM B, GHEISARNEJAD M, ÖZDEMIR M T, et al. Multi-agent fuzzy Q-learning-based PEM fuel cell air-feed system control[J]. International Journal of Hydrogen Energy, 2024, 75: 354-362.
[21]	LIU J X, GAO Y B, SU X J, et al. Disturbance-observer-based control for air management of PEM fuel cell systems via sliding mode technique[J]. IEEE Transactions on Control Systems Technology, 2019, 27(3): 1129-1138.
[22]	ZHAO D D, XU L C, HUANGFU Y G, et al. Semi-physical modeling and control of a centrifugal compressor for the air feeding of a PEM fuel cell[J]. Energy Conversion and Management, 2017, 154: 380-386.
[23]	CHEN H C, LIU Y H, DENG C H, et al. Research on improving dynamic response ability of 30 kW real fuel cell system based on operating parameter optimization[J]. International Journal of Hydrogen Energy, 2023, 48(3): 1075-1089.
[24]	JIN L J, XU J C, WANG L Z. A sensorless control method for energy recovery of EGTAC to improve PEMFC efficiency[J]. IEEE Access, 2024, 12: 34160-34173.
[25]	肖仰淦, 吴肖龙, 李曦. 基于ADRC的PEMFC系统阴极相对湿度和氧气过量比控制[J]. 太阳能学报, 2023, 44(12): 499-509.
	XIAO Yanggan, WU Xiaolong, LI Xi. Control of cathode relative humidity and oxygen excess ratio in PEMFC system based on ADRC[J]. Acta Energiae Solaris Sinica, 2023, 44(12): 499-509.
[26]	袁昊, 张铁臣, 杨祖勇, 等. 基于自抗扰原理的燃料电池阳极压力控制研究[J]. 内燃机与配件, 2023(6): 14-18.
	YUAN Hao, ZHANG Tiechen, YANG Zuyong, et al. Research on anode pressure control of PEMFC based on active disturbance rejection principle[J]. Internal Combustion Engine & Parts, 2023(6): 14-18.
[27]	权浩迪, 刘勇国, 傅翀, 等. 多策略改进的蛇优化算法[J]. 计算机技术与发展, 2024, 34(5): 117-125.
	QUAN Haodi, LIU Yongguo, FU Chong, et al. Improved snake optimizer of multi-strategy[J]. Computer Technology and Development, 2024, 34(5): 117-125.
[28]	谢海波, 陈晗奔, 刘建彬, 等. 基于ITAE准则的大流量平衡阀关键参数优化研究[J]. 液压与气动, 2019, 43(3): 43-48.
	XIE Haibo, CHEN Hanben, LIU Jianbin, et al. Key parameters optimization based on ITAE criterion for large flow load control valve[J]. Chinese Hydraulics & Pneumatics, 2019, 43(3): 43-48.
[29]	班多晗, 吕鑫, 王鑫元. 基于一维混沌映射的高效图像加密算法[J]. 计算机科学, 2020, 47(4): 278-284.
	BAN Duohan, LV Xin, WANG Xinyuan. Efficient image encryption algorithm based on 1D chaotic map[J]. Computer Science, 2020, 47(4): 278-284.
[30]	JIA H M, ZHANG J R, RAO H H, et al. Improved sandcat swarm optimization algorithm for solving global optimum problems[J]. Artificial Intelligence Review, 2024, 58:10986.

E	ΔE
E	NL	NM	NS	ZE	PS	PM	PL
NL	PL/NL	PM/NL	PM/NM	PS/NM	PS/NS	ZE/ZE	ZE/ZE
NM	PM/NL	PM/NL	PS/NM	PS/NS	ZE/NS	ZE/ZE	NS/ZE
NS	PM/NL	PS/NM	PS/NS	ZE/NS	ZE/ZE	NS/PS	NM/PS
ZE	PM/NM	PS/NM	PS/NS	ZE/ZE	NS/PS	NS/PM	NM/PM
PS	PL/NM	PS/NS	ZE/ZE	NS/PS	NS/PS	NS/PM	NM/PL
PM	PS/ZE	ZE/ZE	NS/PS	NS/PS	NS/PM	NM/PL	NM/PL
PL	ZE/ZE	NS/ZE	NS/PS	NS/PM	NM/PM	NM/PL	NL/PL

项目	空气流量		空气压力
项目	OS/%	AT/s	AT/s
解耦前	26.91	5.28	14.07
PI解耦	3.74	6.03	13.36
fuzzyPI 解耦	2.31	1.75	9.67

项目		基准测试函数
项目		F₁	F₂	F₃	F₄
PSO	平均值	2.157×10^-5	1.046×10^-1	5.439×10^-3	7.574×10^-6
	最优值	1.054×10^-6	1.829×10^-2	3.382×10^-4	5.129×10^-8
	标准差	3.754×10^-5	2.785×10^-2	7.143×10^-3	6.729×10^-6
SO	平均值	3.057×10^-2	4.527×10^-3	5.147×10^-13	6.754×10^-4
	最优值	1.276×10^-3	2.711×10^-4	4.441×10^-15	9.389×10^-6
	标准差	1.056×10^-2	3.480×10^-3	3.724×10^-13	4.623×10^-4
ISO	平均值	9.473×10^-8	5.267×10^-4	5.178×10^-14	7.442×10^-9
	最优值	3.181×10^-9	3.377×10^-5	8.882×10^-16	9.521×10^-11
	标准差	5.418×10^-8	3.713×10^-4	4.135×10^-14	4.176×10^-9

参数	下界	上界
β₀₁	0	300
β₀₂	300	1 000
β₀₃	1 000	5 000
β₁	0	300
β₂	0	300

控制变量	控制方法	OS		AT/s
控制变量	控制方法	流量/%	压力/V	流量	压力
空气流量、压力解耦	未解耦	26.91	0	5.280 0	14.07
	PI	3.74	0	6.030 0	13.36
	Fuzzy-PI	2.31	0	1.750 0	9.67
恒定OER	PID	24.79		2.436 2
	fuzzyADRC	20.21		4.748 5
	ISO-fuzzyADRC	10.06		0.514 6
变OER	PID	6.17		16.471 4
	fuzzyADRC	2.05		11.427 5
	ISO-fuzzyADRC	0		7.103 6
Δp_ca	PID	13.98		7.375 8
	fuzzyADRC	0		4.937 6
	ISO-fuzzyADRC	5.07		1.428 5

基于蛇优化算法的PEMFC空气供给系统模糊自抗扰控制

Fuzzy active disturbance rejection control of PEMFC air intake unit based on snake optimization algorithm

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