基于Nelder-Mead优化的PEMFC三阶RQ等效电路参数辨识研究
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摘要
精确、有效的燃料电池等效电路有助于分析燃料电池内部工作情况,确保燃料电池系统稳定可靠地运行。针对燃料电池内部工作状态无法使用传感器精确监测的问题,提出了基于Nelder-Mead优化算法的PEMFC 3阶RQ等效电路参数辨识方法 ,根据电池工作原理建立3阶RQ非线性等效电路模型,将模型参数与实际物理意义相结合建立复数域下的实部、虚部加权目标函数,最后在电化学交流阻抗谱实验的基础上进行参数辨识研究。研究结果表明:在相同初始条件下,Nelder-Mead优化算法在精度、速度上与最小二乘法相当,但抗干扰能力更强;与遗传算法相比,精度相当但速度更高,可以用于燃料电池的在线参数辨识。
An accurate and effective equivalent circuit of fuel cell helps to analyze the internal working conditions of the fuel cell and ensure the stable and reliable operation of the fuel cell system. Aiming at the problem that the internal working state of the fuel cell cannot be accurately monitored by sensors, a parameter identification method for the third-order RQ equivalent circuit of proton exchange membrane fuel cell(PEMFC) was proposed based on the Nelder-Mead optimization algorithm. According to the working principle of the battery, a third-order RQ nonlinear equivalent circuit model was established. The model parameters and the actual physical meaning were combined to establish the weighted objective functions of the real and imaginary parts in the complex domain. Finally, parameter identification was carried out based on an electrochemical AC impedance spectroscopy experiment. Research results showed that under the same initial conditions, the Nelder-Mead optimization algorithm was equivalent to the least squares method in terms of accuracy and speed, but its anti-interference capability was stronger; compared with the genetic algorithm, its accuracy was comparable but its speed was higher, thus it can be used for the online parameter identification of fuel cell.
An accurate and effective equivalent circuit of fuel cell helps to analyze the internal working conditions of the fuel cell and ensure the stable and reliable operation of the fuel cell system. Aiming at the problem that the internal working state of the fuel cell cannot be accurately monitored by sensors, a parameter identification method for the third-order RQ equivalent circuit of proton exchange membrane fuel cell(PEMFC) was proposed based on the Nelder-Mead optimization algorithm. According to the working principle of the battery, a third-order RQ nonlinear equivalent circuit model was established. The model parameters and the actual physical meaning were combined to establish the weighted objective functions of the real and imaginary parts in the complex domain. Finally, parameter identification was carried out based on an electrochemical AC impedance spectroscopy experiment. Research results showed that under the same initial conditions, the Nelder-Mead optimization algorithm was equivalent to the least squares method in terms of accuracy and speed, but its anti-interference capability was stronger; compared with the genetic algorithm, its accuracy was comparable but its speed was higher, thus it can be used for the online parameter identification of fuel cell.
引文
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[20]罗琴琴,苏建徽,林志光,等.基于递推最小二乘法的虚拟同步发电机参数辨识方法[J].电力系统自动化,2019,43(1):215-221.Luo Qinqin,Su Jianhui,Lin Zhiguang,et al.Parameter identification method for virtual synchronous generators based on recursive least squares algorithm[J].Automation of Electric Power Systems,2019,43(1):215-221(in Chinese).
[21]刘占生,苏平线,姜兴渭.航天器电源模拟系统故障诊断的神经网络方法[J].哈尔滨工业大学学报,2001,33(1):65-67.Liu Zhansheng,Su Pingxian,Jiang Xingwei.A neural networks for fault diagnosis in spacecraft[J].Journal of Harbin Institute of Technology,2001,33(1):65-67(in Chinese).
[2]郭建伟,毛宗强,徐景明.采用交流阻抗法对质子交换膜燃料电池(PEMFC)电化学行为的研究[J].高等学校化学学报,2003,24(8):1477-1481.Guo Jianwei,Mao Zongqiang,Xu Jingming.Study on the electrochemical behavior of polymer electrolyte membrane fuel cell(PEMFC)by AC Impedance Method[J].Chemical Journal of Chinese Universities,2003,24(8):1477-1481(in Chinese).
[3]Petrone R,Zheng Zhixue,Hissel D,et al.A review on model-based diagnosis methodologies for PEMFCs[J].International Journal of Hydrogen Energy,2013,38(17):7077-7091.
[4]Salim R,Nabag M,Noura H,et al.The parameter identification of the Nexa 1.2 kW PEMFC's model using particle swarm optimization[J].Renewable Energy,2015,82:26-34.
[5]Rubio M A,Urquia A,Dormido S.Diagnosis of PEM fuel cells through current interruption[J].Journal of Power Sources,2007,171(2):670-677.
[6]Ettihir K,Boulon L,Becherif M,et al.Online identification of semi-empirical model parameters for PEMFCs[J].International Journal of Hydrogen Energy,2014,39(36):21165-21176.
[7]Priya K,Sathishkumar K,Rajasekar N.A comprehensive review on parameter estimation techniques for Proton Exchange Membrane fuel cell modelling[J].Renewable and Sustainable Energy Reviews,2018,93:121-144.
[8]Kandidayeni M,Macias A,Amamou A A,et al.Overview and benchmark analysis of fuel cell parameters estimation for energy management purposes[J].Journal of Power Sources,2018,380:92-104.
[9]Salim R I,Noura H,Fardoun A.A review on fault diagnosis tools of the proton exchange Membrane Fuel Cell[C].2013 Conference on Control and Fault-Tolerant Systems.Nice,France,2013:686-693.
[10]Yuan Xiaozi,Wang Haijiang,Colin Sun J,et al.AC impedance technique in PEM fuel cell diagnosis-A review[J].International Journal of Hydrogen Energy,2007,32(17):4365-4380.
[11]Kim J,Lee I,Tak Y,et al.Impedance-based diagnosis of polymer electrolyte membrane fuel cell failures associated with a low frequency ripple current[J].Renewable Energy,2013,51:302-309.
[12]Zeller A,Rallières O,Régnier J,et al.Diagnosis of a hydrogen/air fuel cell by a statistical model-based method[C].2010 IEEE Vehicle Power and Propulsion Conference.Lille,France,2010:1-6.
[13]Fouquet N,Doulet C,Nouillant C,et al.Model based PEMfuel cell state-of-health monitoring via ac impedance measurements[J].Journal of Power Sources,2006,159(2):905-913.
[14]鲜亮,肖建,贾俊波.质子交换膜燃料电池交流阻抗谱实验研究[J].中国电机工程学报,2010,30(35):101-106.Xian Liang,Xiao Jian,Jia Junbo.An experimental study on AC impedance spectroscopy of proton exchange membrane fuel cell[J].Proceedings of the CSEE,2010,30(35):101-106(in Chinese).
[15]刘盛捷.基于Nelder-Mead单纯形法的逆合成孔径激光雷达成像技术[D].北京:中国科学院大学中国科学院光电技术研究所,2018.Liu Shengjie.Imaging technology for inverse synthetic aperture lidar based on nelder-mead simplex method[D].Beijing:Institute of Optoelectronic Technology,Chinese A-cademy of Sciences,University of Chinese Academy of Sciences,2018(in Chinese).
[16]戴晓,江效龙,吴成,等.基于Nelder-Mead单纯形法的汽轮机调速系统参数辨识[J].仪器仪表用户,2015,22(1):32-34.Dai Xiao,Jiang Xiaolong,Wu Cheng,et al.Parameter identification of turbine governing system based on simplex method[J].Instrumentation,2015,22(1):32-34(in Chinese).
[17]程婵娟.系统辨识的线性规划方法研究[D].厦门:厦门大学,2009.Cheng Chanjuan.Research on system identification based on linear programming method[D].Xiamen:Xiamen University,2009(in Chinese).
[18]Nocedal J,Wright S J.Numerical optimization[M].Germany:Springer,1999:29-76.
[19]赵洋,姜鸣,刘学良.超级电容器等效电路模型参数辨识算法比较研究[J].电气应用,2015,34(15):80-84.
[20]罗琴琴,苏建徽,林志光,等.基于递推最小二乘法的虚拟同步发电机参数辨识方法[J].电力系统自动化,2019,43(1):215-221.Luo Qinqin,Su Jianhui,Lin Zhiguang,et al.Parameter identification method for virtual synchronous generators based on recursive least squares algorithm[J].Automation of Electric Power Systems,2019,43(1):215-221(in Chinese).
[21]刘占生,苏平线,姜兴渭.航天器电源模拟系统故障诊断的神经网络方法[J].哈尔滨工业大学学报,2001,33(1):65-67.Liu Zhansheng,Su Pingxian,Jiang Xingwei.A neural networks for fault diagnosis in spacecraft[J].Journal of Harbin Institute of Technology,2001,33(1):65-67(in Chinese).