Electronics and Electrical Engineering and Control

Modeling and control of fuel cell cathode gas supply system for UAV

  • ZHAO Dongdong ,
  • ZHAO Guosheng ,
  • XIA Lei ,
  • FANG Chun ,
  • MA Rui ,
  • HUANGFU Yigeng
Expand
  • 1. College of Automation, Northwestern Polytechnical University, Xi'an 710129, China;
    2. AVIC The First Aircraft Design and Research Institute of Aviation Industry, Xi'an 710089, China

Received date: 2020-08-20

  Revised date: 2020-09-08

  Online published: 2020-11-20

Supported by

National Natural Science Foundation of China(61873343)

Abstract

Fuel cells are considered to be the most potential power source for Unmanned Aerial Vehicles (UAVs) in the future due to their high efficiency, non-pollution, low noise, and other characteristics. The control technology of the cathode gas supply system of the fuel cell is the key technology to determine the performance and reliability of the fuel cell system. For the air supply system of the Proton Exchange Membrane Fuel Cell (PEMFC) for UAVs, the parameters that vary with altitude, such as the outside temperature, pressure, air density, and Reynolds number, are firstly analyzed. A cross-height centrifugal air compressor model is established and its working characteristics at different altitudes are analyzed. Based on the back electromotive force characteristics of the brushless DC motor, we build a drive motor model of the high-speed air compressor. Secondly, the output voltage of the PEMFC stack is obtained by calculating the dynamic partial pressure of oxygen and nitrogen in the cathode of the fuel cell. The oxygen excess ratio and cathode air pressure control methods based on fractional order PIλDμ are designed. The drive motor adopts finite set Model Predictive Control (MPC) to achieve fast torque response. The simulation results show that the designed controller can realize rapid adjustment of the oxygen excess ratio under the operating conditions of the UAV across the altitude, while maintaining the cathode pressure stability and meeting the fuel cell cathode gas supply requirements.

Cite this article

ZHAO Dongdong , ZHAO Guosheng , XIA Lei , FANG Chun , MA Rui , HUANGFU Yigeng . Modeling and control of fuel cell cathode gas supply system for UAV[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021 , 42(7) : 324659 -324659 . DOI: 10.7527/S1000-6893.2020.24659

References

[1] SHARAF O Z, ORHAN M F. An overview of fuel cell technology:Fundamentals and applications[J]. Renewable and Sustainable Energy Reviews, 2014, 32:810-853.
[2] 黄俊, 杨凤田. 新能源电动飞机发展与挑战[J]. 航空学报, 2016, 37(1):57-68. HUANG J, YANG F T. Development and challenges of electric aircraft with new energies[J]. Acta Aeronautica et Astronautica Sinica, 2016, 37(1):57-68(in Chinese).
[3] CHEN J, LIU Z Y, WANG F, et al. Optimal oxygen excess ratio control for PEM fuel cells[J]. IEEE Transactions on Control Systems Technology, 2018, 26(5):1711-1721.
[4] 向乾, 张晓辉, 王正平, 等. 适用无人机的小型燃料电池控制方法[J]. 航空学报, 2021, 42(3):92-103. XIANG Q, ZHANG X H, WANG Z P, et al. Control method of small fuel cells for UAVs[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(3):92-103(in Chinese).
[5] LEE B, PARK P, KIM C, et al. Power managements of a hybrid electric propulsion system for UAVs[J]. Journal of Mechanical Science and Technology, 2012, 26(8):2291-2299.
[6] GONG A, VERSTRAETE D. Fuel cell propulsion in small fixed-wing unmanned aerial vehicles:Current status and research needs[J]. International Journal of Hydrogen Energy, 2017, 42(33):21311-21333.
[7] PUKRUSHPAN J T, STEFANOPOULOU A G, PENG H E. Control of fuel cell breathing[J]. IEEE Control Systems Magazine, 2004, 24(2):30-46.
[8] DANZERM A, WILHELM J, ASCHEMANN H, et al. Model-based control of cathode pressure and oxygen excess ratio of a PEM fuel cell system[J]. Journal of Power Sources, 2008, 176(2):515-522.
[9] LEI T, YANG Z, LIN Z C, 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.
[10] 张晓辉, 刘莉, 戴月领, 等. 燃料电池无人机动力系统方案设计与试验[J]. 航空学报, 2018, 39(8):221874. ZHANG X H, LIU L, DAI Y L, et al. Design and test of propulsion system for fuel cell powered UAVs[J]. Acta Aeronautica et Astronautica Sinica, 2018, 39(8):221874(in Chinese).
[11] DENG Z H, CHEN Q H, ZHANG L Y, et al. Data driven NARMAX modeling for PEMFC air compressor[J]. International Journal of Hydrogen Energy, 2020, 45(39):20321-20328.
[12] 王云飞. 雷诺数对离心压气机性能影响的研究[D]. 哈尔滨:哈尔滨工业大学, 2012. WANG Y F. Effects of Reynolds number on the performance of centrifugal compressor[D]. Harbin:Harbin Institute of Technology, 2012(in Chinese).
[13] LI X, YANG C L, WANG Y Y, et al. Compressor map regression modelling based on partial least squares[J]. Royal Society Open Science, 2018, 5(8):172454.
[14] GRAVDAHL J T, EGELAND O, VATLAND S O. Drive torque actuation in active surge control of centrifugal compressors[J]. Automatica, 2002, 38(11):1881-1893.
[15] CHU F, WANG F L, WANG X G, et al. Performance modeling of centrifugal compressor using kernel partial least squares[J]. Applied Thermal Engineering, 2012, 44:90-99.
[16] PUKRUSHPAN J T, STEFANOPOULOU A G, PENG H. Control of fuel cell power systems:Principles,modeling, analysis and feedback design[M]. Berlin:Springer Science & Business Media, 2014.
[17] LI Q, CHEN WR, LIU Z X, et al. Control of proton exchange membrane fuel cell system breathing based on maximum net power control strategy[J]. Journal of Power Sources, 2013, 241:212-218.
[18] ZHANG Y, LI F Q, HU X, et al. Fuel cell air supply system control based on oxygen excess ratio[C]//IECON 2019-45th Annual Conference of the IEEE Industrial Electronics Society. Piscataway:IEEE Press, 2019:6394-6397.
[19] BAROUD Z, BENMILOUD M, BENALIA A, et al. Novel hybrid fuzzy-PID control scheme for air supply in PEM fuel-cell-based systems[J]. International Journal of Hydrogen Energy, 2017, 42(15):10435-10447.
[20] 刘秋秀. 燃料电池空气供给系统的调控策略研究[D]. 成都:电子科技大学, 2020. LIU Q X. Research on control strategy of fuel cell air supply system[D]. Chengdu:University of Electronic Science and Technology of China, 2020(in Chinese).
[21] 王帅. 质子交换膜燃料电池供气系统的建模与控制方法研究[D]. 哈尔滨:哈尔滨工业大学, 2019. WANG S. Research on modeling and control of PEM fuel cell air supply system[D]. Harbin:Harbin Institute of Technology, 2019(in Chinese).
[22] 李克雷, 李艳昆, 史青, 等. 基于车载燃料电池过氧比的空气流量控制[J]. 可再生能源, 2017, 35(2):304-310. LI K L, LI Y K, SHI Q, et al. Air flow control of vehicle fuel cell based on oxygen excess ratio[J]. Renewable Energy Resources, 2017, 35(2):304-310(in Chinese).
[23] 郭爱. 基于过氧比的车载燃料电池系统控制技术[D]. 成都:西南交通大学, 2015. GUO A. Control of fuel cell system for vehicle based on oxygen excess ratio[D]. Chengdu:Southwest Jiaotong University, 2015(in Chinese).
[24] ZHANG H K, WANG Y F, WANG D H, et al. Adaptive robust control of oxygen excess ratio for PEMFC system based on type-2 fuzzy logic system[J]. Information Sciences, 2020, 511:1-17.
[25] 方思雨. 车用燃料电池空气供给系统控制方法研究[D]. 大连:大连理工大学, 2019. FANG S Y. Research on control method of air supply system for vehicle fuel cells[D]. Dalian:Dalian University of Technology, 2019(in Chinese).
[26] GRUBER J K, DOLL M, BORDONS C. Design and experimental validation of a constrained MPC for the air feed of a fuel cell[J]. Control Engineering Practice, 2009, 17(8):874-885.
[27] DANZER M A, WILHELM J, ASCHEMANN H, et al. Model-based control of cathode pressure and oxygen excess ratio of a PEM fuel cell system[J]. Journal of Power Sources, 2008, 176(2):515-522.
[28] HÄHNEL C, AUL V, HORN J. Power efficient operation of a PEM fuel cell system using cathode pressure and excess ratio by nonlinear model predictive control[C]//2015 European Control Conference (ECC). Piscataway:IEEE Press, 2015:3340-3345.
[29] ZHAO D D, HUA Z G, DOU M F, et al. Control oriented modeling and analysis of centrifugal compressor working characteristic at variable altitude[J]. Aerospace Science and Technology, 2018, 72:174-182.
[30] MATRAJI I, AHMED F S, LAGHROUCHE S, et al. Extremum seeking control for net power output maximization of a PEM fuel cell using second order sliding mode[C]//2012 12th International Workshop on Variable Structure Systems, 2012:331-336.
[31] RAKHTALAS M, NOEI A R, GHADERI R, et al. Control of oxygen excess ratio in a PEM fuel cell system using high-order sliding-mode controller and observer[J]. Turkish Journal of Electrical Engineering & Computer Sciences, 2015, 23:255-278.
[32] 李奇, 陈维荣, 贾俊波, 等. 质子交换膜燃料电池动态响应建模与仿真研究[J]. 系统仿真学报, 2009, 21(11):3443-3447. LI Q, CHEN W R, JIA J B, et al. Modeling and dynamic response simulation of fuel cell[J]. Journal of System Simulation, 2009, 21(11):3443-3447(in Chinese).
[33] 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.
[34] PUKRUSHPAN J T, PENGH, STEFANOPOULOU A G. Control-oriented modeling and analysis for automotive fuel cell systems[J]. Journal of Dynamic Systems, Measurement, and Control, 2004, 126(1):14-25.
[35] 严慧. 分数阶PIλDμ控制器的设计及数字实现[D]. 南京:南京航空航天大学, 2007. YAN H. Research on design of fractional order PIλDμ controller and its digital implemetation[D]. Nanjing:Nanjing University of Aeronautics and Astronautics, 2007(in Chinese).
[36] 史婷娜, 李聪, 姜国凯, 等. 基于无模型预测控制的无刷直流电机换相转矩波动抑制策略[J]. 电工技术学报, 2016, 31(15):54-61. SHI T N, LI C, JIANG G K, et al. Model free predictive control method to suppress commutation torque ripple for brushless DC motor[J]. Transactions of China Electrotechnical Society, 2016, 31(15):54-61(in Chinese).
[37] ZHAO D D, WANG X P, TAN B, et al. Fast commutation error compensation for BLDC motors based on virtual neutral voltage[J]. IEEE Transactions on Power Electronics, 2021, 36(2):1259-1263.
[38] WANG F X, DAVARI S A, CHEN Z, et al. Finite control set model predictive torque control of induction machine with a robust adaptive observer[J]. IEEE Transactions on Industrial Electronics, 2017, 64(4):2631-2641.
[39] DARBA A, DE BELIE F, D'HAESE P, et al. Improved dynamic behavior in BLDC drives using model predictive speed and current control[J]. IEEE Transactions on Industrial Electronics, 2016, 63(2):728-740.
[40] XIA K, YE Y H, TIAN Y N, et al. The model predictive control method of torque ripple reduction for BLDC motor[J]. 2018 Asia-Pacific Magnetic Recording Conference (APMRC), 2018:1-2.
[41] DE CASTRO A G, PEREIRA W C A, DE ALMEIDA T E P, et al. Improved finite control-set model-based direct power control of BLDC motor with reduced torque ripple[J]. IEEE Transactions on Industry Applications, 2018, 54(5):4476-4484.
Outlines

/