基于支持向量回归机的燃料电池研究
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摘要
燃料电池（Fuel Cell, FC）能通过电化学反应把燃料的化学能直接转变成电能和热能，被认为是继水力、火力和核能之后的第四代发电技术。由于不受“卡诺循环”的限制，FC的能量转换率可达到60%以上，实际使用效率为普通内燃机的2~3倍。另外，由于工作不经过燃烧，FC不排放硫氧化物（SOX）与氮氧化物（NOX），对环境的污染极小。因此，作为一种高效、洁净的能源，FC已成为21世纪各国竞相发展的新型绿色能源。
支持向量机（Support Vector Machine, SVM）是由Vapnik等人在统计学习理论的VC维理论和结构风险最小化原则的基础上提出来的一类新型机器学习方法。它集成了最大间隔超平面、Mercer核、凸二次规划、稀疏解和松弛变量等多项技术，较好地解决了以往困扰很多机器学习方法的小样本、非线性、过学习、高维数、局部极小等实际问题。目前，SVM已经在很多不同领域的分类和回归问题上获得了很好的应用。当SVM用于解决回归问题时，又称为支持向量回归机（SupportVector Regression, SVR）。
由于FC是多变量输入及多变量输出系统，一个好的模型有利于FC研究过程的仿真、优化和评估。人们可以利用相关模型预测在不同的工艺参数/操作条件下燃料电池的性能。然而，现有大部分的燃料电池模型对于研究者和使用者来说过于复杂或准确性不够高。本论文利用基于粒子群寻优（Particle Swarm Optimization,PSO）算法的SVR建立起非线性离线模型用于FC及其各种性能的研究。基于实验数据的PSO-SVR的燃料电池研究，有助于提高实验的效率，可以节省大量的人力、时间和财力，为研发燃料电池提供了一条新思路和新方法，对推进燃料电池研制技术进步和燃料电池发展具有重要的意义。
本论文研究的主要内容包括：
（1）对FC的基本情况进行了简要的介绍和分析，包括FC的工作原理、分类、特点、发展及应用等。
（2）简述了SVR的理论基础：机器学习理论、统计学习理论，以及核函数等。
（3）根据质子交换膜燃料电池（Proton Exchange Membrane Fuel Cell，PEMFC）在不同工作温度和膜电阻下的实测PEM含水量的数据集，利用PSO-SVR，对PEMFC中的PEM含水量进行了建模与预测研究。虽然PEMFC中的PEM含水量和电池温度、膜电阻两因素之间存在非常复杂的非线性关系，然而PSO-SVR模型对于PEM含水量的预测值与实验值能很好的吻合，其平均绝对误差(MAE)=0.01，平均绝对百分误差(MAPE)=0.16%，复相关系数(R2)≈1.00。此外，还利用所建立的PSO-SVR模型对PEM含水量的最大值和最小值进行了预测：当电池温度为51.5℃，电阻为1.96m时，λmax=9.73；当电池温度为24.0℃，电阻为27.20m时，λmin=1.84。
（4）由于PEMFC电功率大小主要受电池温度、电池工作压强、阳极加湿温度、阴极加湿温度、氢流量比和氧流量比等因素的影响，我们以这些因素为输入，以PCMFC系统的输出电功率为输出的实测数据进行了PSO-SVR建模和预测研究。研究结果显示：PSO-SVR模型所预测的输出电功率的MAE仅为0.156W，MAPE为0.68%，R2达到0.998，表明PSO-SVR模型的预测值与实验值符合得很好。
（5）通过实测数据集，建立了以直接甲醇燃料电池（Direct Methanol Fuel Cell，DMFC）的电池温度和电池电流密度为输入参数的PSO-SVR模型，对DMFC的输出电压进行了回归预测研究，并与人工神经网络（Artificial Neural Networks,ANN）所建模型进行了比较。结果显示，ANN模型对DMFC的输出电压预测值的MAE和MAPE分别为0.009943V和2.23%；而PSO-SVR模型预测值的MAE和MAPE仅为0.004990V和0.93%；ANN模型的R2为0.991，而PSO-SVR模型的R2达到0.995。表明PSO-SVR模型的回归预测能力比ANN模型更强，可以很好地应用于DMFC输出电压的预测研究。
（6）基于不同工作温度和SSC含量下固体氧化物燃料电池（Solid Oxide FuelCell，SOFC）的BSCF-SSC复合阴极电导率的实测数据集，应用PSO-SVR方法，对SOFC的BSCF-SSC复合阴极电导率进行了建模预测研究。研究结果显示：预测电导率的MAE值、MAPE值和R2值分别为：0.0467S/cm，0.09%和0.999，预测值和实验值吻合得很好，模型可以用于BSCF-SSC复合阴极电导率预测。此外，利用建立的PSO-SVR模型对BSCF-SSC复合阴极电导率的最大值进行了预测，结果为：当工作温度为344℃，SSC含量为39wt%时，复合阴极的最大电导率可达242.9S/cm。
Fuel cell (FC), be capable of directly converting the chemical energy of fuel toelectrical energy and thermal energy by electrochemical reaction, is regarded as thefourth-generation of devices for generating electricity following hydroelectric power，thermal power and nuclear energy. Because it is not limited by the Carnot cycle, itsenergy conversion efficiency can be more than60%and is2~3times as that ofinternal-combustion engine. In addition, because there is no burning process duringworking, fuel cell does not emit harful gases such as sulfur oxide (SOx) and the nitrogenoxide (NOx) so that it has little pollution to environment. Therefore, as a kind of highefficient and clean energy, fuel cell has been competing for development by differentcountries in the21stcentury.
The Support Vector Machine (SVM) brought forward by Vapnik and others, is anew machine learning method based on the VC dimension theory and the StructuralRisk Minimization Principle of the Statistical Learning Theory. It integratedtechnologies in maximum interval hyper-plane, Mercer kernel, convex quadraticprogramming, sparse solution and slack variable, and solved various practical problemssuch as small samples, nonlinearity, over learning/fitting, high dimension and localminimization problem, etc. In recent years, SVM has been successfully employed tosolve classification and regression problems in many fields. When SVM is applied toregression, it is called support vector regression (SVR).
The FC system is a multi-input and multi-output system, a sound model can helpus to simulate, optimize and evaluate for FC. For example, one can foresee the output ofFC under different operating conditions by using a correlation model. However, most ofthe existing models for FCs are either too complicated, or the accuracy is not highenough for researchers and users. In this thesis, the nonlinear and offline models for FCsphysical properties are constructed by using the SVR approach combined with particleswarm optimization (PSO) algorithm for its parameter optimization. The research offuel cell based on PSO-SVR, is helpful to improve the experimental efficiency, can savea lot of manpower, valuable time and financial resources, provides a new clue for FCresearch, and would significantly promote the development of FC technology progressand FC development.
The main contents of this thesis are as follows:
(1) The basic information of the fuel cells are briefly summarized and analyzed,including their working principles, types, characteristics, development and theirapplications, etc.
(2) The theory of SVM has been introduced briefly, which contains machinelearning theory, statistical learning theory and kernel function theory, etc.
(3) According to experimental membrane water content dataset of proton exchangemembrane fuel cell (PEMFC), which was measured under different operatingtemperature and membrane impedance, a PSO-SVR model was established tomodel/predict the PEM membrane water content for PEMFC. The relationship betweenPEM membrane water content and two factors (cell temperature, membrane impedance)is very complicated and exists high-nonlinear, but the predicted value of PSO-SVRmodel can match the experimental value very well. The maen absolute error(MAE)=0.01, mean absolute percentage error (MAPE)=0.16%, correlation coefficient(R2)≈1.00, respectively. In addition, the available maximum PEM membrane watercontent and minimum PEM membrane water content are predicted by using theestablished PSO-SVR model, i.e., when the operating temperature is51.5℃andmembrane impedance is1.96m, the maximum PEM membrane water content wouldbe λmax=9.73; when the operating temperature is24.0℃, membrane impedance is27.20m, the minimum PEM membrane water content would be λmin=1.84.
(4) As the electrical power of PEMFC can be affected by the operatingtemperatures, operating pressures, anode/cathode humidification temperatures,anode/cathode stoichiometric flow ratios, these factors were acted as input variables andthe electrical power of PEMFC as output, a PSO-SVR model was constructed to predictthe electrical power of PEMFC. The results illuatrate that predicted the MAE is0.156W,the MAPE is0.68%and R2reaches0.998. These results demonstrate the calculatedvalues via the PSO-SVR model are quite agreement with the measured indices.
(5) By taking the direct methanol fuel cells (DMFC) cell temperature and cellcurrent density as input parameters, a PSO-SVR model was built to forecast the DMFCoutput voltage, the predicted performance was compared with that of Artificial NeuralNetwork (ANN) model. The result reveals that the predicted MAE and MAPE ofPSO-SVR reaches0.004990V and0.93%, which are superior to those(MAE=0.009943V and MAPE=2.23%) of ANN, respectively; the correlation coefficientR2=0.995of POS-SVR is also greater than that (0.991) of ANN model. This confirmedthat the PSO-SVR model regression/prediction ability surpasses that of ANN model, thus the PSO-SVR model is more suitable for modeling/predicting the DMFC cellvoltage.
(6) Based on the measured electrical conductivity dataset of BSCF-SSC compositecathode, a PSO-SVR model is constructed and employed to predict the BSCF-SSCcomposite cathode electrical conductivity. The results reveal that calculated MAE,MAPE and R2by the PSO-SVR model are0.0467S/cm,0.09%and0.999, respectively.These means that the predicted value of PSO-SVR model is tallied well with theexperimental value, and the PSO-SVR model can be used for BSCF-SSC compositecathode conductivity prediction. Use PSO-SVR model to predicted BSCF-SSCcomposite cathode conductivity, an available maximum BSCF-SSC composite cathodeconductivity was predicted via the PSO-SVR model, i.e., when the operatingtemperature is344℃and SSC content is39wt%, it would reach242.9S/cm.
支持向量机（Support Vector Machine, SVM）是由Vapnik等人在统计学习理论的VC维理论和结构风险最小化原则的基础上提出来的一类新型机器学习方法。它集成了最大间隔超平面、Mercer核、凸二次规划、稀疏解和松弛变量等多项技术，较好地解决了以往困扰很多机器学习方法的小样本、非线性、过学习、高维数、局部极小等实际问题。目前，SVM已经在很多不同领域的分类和回归问题上获得了很好的应用。当SVM用于解决回归问题时，又称为支持向量回归机（SupportVector Regression, SVR）。
由于FC是多变量输入及多变量输出系统，一个好的模型有利于FC研究过程的仿真、优化和评估。人们可以利用相关模型预测在不同的工艺参数/操作条件下燃料电池的性能。然而，现有大部分的燃料电池模型对于研究者和使用者来说过于复杂或准确性不够高。本论文利用基于粒子群寻优（Particle Swarm Optimization,PSO）算法的SVR建立起非线性离线模型用于FC及其各种性能的研究。基于实验数据的PSO-SVR的燃料电池研究，有助于提高实验的效率，可以节省大量的人力、时间和财力，为研发燃料电池提供了一条新思路和新方法，对推进燃料电池研制技术进步和燃料电池发展具有重要的意义。
本论文研究的主要内容包括：
（1）对FC的基本情况进行了简要的介绍和分析，包括FC的工作原理、分类、特点、发展及应用等。
（2）简述了SVR的理论基础：机器学习理论、统计学习理论，以及核函数等。
（3）根据质子交换膜燃料电池（Proton Exchange Membrane Fuel Cell，PEMFC）在不同工作温度和膜电阻下的实测PEM含水量的数据集，利用PSO-SVR，对PEMFC中的PEM含水量进行了建模与预测研究。虽然PEMFC中的PEM含水量和电池温度、膜电阻两因素之间存在非常复杂的非线性关系，然而PSO-SVR模型对于PEM含水量的预测值与实验值能很好的吻合，其平均绝对误差(MAE)=0.01，平均绝对百分误差(MAPE)=0.16%，复相关系数(R2)≈1.00。此外，还利用所建立的PSO-SVR模型对PEM含水量的最大值和最小值进行了预测：当电池温度为51.5℃，电阻为1.96m时，λmax=9.73；当电池温度为24.0℃，电阻为27.20m时，λmin=1.84。
（4）由于PEMFC电功率大小主要受电池温度、电池工作压强、阳极加湿温度、阴极加湿温度、氢流量比和氧流量比等因素的影响，我们以这些因素为输入，以PCMFC系统的输出电功率为输出的实测数据进行了PSO-SVR建模和预测研究。研究结果显示：PSO-SVR模型所预测的输出电功率的MAE仅为0.156W，MAPE为0.68%，R2达到0.998，表明PSO-SVR模型的预测值与实验值符合得很好。
（5）通过实测数据集，建立了以直接甲醇燃料电池（Direct Methanol Fuel Cell，DMFC）的电池温度和电池电流密度为输入参数的PSO-SVR模型，对DMFC的输出电压进行了回归预测研究，并与人工神经网络（Artificial Neural Networks,ANN）所建模型进行了比较。结果显示，ANN模型对DMFC的输出电压预测值的MAE和MAPE分别为0.009943V和2.23%；而PSO-SVR模型预测值的MAE和MAPE仅为0.004990V和0.93%；ANN模型的R2为0.991，而PSO-SVR模型的R2达到0.995。表明PSO-SVR模型的回归预测能力比ANN模型更强，可以很好地应用于DMFC输出电压的预测研究。
（6）基于不同工作温度和SSC含量下固体氧化物燃料电池（Solid Oxide FuelCell，SOFC）的BSCF-SSC复合阴极电导率的实测数据集，应用PSO-SVR方法，对SOFC的BSCF-SSC复合阴极电导率进行了建模预测研究。研究结果显示：预测电导率的MAE值、MAPE值和R2值分别为：0.0467S/cm，0.09%和0.999，预测值和实验值吻合得很好，模型可以用于BSCF-SSC复合阴极电导率预测。此外，利用建立的PSO-SVR模型对BSCF-SSC复合阴极电导率的最大值进行了预测，结果为：当工作温度为344℃，SSC含量为39wt%时，复合阴极的最大电导率可达242.9S/cm。
Fuel cell (FC), be capable of directly converting the chemical energy of fuel toelectrical energy and thermal energy by electrochemical reaction, is regarded as thefourth-generation of devices for generating electricity following hydroelectric power，thermal power and nuclear energy. Because it is not limited by the Carnot cycle, itsenergy conversion efficiency can be more than60%and is2~3times as that ofinternal-combustion engine. In addition, because there is no burning process duringworking, fuel cell does not emit harful gases such as sulfur oxide (SOx) and the nitrogenoxide (NOx) so that it has little pollution to environment. Therefore, as a kind of highefficient and clean energy, fuel cell has been competing for development by differentcountries in the21stcentury.
The Support Vector Machine (SVM) brought forward by Vapnik and others, is anew machine learning method based on the VC dimension theory and the StructuralRisk Minimization Principle of the Statistical Learning Theory. It integratedtechnologies in maximum interval hyper-plane, Mercer kernel, convex quadraticprogramming, sparse solution and slack variable, and solved various practical problemssuch as small samples, nonlinearity, over learning/fitting, high dimension and localminimization problem, etc. In recent years, SVM has been successfully employed tosolve classification and regression problems in many fields. When SVM is applied toregression, it is called support vector regression (SVR).
The FC system is a multi-input and multi-output system, a sound model can helpus to simulate, optimize and evaluate for FC. For example, one can foresee the output ofFC under different operating conditions by using a correlation model. However, most ofthe existing models for FCs are either too complicated, or the accuracy is not highenough for researchers and users. In this thesis, the nonlinear and offline models for FCsphysical properties are constructed by using the SVR approach combined with particleswarm optimization (PSO) algorithm for its parameter optimization. The research offuel cell based on PSO-SVR, is helpful to improve the experimental efficiency, can savea lot of manpower, valuable time and financial resources, provides a new clue for FCresearch, and would significantly promote the development of FC technology progressand FC development.
The main contents of this thesis are as follows:
(1) The basic information of the fuel cells are briefly summarized and analyzed,including their working principles, types, characteristics, development and theirapplications, etc.
(2) The theory of SVM has been introduced briefly, which contains machinelearning theory, statistical learning theory and kernel function theory, etc.
(3) According to experimental membrane water content dataset of proton exchangemembrane fuel cell (PEMFC), which was measured under different operatingtemperature and membrane impedance, a PSO-SVR model was established tomodel/predict the PEM membrane water content for PEMFC. The relationship betweenPEM membrane water content and two factors (cell temperature, membrane impedance)is very complicated and exists high-nonlinear, but the predicted value of PSO-SVRmodel can match the experimental value very well. The maen absolute error(MAE)=0.01, mean absolute percentage error (MAPE)=0.16%, correlation coefficient(R2)≈1.00, respectively. In addition, the available maximum PEM membrane watercontent and minimum PEM membrane water content are predicted by using theestablished PSO-SVR model, i.e., when the operating temperature is51.5℃andmembrane impedance is1.96m, the maximum PEM membrane water content wouldbe λmax=9.73; when the operating temperature is24.0℃, membrane impedance is27.20m, the minimum PEM membrane water content would be λmin=1.84.
(4) As the electrical power of PEMFC can be affected by the operatingtemperatures, operating pressures, anode/cathode humidification temperatures,anode/cathode stoichiometric flow ratios, these factors were acted as input variables andthe electrical power of PEMFC as output, a PSO-SVR model was constructed to predictthe electrical power of PEMFC. The results illuatrate that predicted the MAE is0.156W,the MAPE is0.68%and R2reaches0.998. These results demonstrate the calculatedvalues via the PSO-SVR model are quite agreement with the measured indices.
(5) By taking the direct methanol fuel cells (DMFC) cell temperature and cellcurrent density as input parameters, a PSO-SVR model was built to forecast the DMFCoutput voltage, the predicted performance was compared with that of Artificial NeuralNetwork (ANN) model. The result reveals that the predicted MAE and MAPE ofPSO-SVR reaches0.004990V and0.93%, which are superior to those(MAE=0.009943V and MAPE=2.23%) of ANN, respectively; the correlation coefficientR2=0.995of POS-SVR is also greater than that (0.991) of ANN model. This confirmedthat the PSO-SVR model regression/prediction ability surpasses that of ANN model, thus the PSO-SVR model is more suitable for modeling/predicting the DMFC cellvoltage.
(6) Based on the measured electrical conductivity dataset of BSCF-SSC compositecathode, a PSO-SVR model is constructed and employed to predict the BSCF-SSCcomposite cathode electrical conductivity. The results reveal that calculated MAE,MAPE and R2by the PSO-SVR model are0.0467S/cm,0.09%and0.999, respectively.These means that the predicted value of PSO-SVR model is tallied well with theexperimental value, and the PSO-SVR model can be used for BSCF-SSC compositecathode conductivity prediction. Use PSO-SVR model to predicted BSCF-SSCcomposite cathode conductivity, an available maximum BSCF-SSC composite cathodeconductivity was predicted via the PSO-SVR model, i.e., when the operatingtemperature is344℃and SSC content is39wt%, it would reach242.9S/cm.
引文
[1] Z. M. Jiang, Refletions on energy issues in China [J]. Journal of shanghai Jiaotong University,2008,13(3):257-274.
[2]衣宝廉.燃料电池-高效、环境友好的发电方式[M].北京：化学工业出版社,2000.
[3]衣宝廉.燃料电池-原理技术运用[M].北京：化学工业出版社,2003.
[4]石井弘毅,白彦华,杨晓辉译.图说燃料电池的原理与应用[M].北京：科学出版社,2003.
[5]毛宗强.燃料电池[M].北京：化学工业出版社,2005.
[6]毛晓峰,由宏新,阿布里提·阿布都拉,丁信伟.电解质支撑固体氧化物燃料电池发电性能模拟[J].天然气工业,2004,24(6):107-111.
[7] P. Aguiar, C. S. Adjiman, N. P. Brandon. Anode-supported intermediate temperature directinternal reforming solid oxide fuel cell Ⅱ.Model based dynamic performance and control [J].J. Power. Sources,2005,147:136-147.
[8]孙红,郭烈锦,刘洪谭等. PEM燃料电池中质子交换膜内水和质子的迁移特性[J].化工学报,2005,56(6):1081-1085.
[9] J. Peng, J. Y. Shin, T. W. Song. Transient response of high-temperature PEM fuel cell [J]. J.Power. Sources,2008,179:220-231.
[10]马利忠,申双林,贾斐,刘洪潭,郭烈锦. PEM燃料电池内水分布和温度分布的数值模拟[J].工程热物理学报,2010,31(12):2051-2056.
[11]周开利,康耀红编.神经网络模型及其MATLAB仿真程序设计[M].清华大学出版社,2006.
[12]靳蕃.神经网络智能基本原理、方法[M].西南交通大学出版社,2000.
[13] J. Arriagada, P. Olausson, A. Selimovic, Artificial neural network simulator for SOFCperformance prediction [J]. J. Power Sources,2002,112:54-60.
[14] X. J. Wu, X. J. Zhu, G. Y. Cao, et al. Modeling a SOFC stack based on GA-RBF neuralnetworks identification [J]. J. Power Sources,2007,167:145-150.
[15] S. Jemei, D. Hissel, M. C. Pera, et al. On-board fuel cell power supply modeling on the basisof neural network methodology [J]. J. Power Sources,2003,124(2):479-486.
[16] W. Li, X. J. Zhu, Z. J. Mo. Estimation of equivalent internal-resistance of PEM fuel cellusing artificial neural networks [J]. J. Cent. South. Univ. T,2007,14(5):690-695.
[17]江船.燃料电池[M].北京：国防工业出版社,1983.
[18]兰泽全,曹欣玉,周俊虎,朱建新,岑可法.质子交换膜燃料电池及其在电动车上应用的现状[J].能源工程,2002,3:17-20.
[19]李瑛,王林山.燃料电池[M].北京：冶金工业出版社，2000.
[20] S. Thomas, M. Zalbowitz, Fuel Cells-Green Power [M]. Los Alamos National Laboratory,1999.
[21]杨辉,卢文庆.应用电化学[M].北京:科学技术出版社,2001.
[22]黄镇江.燃料电池及其应用[M].北京:电子工业出版社，2005.
[23] EG&G Technical Services, Fuel Cell Handbook (Seventh Edition)[M].2004.
[24] N. Q. Minh, Ceramic Fuel Cells [J]. J. Am. Ceram Soc.,1993,76(3):563-588.
[25]赵群,张翔,李辉.基于燃料电池技术的新能源发展论述[J].机械,2007,7:1-5.
[26]刘雁.燃料电池-人类未来的能源终极解决方案[J].资源与人居环境,2007,5:28-31.
[27]邓乃扬,田英杰.数据挖掘中的新方法-支持向量机[M].北京:科学出版社,2004.
[28] V. N. Cristianini, J. Shawe-Tayor, An introduction to support vector machines and otherkernel-based learning methods [M]. Publishing House of Electronics Industry,2004.
[29] T. M. Mitchell, Machine Learning [M]. New York: McGraw-Hill Companies, Inc.,1997.
[30] V. N. Vapnik, The nature of statistical learning theory [M]. Berlin: Springer,1995.
[31] K. Veropoulos, C. Campbell, N. Cristianini, Controlling the sensitivity of support vectormachines [C]. Proceedings of the International Joint Conference on AI,1999:55-60.
[32] Z. Lu, J. Sun, Non-Mercer hybrid kernel for linear programming support vector regression innonlinear systems identification [J]. App. Soft. Comput,2009,9(1):94-99.
[33] G. Camps-Valls, L. Bruzzone, J. L. Rojo-Alvarez, F. Melgani, Robust support vectorregression for biophysical variable estimation from remotely sensed images [J].2006, IEEEGeoscience and Remote Sensing LettersS,3(3):339-343.
[34] K. W. Lau, Q. H. Wu, Local prediction of non-linear time series using support vectorregression [J]. Pattern Recog,2008,41(5):1539-1547.
[35] B. Schǒlkopf, A. J. Smola, Learning with kernels [M].1st edition. London: The MIT press,2002.
[36] C. Cortes, V. N. Vapnik, Support Vector Networks [M]. USA: Machine Learning,1995.
[37] J. A. K. Suykens, Support Vector Machines: A nonlinear modeling and control perspective [J].Eur. J. Control., Special Issue on Fundamental Issues in Control,2001,7(2-3):311-327.
[38] J. Kennedy, R. C. Eberhart, Particle swarm optimization[C]. In: IEEE Service Center ed.IEEE International Conference on Neural Networks, Perth, Australia,1995, Piscataway:IEEE Press,1995:1942-1948.
[39] R. Ebethart, J. Kennedy, A new optimizer using particle swarm theory[C], In: Proceeding ofthe6thInternational Symposium on Micro Machine and Human Science, NJ: IEEECS,1995:39-43.
[40] X. Hu, R. C. Eberhart, Adaptived Particle Swarm Optimization: Detection and Response toDynamic Systems[C]. In: The2002IEEE Congress on Evolutionary Computation.Piscataway, NJ: IEEE Press,2002,1666-1670.
[41] J. A. Nelder, A. Mead, A simple method for function minimization [J]. Comput J,1965,7:308-313.
[42] Y. H. Shi, R. Eberhart, A modified particle swarm optimizer [J]. IEEE InternationalConference on Evolutionary Computation,1998,69-73.
[43] J. X. Hu, Y. H. Shi, R. C. Eberhart, Recent advance in particle swarm, Proceedings of the2004Congress on Evolutionary Computation, Piscataway[J], NJ: IEEE Press,2004,90-97.
[44] R. C. Eberhart, Y. H. Shi, Special Issue on Particle Swarm Optimization [J], IEEETransactions on Evolutionary Computation,2004,8(3):201-203.
[45] R. C. Eberhart, X. H. Hu, Human tremor analysis using particle swarm optimization [J].Proceedings of the IEEE Congress on Evolutionary Computation, Piscataway,1999,1927-1930.
[46] M. P. Wachowiak, R. Smolikova, Y. Zheng et al, An Approach to Multimodal BiomedicalImage Registration Utilizing Particle Swarm Optimization [J]. IEEE Transactions onEvolutionary Computation,2004,8(3):289-301.
[47] T. Souda, A. Silva, A. Neves, Particle Swarm based Data Mining Algorithms forclassification task [J]. Parallel. Comput,2004,30:767-783.
[48] D. W. Merwe, A. P. Engelbrecht, Data Clustering using Particle Swarm Optimization. In:IEEE Congress on Evolutionary Computation [J]. Piscataway, NJ: IEEE Service Center,2003,215-220.
[49] H. Yoshida, K. Kawata, Y. Fukuyama et al., A Particle Swarm Optimization for ReactivePower and Voltage Control Considering Voltage Stability [J]. In: Proc of the InternationalConference on Intelligent System Application to Power Systems. Rio de Janeiro, Brazil,1999,1l7-121.
[50] Y. Liu, H. Zhang, Particle swarm optimization for fault state power supply reliabilityenhancement [J]. In: Proc of the IEEE International Conference on Intelligent SystemsApplication to Power Systems. Budapest, Hungary,2001,172-176.
[51] T. Ray, K. M. Liew, A swarm with an effective information sharing mechanism forunconstrained and constrained single objective optimization problems [J]. Proc IEEE Confon Evolutionary Computation, Seoul,2001,75-80.
[52] G. Ciuprina, D. Ioan, I. Munteanu, Use of intelligent-particle swarm optimization inelectromagnetics [J]. IEEE transactions on magnetics,2002,38(2):1037-1040.
[53] M. A. Abido, Optimal power flow using particle swarm optimization [J], Int. J. Elec Power,2002,24,563-571.
[54] R. Eberhart, Y. H. Shi, Tracking and optimizing dynamic systems with particle swarms [J],Proc IEEE Int Conf on Evolutionary Computation, Hawaii,2001,94-100.
[55] K. Parsopoulos, M. Vrahatis, Particle swarm optimization in noisy and continuouslychanging environments [J]. Artificial Intelligence and Soft Computing,2001,289-294.
[56] M. Omran, A. Salman and A.P. Engebrecht, Image Classification Using Particle SwarmOptimization [J]. In: The4th Asia-Pacific Conference on Simulated Evolution and Learning.Piscataway, NJ: IEEE Press,2002,370-374.
[57] L. N. Kassabalidis, M. A. EI-Sharkawi, R. J. Marks et al., Dynamic security borderidentification using enhanced particle swarm optimization [J], IEEE Transactions on PowerSystems,2002,17(3):723-729.
[58] R. C. Eberhart, Y. H. Shi, Particle swarm optimization: Developments, applications andresources [J]. In Proceedings of the2001Congress on Evolutionary Computation,2001,81-86.
[59] S. Doctor, G. Venayagamoorthy, Unmanned vehicle navigation using swarm intelligence [J].In: The2004International Conference on Intelligent Sensing and Information Processing.Piscataway, NJ: IEEE Press,249-253.
[60] L. Messerschmidt, P. E. Engelbrecht, Learning to play games using a PSO-based competitivelearning approach [J], IEEE Transactions on Evolutionary Computation,2004,8(3):280-288.
[61] A. Lunts, V. Brailovskiy, Evaluation of attributes obtained in statistical decision rules [J],Engineering Cybenretics,1967,3(l):98-109.
[62] V. Vapnik, O. Chapelle, Boundson, Error expectation for support vector machine [J].Advances in Large Margin Classifiers, Cambridge, M A: MIT Press,2000:5-26.
[63] T. Jaakkola, D. Haussler, Probabilistic kernel regression models [J]. Proceedings of the7thWorkshop on AI and Statistics, San Francisco,1999:26-34.
[64] G. Wahba, Y. Lin, H. Zhang, Generalized approximate cross-validation for support vectormachines: Another way to look at margin-like quantities [J]. Advances in Large MargeClassifiers, Cambridge, M A: MIT Press,2000:397-309.
[65] T. Joachims, Estimating the generalization performance of a SVM efficiently [J]. In:Proceedings of the17th International Conference on Machine Leaning, Morgan Kaufman,2000,431-438.
[66] R. Kohavi, A study of cross-validation and bootstrap for accuracy estimation and modelselection [J]. Proceedings of the Fourteenth International Joint Conference on ArtificialIntelligence,1995,2(12):1137-1143.
[67] J. S. Chang, Y. F. Luo, K. Y. Su, GPSM: a generalized probabilistic semantic model forambiguity resolution [C]. In Proceedings of the30th Annual Meeting on Association forComputational Linguistics (Newark, Delaware, June28-July02,1992) Annual Meeting ofthe ACL Association for Computational Linguistics, Morristown, NJ,177-184.
[68] A. R. Ahmad, M. Khalid, R. Yusof, Kernel methods and support vector machines forhandwriting recognition[C]. Student Conference on Research and Development, Shah, Alam,Selangor,2002:309-312.
[69] A. V. Anghelescu, I. B. Muchnik, Combinatorial PCA and SVM methods for feature selectionin learning classifications (Applications to text categorization)[J], International Conferenceon Integration of Knowledge Intensive Multi-Agent Systems,2003,491-496.
[70] V. Wan, W. M Campbell, Support vector machines for speaker verification and identification[J]. Proceedings of IEEE Signal Processing Society Workshop on Neural Networks for SignalProcessing,2000,2(2):775-784.
[71] W. M. Campbell, A SVM/HMM system for speaker recognition [J]. In Proceedings of IEEEInternational Conference on Acoustics, Speech, and Signal Processing,2003,2: II-209-212.
[72] A. Tefas, C. Kotropoulos, I. Pitas, Using support vector machines to enhance theperformance of elastic graph matching for frontal face authentication [J]. IEEE Transactionson Pattern Analysis and Machine Intelligence,2001,23(7):735-746.
[73] K. H. Lee, Y. W. Chung, H. Byun, SVM-based face verification with feature set of small size[J]. Electronics Letters,2002,38(15):787-789.
[74] H. Sahbi, D. Geman, N. Boujemaa. Face detection using coarse-to-fine support vectorclassifiers [J]. Proceeding of the2002International Conference on Image Processing,2002,3:925-928.
[75] X. Dihua, L. S. Whan, Face detection and facial feature extraction using support vectormachines [J]. Proceedings of16th International Conference on Pattern Recognition,2002,4(4):209-212.
[76] K. Lee, H. Byun, A new face authentication system for memory-constrained devices [J].IEEE Transactions on Consumer Electronics,2003,49(4):1214-1222.
[77] Y. H. Liu, Y. T. Chen, Face recognition using total margin-based adaptive fuzzy supportvector machines [J]. IEEE Transactions on Neural Networks,2007,18(l):178-192.
[78] G. D. Guo, A. K. Jain, W. Y. Ma, et al., Learning similarity measure for natural imageretrieval with relevance feedback [J]. IEEE Transactions on Neural Networks,2002,13(4):811-820.
[79] F. Melgani, L. Bruzzone, Classification of hyperspectral remote sensing Images with supportvector machines [J]. IEEE Transactions on Geoscience and Remote Sensing,2004,42(8):1778-1790.
[80] Y. Bazi, F. Melgani, Toward an optimal SVM classification system for hyperspectral remotesensing images [J]. IEEE Transactions on Geoscience and Remote Sensing,2006,44(11):3452-3461.
[81] J. Li, N. Allinson, D. Tao, et al., Multitraining support vector machine for image retrieval[J],IEEE Transactions on Image Processing,2006,15(11):3597-3601.
[82] M. M. Chi, L. Bruzzone, Semisupervised classification of hyperspectral images by SVMsoptimized in the primal. Transactions on Geoscience and Remote Sensingm [J],2007,45(6):1870-1880.
[83] W. Liu, P. Shen, Y. Qu et al., Fast algorithm of support vector machines in lung cancerdiagnosis [J]. Proceedings of International Workshop on Medical Imaging and AugmentedReality, Hong Kong, China,2001:188-192.
[84] P. K. N. Bhanu, A. G. Ramakrishnan, S. Suresh et al., Fetal lung maturity analysis usingultrasound image features [J]. IEEE Transactions on Information Technology in Biomedicine,2002,6(1):38-45.
[85] S. Osowski, L. T. Hoai, T. Markiewicz, Support vector machine-based expert system forreliable heartbeat recognition [J]. IEEE Transactions on Biomedical Engineering,2004,51(4):582-589.
[86] I. E. Naqa, Y. Y. Yang, A support vector machine approach for detection ofMicro-calcifications[J], IEEE Transactions on Medical Imaging,2002,21(12):1552-1563.
[87] W. H. Land, L. Wong, D. W. Mckee, et al., Breast cancer computer aided diagnosis (CAD)using a recently developed SVM/GRNN Oracle hybrid [J]. IEEE International Conference onSystems, Man and Cybernetics,2003,5:4705-4711.
[88] C. Batur, L. Zhou, C. C. Chan. Support vector machines for fault detection [J]. Proceedingsof the41stIEEE Conference on Decision and Control,2002,2:1355-1356.
[89] M. Rychetsky, S. Ortmann, M. Glesner, Support Vector Approaches for Engine KnockDetection [J]. International Joint Conference on Neural Networks,1999,2:969-974.
[90] S. J. Hong, S. M. Weiss, Advance in predictive models for data mining [J]. Pattern RecognLett,2001,22:55-61.
[91] S. Chen, L. Hanzo. Block-adaptive kernel-based CDMA multiuser detection [J]. IEEEInternational Conference on Communications,2002,2:682-686.
[92] A. Kuh, Adaptive kernel methods for CDMA systems [J]. Proceedings of IJCNN'01International Joint Conference on Neural Networks,2001,4:2404-2409.
[93] J. Gao, W. Shi, J. Tan, et al., Support vector machines based approach for fault diagnosis ofvalves in reciprocating pumps [J]. Proceeding of IEEE Conference on Electrical andComputer Engineering, Canadian,2002,3:1622-1627.
[94] H. Drucker, B. Shahrary, C. G. David, Support vector machine: Relevance feedback andinformation retrieval [J]. Inform. Process and manag,2002,38:305-323.
[95] K. R. Muller, A. J. Smola, Prediction time series with support vector machines [J],Proceeding of the International Conference on Artificial Neural Networks, Lausanne,Switzerland, Springer,1997:999-1004.
[96] J. Sjoberg, Q. Zhang, L. Ljung, Nonlinear black-box modeling in system identification: aunified overview [J]. Automatica,1995,31(12):1691-1724.
[97] M. Brown, S.R. Gunn, Empirical data modelling algorithms: additive spline models andsupport vector machines [J]. Proc of UKACC International Conference on Control, Swansea,UK,1998,1:709-714.
[98] F. E. H. Tay, L. Cau, Application of support vector machines in financial time seriesforecasting [J]. Omega,2001,29:309-317.
[99] S. Mukherjee, E. Osuna, F. Girosi, Nonlinear prediction of chaotic time series using a supportvector machine [J]. Neural Networks for Signal Processing VII-Proceeding of the1997IEEEWorkshop, New York,1997:511520.
[100] D. Mao, Z. Y. Zeng, C. Wang, W. H. Lin, Support vector machine with PSO algorithm forsoil erosion evaluation and prediction [J]. Proceedings of Third International Conference onNatural Computation2007,1:656-660.
[101] K. B. Parter, Solid polymer fuel cell developments at ballard [J]. J. power sources,1992,37:181-188.
[102] P. Rodatz, F. Buchi, F Onder et al., Operational aspects of a large PEFC stack under practicalconditions [J]. J. powder source,2004,128:208-2.
[103] Angelo, U. Dufour, Fuel Cells: A new contributor to stationery power [J]. J. Power Sources,1998,71:15-19.
[104] M. D. Francesco, E. Arato, Star-up analysis for automotive PEMFC systems [J]. J. PowerSources,2002,108:41-52.
[105] M. Zobel, A. Polymer, Electrolyte membrane fuel Ccell system for powering portablecomputers [J]. J. Power Sources,2003,122: l-8.
[106] T. F. Fuller, J. Newman, Water and thermal management in solid-polymer-electrolyte fuelcells [J]. J. Electrochem. Soc,1993,140(5):1218-1224.
[107] S. H. Chan, W. A. Tun, Catalyst layer models for proton exchange membrane fuel cells [J].Chem. Eng. Technol,2001,24(1):51-57.
[108] A. Rowel, X. G. L, Mathematical modeling of proton exchange membrane fuel cells [J]. J.Power Sources,2001,102:82-96.
[109] N. Djilali, D. M. Lu. Influence of heat transfer on gas and water transport in fuel cells [J]. Int.J. Therm. Sci,2002,41(1):29-40.
[110] T. V. Nguyen, R. E. White, A water and heat management model for proton exchangemembrane fuel cells[J]. J. Electrochem. Soc,1993,140(8):2178-2186.
[111] J. S. Yi, T. V. Nguyen, An along-the-channel model for protone exchange membrane fuelcells [J]. J. Electrochem. Soc,1998,145(4):1149-1159.
[112] V. Gurau, H. T. Liu, S. Kakac, Two-dimensional model for proton exchange membrane fuelcells [J]. Aiche J,1998,44(11):2410.
[113] W. He, J. S. Yi, T. V. Nguyen, Two-phase flow model of the cathode of PEM fuel cells usinginterdigitated flow fields [J]. Aiche J,2000,46(10):2053-2064.
[114]周孝锋,陈晓宁,徐晔,张云生蛇形流场PEMFC的三维多物理场数值模拟[J].电池工业,2010,15(6):367-371.
[115] A. Su, Y. M. Femg, J. C. Shih, CFD investigating the effects of different operating conditionson the performance and the characteristics of a high-temperature PEMFC [J]. Energy,2010,35:16-27.
[116] G. L. Hu, J. R. Fan, A three-dimensional, multicomponent, two-phase model for a protonexchange membrane fuel cell with straight channels [J].·Energ. Fuel,2006,20(2):738-747.
[117]杜春雨,史鹏飞.三维质子交换膜燃料电池的完整模型[J].哈尔滨工业大学学报,2006,38(9):1511-1514.
[118]蔡年生.质子交换膜在燃料电池中的应用[J].膜科学与技术,1996,16(4):1-6.
[119] A. J. Appleby, F. R. Foulkes, Fuel cell handbook [M]. Van Nostrand Reinhold, New York,1989.
[120] M. A. Hickner, H. Ghassemi, Y. S. Kim, et al., Alternative polymer systems for protonexchange membranes (PEMs)[J]. Chemical Reviews,2004,104,4587-4612.
[121] M. Wakizoe, O. A. Velev, S. Srinivasan, Analysis of proton exchange membrane fuel cellperformance with alternate membranes [J]. Electrochimica Acta1995,40(3):335-344.
[122] M. Yoshitake, M. Tamura, N. Yoshida, et al., Studies of perfluorinated ion exchangemembranes for polymer elecrtolyte fuel cells [J]. Denki Kagaku,1996,64(6):727-736.
[123] N. Yoshida, T. Ishisaki, A. Watanabe, et al., Characterization of flemion membranes forPEFC [J]. Elecctrochimica Acta,1998,43(24):3749-3754.
[124] O. Savadogo, Emerging membranes for electrochemical sysytems:(I) solid polymerelectrolyte membranes for fuel cell syytems [J]. J. New Mat for Electrochem Systems,1998,1:47-66.
[125] M. Yoshitake, M. Tamura, N. Yoshida, et al., Studies of perfluorinated ion exehangemembranes for polymer electrolyte fuel cells. Denki Kagaku [J].1996,64(6):727-736.
[126] Y. Yin, J. Fang, H. Kita, et al., Novel sulfoalkoxylated polyimide membrane for polymerelectrolyte fuel cells[J]. Chemstry Letters,2003,32(4):328-329.
[127] R. A. Komoroski, K. A. Mauritz, A sodium nuclear magnetic resonance study of ionicmobility and contaction pairing in a perfluorosulfonate ionomer[J]. J. Am. Chem. Sco.,1978,100:7487-7489.
[128] W. Y. Hsu, T. D. Girerke, Ion transport and clustering in Nafion perfluorinated membranes[J]. J. Membrance Sci.,1983,13:307-326.
[129] P. C. Lee, D. Meisel, Luminescence quenching in the cluster network of perfluorosulfonatemembrane [J]. J. Am. Chem. Soc.1980,102:5477-5481.
[130] T. E. Springer, T. A. Zawodzinski, S. Gottesfeld, Polymer electrolyte fuel cell model [J]. J.Electrochem Soc1991,138:2334-2342.
[131] U. Pasaogullari, C. Y. Wang, Two-phase transport and the role of micro-porous layer inpolymer electrolyte fuel cells [J]. Electrochim Acta,2004,49:4359-4369.
[132] D. P. Wilkinson, H. H. Voss, K. Prater, Water management and stack design for solidpolymer fuel cells [J]. J. Power Sources,1994,49:117-127.
[133] F. A. Uribe, T. E. Springer, S. Gottesfeld, Microelectrode study of oxygen reduction at theplatinum/recast-nafion film interface [J]. J. Electrochem. Soc.,1992,139:765-773.
[134] R. Mosdale, S. Srinivasan, Analysis of performance and of water and thermal management inproton-exchange membrane fuel cells [J]. Electrochimica Acta,1995,40(4):413-421.
[135] F. Barbir, H. Gorgun, X. Wang, Relationship between pressure drop and cell resistance as adiagnostic tool for PEM fuel cells (Short communication)[J]. J. Power Sources,2005,141:96-101.
[136] T. A. Zawodzinski, C. Derouin, S. Radzinski, Water up take by and transport through Nafionl17membranes [J]. Electrochem. Soc.,1993,140:1041-1047.
[137] T. E. Springer, M. S. Wilson, S. Gottefsfeld, Modeling and experimental diagnositcs inpolymer electrolyte fuel cells [J]. J. Electrochem Soc,1993,140:3513-3526.
[138] P. C. Rieke, N. E. Vanderborgh, Temperature dependence of water content and protonconductivity in polyperfluorosulfonic acid membranes [J]. J. Membrane Sci,1987,32(2-3):313-328.
[139] Y. Sone, P. Ekdunge, D. Simonsson, Proton conductivity of Nafion117as measured by afour-electrode AC impedance method [J] J. Electrochem Soc,1996,1434:1254-1259.
[140]杜文朝,张立炎,全书海.基于内阻测试的质子交换膜含水量软测量研究[J].2009,26（2）:14-17.
[141] W. L. Yu, S. J. Wu, S. W Shiah, Parametric analysis of the proton exchange membrane fuelcell performance using design of experiments [J] Int. J. Hydrogen energ.,2008,33,2311-2322.
[142] V. Neburchilov, J. Martin, H. J. Wan, J. J. Zhang, A review of polymer electrolyte membranesfor direct methanol fuel cells [J]. J. Power Sources,2007,169(2):221-238.
[143] N. T. Nguyen, S. H. Chan, Micromachined polymer electrolyte membrane and directmethanol fuel cells-A review [J]. J. Micro. Microeng.,2006,16(4):1-12.
[144] A. Heinzel, V. M. Barragan, A review of the state-of-the-art of the methanol crossover indirect methanol fuel cells [J]. J. Power Sources,1999,84(l):70-74.
[145] S. Wasmus, A. Kuver, Methanol oxidation and direct methanol fuel cells: a selective review[J]. J. Elec. Chem.,1999,461(l-2):14-31.
[146] S. Surampudi, Aqueous liquid feed organic fuel cell using solid polymer electrolytemembrane [P] USP:5599638,1997202204:
[147] R. G. Hockaday, Fuel cell [P] USP:4673624,1987206216:
[148] J. Pavio, J. Hallmark, J. Bostaph, et al., Developing micro-fuel cells for wirelesscommunications [J]. Fuel Cells Bulletin,2002,2002(4):8-11.
[149] M. Waidhas, W. Drenckhahn, W. Preidel, et al., Direct-fuelled fuel cell [J]. J. Power Sources,1996,61(1):91-97.
[150] M. Baldauf, W. Preidel, Status of the development of a direct methanol fuel cell [J] J. PowerSources,1999,84(2):161-166.
[151] D. Buttin, M. Dupont, M. Straumann, et al., Development and operation of a150W air-feeddirect methanol fuel cell stack [J]. J. Appl. Electrochem.,2001,31:275-279
[152] P. Piela, R. Fields, P. Zelenay, Electrochemical impedance spectroscopy for direct methanolfuel cell diagnostics [J]. J. Eletroehem Soc,2006,153(10): A1902-A1913.
[153] K. Scott, W. Taama, J. Cruickshank, Performance and modeling of a direct methanol solidpolymer electrolyte fuel cell [J]. J. Power Sources,1997,65:159-171.
[154] K. Scott, P. Argyropoulos, K. Sundamacher, A model for the liquid feed direct methanol fuelcell [J]. J. Electroanal. Chem.,1999,477:97-110.
[155]康明艳,土红星,许莉,土宇新.直接甲醇燃料电池中多物理量分布的一维模拟[J].电源技术,2006,30(11)：890-894.
[156]胡军,衣宝廉.常规流场质子交换膜燃料电池阴极二维两相流模型[J].化工学报,2004,55:967-973.
[157] A. A. Kulikovsky, Two-dimensional numerical modeling of a direct methanol fuel cell [J]. J.Appl. Electrochem.,2000,30:1005-1014.
[158]胡桂林,樊建人,岑可法.直接液体甲醇燃料电池的数学模型[J].动力工程,2003,23(3):2465-2469.
[159]苗政,邹金强,何雅玲,刘增.空气自呼吸式直接甲醇燃料电池传输特性数值研究[J].化工学报,2011,62(s1):66-71
[160]胡桂林,刘永江.质子交换膜燃料电池内传递现象的数值模拟[J].工程热物理学报,2004,25:828-830.
[161] A. A. Kulikovsky, Numerieal simulation of a new operational regime for a polymer eletrolytefuel cell [J] Electrochem Commun,2001,3:460-466.
[162]王胜军,陈吉,邹志青.基于MEMS技术的微型DMFC阳极流场结构的设计与研究[J].功能材料与器件学报,2011,17(5):476-480.
[163]王晓红,谢克文,姜英琪,钟凌燕,刘理天.硅基微型直接甲醇燃料电池的研究[J].半导体学报,2005,26(7):1437-1441.
[164] S. D. Ou, L. E. K. Achenie, A hybrid neural network model for PEM fuel cells [J]. J PowerSources,2005,140:319-330.
[165] Y. F. Yi, A. D. Ra, J. Brouwer et al., Analysis and optimization of a solid oxide fuel cell andintercooled gas turbine (SOFC–ICGT) hybrid cycle [J]. J. power sources,2004,132:77-85.
[166] T. Kawada, N. Sakai, H. Yakolawa, et al. Characteristics of Slurry-coated Nickel ZirconiaCermet Anodes for Solid Oxide Fuel Cell [J]. Electrochem Soc.,1990,137:3042-3049.
[167] B. C. H. Steele, Survey of materials selection for ceramic fuel cells cathodes and anodes [J]Solid State Ionics,1996,86-88:1223-1234.
[168] J. G. Cheng, L. P. Deng, B. Zhang, P. Shi, Properties and microstructure of NIO/SDCmaterials for SOFC anode applications[J]. Rare Metals,2007,26:110-117.
[169] K. C. Wincewicz, J. S. Cooper, Taxonomies of SOFC material and manufacturingalternatives [J]. J. Power Sources,2005,140:280-296.
[170] N. Bonanos, Proton conducting ceramics and their applications [J]. Solid State Ionics,2001,86-88:9-15.
[171] J. Liu, W. Liu, Z. Lv, et al. Study on the properties of YSZ electrolyte made by plaser castingmethod and the applications in solid oxide fuel cells [J]. Solid State Ionics,1999,118:67-72.
[172] S. P. S. Badwal, K. Foger. Materials for solid oxide fuel cells [J] Mater Forum,1997,21:187-224.
[173] S. C. Singhal, Advances in solid oxide fuel cell technology [J]. Solid State Ionics,2000,135:305-313.
[174] W. Z. Zhu, S. C. Deevi, Development of inter connect materials for solid oxide fuel cells [J].Mat. Sci Eng A,2003, A348(l-2):227-243.
[175] S. C. Singhal, Science and technology of solid-oxide fuel cells [J]. MRS Bulletin,2000,25(3):16-21.
[176] J. W. Fergus, Metallic interconnects for solid oxide fuel cells [J]. Mat. Sci. Eng A,2005,397(l-2):271-283.
[177] N. Q. Minh, T. Takahashi, Science and technology of ceramics fuel cells [M]. Elsevierpublishing, Amsterdam, The Netherlands,1995,283-296.
[178] R. Doshi, V. L. Righards, J. D. Carter, et al., Development of Solid-Oxide Fuel Cells ThatOperate at500℃[J]. J. Electrochem Soc.,1999,146:1273-1278.
[179] S. C. Sinhal, Solid oxide fuel cells [J]. Electrochem Soc Interface,2007,16(4):41-44.
[180] W. Bujalskp, C. M. Dikwal, K. Kendall, Cycling of three solid oxide fuel cell types [J]. J.Power Sources,2007,171:96–100.
[181]史翊翔,蔡宁生.固体氧化物燃料电池阴极数学模型与性能分析[J].中国电机工程学报,2006,26(4):82-87.
[182]冯兴强,余晴春.平板式固体氧化物燃料电池的建模与仿真研究[J].微型电脑应用,2010,26(9):7-9.
[183]李晨,史翊翔,蔡宁生.管式固体氧化物燃料电池的数值模拟[J].动力工程,2006,26(5):742-746.
[184]史翊翔,李晨,蔡宁生.管式固体氧化物燃料电池机理模型与性能分析[J].化工学报,2007,58(3):722-727.
[185]李彦,胡桂林,骆仲泱,余春江.固体氧化物燃料电池的三维数值模拟[J].电源技术,2009,133:865-868.
[186]杨国刚,岳丹婷,吕欣荣,袁金良.多孔介质孔隙率对SOFC阳极管道化学/电化学反应与传递过程影响[J].工程热物理学报,2010,35(5):864-866.
[187]罗丹,王关晴,黄雪峰,徐江荣.阳极支撑中温固体氧化物燃料电池的数值模拟[J].杭州电子科技大学学报,2010,30(4):50-54.
[188] S. P. Jlang, J. P. Zhang, X. G. Zhang, et al., A compative investigate of chromium depositionat air electrodes of solid oxide fuel cells [J]. J. Eur Ceram Soc,2002,22(3):361-373.
[189] E. Maguire, B. Gharbage, F. M. B. Marques, et al., Cathode materials for intermediatetemperature SOFCs [J].Solid State Ionies,2000,127(l):329-335.
[190] H. Lou, Y. P. Ge, P. Chen, M. H. Mei, F. T. Ma, G. L. Lu, Preparation, crystal structure andreducibility of K2NiF4type oxides Sm2xSrxNiO4[J]. Mater Chem.,1997,7(10):2097-2101.
[191] S. D. Mark, M. S. Islam, G. W. Watson, F. E. Hancock, Surface structures and defectproperties of pure and doped La2NiO4[J]. J. Mater. Chem.,2001,(11):2597-2602.
[192]李强.类钙钛矿结构中温固体氧化物燃料电池阴极材料的性能研究[D].哈尔滨理工大学,2007.
[193] X. M. Yang, L. T. Luo, H. Zhong. Structure of La2xSrxCoO4±λ(x=0.0-1.0) and their catalyticproperties in the oxidation of CO and C3H8[J]. Applied Catalysis A,2004,272:299-303
[194] H. W. Nie, T. L. Wen, S. R. Wang, Y. S. Wang, U. Guth, V. Vashook. Preparation, thermalexpansion, chemical compatibility, electrical conductivity and polarization of A2-αA’αMO4(A=Pr, Sm; A’=Sr; M=Mn, Ni; α=0.3,0.6) as a new cathode for SOFC [J]. Solid State Ionics,2006,177:1929-1932.
[195]高建峰.中温固体氧化物燃料电池阴极材料及电极过程研究[D].中国科技大学材料科学与工程学院,2003.
[196] R. Maric, S. Ohara, T. Fukui, High-performance Ni-SDC cermet anode forsolid oxide fuelcells at medium operating temperature [J]. Electrochem Solid State Lett,1998,1(5):201-203.
[197] J. Mizusaki, H. Tagawa, K. Tsuneyoshi, et al., Reaction Kinetics and Microstructure of theSOFC Air Electrode Lao.6Cao.4MnO3/YSZ [J]. J. Electrochem Soc.1991,138:1867-1874.
[198] T. Kenjo, M. Nishiya, LaMnO3air cathode containing ZrO2electrolyte or high temperaturesolid oxide fuel cells [J]. Solid State Ionics,1992,57:295-302.
[199] Z. P. Shao, S. M. Haile, A high-performance cathode for the next generation of solid-oxidefuel cells [J]. Nature,2004,431:170-173.
[200] W. X. Zhu, Z. Lu, S. Y. Li, et al., Study on Ba0.5Sr0.5Co0.8Fe0.2O3-δ-Sm0.5Sr0.5CoO3-δcomposite cathode materials for IT-SOFCs[J]. J. Alloy Compd,2008,465:274-279.
[201]朱文霞. Ba0.5Sr0.5Co0.8Fe0.2O3-δ基中温燃料电池复合阴极性能研究[D].哈尔滨工业大学.2007.
[202] Z. P. Shao, W. S. Yang, Y. Cong, H. Dong, J. H. Tong, G. X. Xiong. Investigation of thepermeation behavior and stability of a Ba0.5Sr0.5Co0.8Fe0.2O3-δoxygen memhrane [J]. J.Memhr. Sci,2000,172:177-188.
[2]衣宝廉.燃料电池-高效、环境友好的发电方式[M].北京：化学工业出版社,2000.
[3]衣宝廉.燃料电池-原理技术运用[M].北京：化学工业出版社,2003.
[4]石井弘毅,白彦华,杨晓辉译.图说燃料电池的原理与应用[M].北京：科学出版社,2003.
[5]毛宗强.燃料电池[M].北京：化学工业出版社,2005.
[6]毛晓峰,由宏新,阿布里提·阿布都拉,丁信伟.电解质支撑固体氧化物燃料电池发电性能模拟[J].天然气工业,2004,24(6):107-111.
[7] P. Aguiar, C. S. Adjiman, N. P. Brandon. Anode-supported intermediate temperature directinternal reforming solid oxide fuel cell Ⅱ.Model based dynamic performance and control [J].J. Power. Sources,2005,147:136-147.
[8]孙红,郭烈锦,刘洪谭等. PEM燃料电池中质子交换膜内水和质子的迁移特性[J].化工学报,2005,56(6):1081-1085.
[9] J. Peng, J. Y. Shin, T. W. Song. Transient response of high-temperature PEM fuel cell [J]. J.Power. Sources,2008,179:220-231.
[10]马利忠,申双林,贾斐,刘洪潭,郭烈锦. PEM燃料电池内水分布和温度分布的数值模拟[J].工程热物理学报,2010,31(12):2051-2056.
[11]周开利,康耀红编.神经网络模型及其MATLAB仿真程序设计[M].清华大学出版社,2006.
[12]靳蕃.神经网络智能基本原理、方法[M].西南交通大学出版社,2000.
[13] J. Arriagada, P. Olausson, A. Selimovic, Artificial neural network simulator for SOFCperformance prediction [J]. J. Power Sources,2002,112:54-60.
[14] X. J. Wu, X. J. Zhu, G. Y. Cao, et al. Modeling a SOFC stack based on GA-RBF neuralnetworks identification [J]. J. Power Sources,2007,167:145-150.
[15] S. Jemei, D. Hissel, M. C. Pera, et al. On-board fuel cell power supply modeling on the basisof neural network methodology [J]. J. Power Sources,2003,124(2):479-486.
[16] W. Li, X. J. Zhu, Z. J. Mo. Estimation of equivalent internal-resistance of PEM fuel cellusing artificial neural networks [J]. J. Cent. South. Univ. T,2007,14(5):690-695.
[17]江船.燃料电池[M].北京：国防工业出版社,1983.
[18]兰泽全,曹欣玉,周俊虎,朱建新,岑可法.质子交换膜燃料电池及其在电动车上应用的现状[J].能源工程,2002,3:17-20.
[19]李瑛,王林山.燃料电池[M].北京：冶金工业出版社，2000.
[20] S. Thomas, M. Zalbowitz, Fuel Cells-Green Power [M]. Los Alamos National Laboratory,1999.
[21]杨辉,卢文庆.应用电化学[M].北京:科学技术出版社,2001.
[22]黄镇江.燃料电池及其应用[M].北京:电子工业出版社，2005.
[23] EG&G Technical Services, Fuel Cell Handbook (Seventh Edition)[M].2004.
[24] N. Q. Minh, Ceramic Fuel Cells [J]. J. Am. Ceram Soc.,1993,76(3):563-588.
[25]赵群,张翔,李辉.基于燃料电池技术的新能源发展论述[J].机械,2007,7:1-5.
[26]刘雁.燃料电池-人类未来的能源终极解决方案[J].资源与人居环境,2007,5:28-31.
[27]邓乃扬,田英杰.数据挖掘中的新方法-支持向量机[M].北京:科学出版社,2004.
[28] V. N. Cristianini, J. Shawe-Tayor, An introduction to support vector machines and otherkernel-based learning methods [M]. Publishing House of Electronics Industry,2004.
[29] T. M. Mitchell, Machine Learning [M]. New York: McGraw-Hill Companies, Inc.,1997.
[30] V. N. Vapnik, The nature of statistical learning theory [M]. Berlin: Springer,1995.
[31] K. Veropoulos, C. Campbell, N. Cristianini, Controlling the sensitivity of support vectormachines [C]. Proceedings of the International Joint Conference on AI,1999:55-60.
[32] Z. Lu, J. Sun, Non-Mercer hybrid kernel for linear programming support vector regression innonlinear systems identification [J]. App. Soft. Comput,2009,9(1):94-99.
[33] G. Camps-Valls, L. Bruzzone, J. L. Rojo-Alvarez, F. Melgani, Robust support vectorregression for biophysical variable estimation from remotely sensed images [J].2006, IEEEGeoscience and Remote Sensing LettersS,3(3):339-343.
[34] K. W. Lau, Q. H. Wu, Local prediction of non-linear time series using support vectorregression [J]. Pattern Recog,2008,41(5):1539-1547.
[35] B. Schǒlkopf, A. J. Smola, Learning with kernels [M].1st edition. London: The MIT press,2002.
[36] C. Cortes, V. N. Vapnik, Support Vector Networks [M]. USA: Machine Learning,1995.
[37] J. A. K. Suykens, Support Vector Machines: A nonlinear modeling and control perspective [J].Eur. J. Control., Special Issue on Fundamental Issues in Control,2001,7(2-3):311-327.
[38] J. Kennedy, R. C. Eberhart, Particle swarm optimization[C]. In: IEEE Service Center ed.IEEE International Conference on Neural Networks, Perth, Australia,1995, Piscataway:IEEE Press,1995:1942-1948.
[39] R. Ebethart, J. Kennedy, A new optimizer using particle swarm theory[C], In: Proceeding ofthe6thInternational Symposium on Micro Machine and Human Science, NJ: IEEECS,1995:39-43.
[40] X. Hu, R. C. Eberhart, Adaptived Particle Swarm Optimization: Detection and Response toDynamic Systems[C]. In: The2002IEEE Congress on Evolutionary Computation.Piscataway, NJ: IEEE Press,2002,1666-1670.
[41] J. A. Nelder, A. Mead, A simple method for function minimization [J]. Comput J,1965,7:308-313.
[42] Y. H. Shi, R. Eberhart, A modified particle swarm optimizer [J]. IEEE InternationalConference on Evolutionary Computation,1998,69-73.
[43] J. X. Hu, Y. H. Shi, R. C. Eberhart, Recent advance in particle swarm, Proceedings of the2004Congress on Evolutionary Computation, Piscataway[J], NJ: IEEE Press,2004,90-97.
[44] R. C. Eberhart, Y. H. Shi, Special Issue on Particle Swarm Optimization [J], IEEETransactions on Evolutionary Computation,2004,8(3):201-203.
[45] R. C. Eberhart, X. H. Hu, Human tremor analysis using particle swarm optimization [J].Proceedings of the IEEE Congress on Evolutionary Computation, Piscataway,1999,1927-1930.
[46] M. P. Wachowiak, R. Smolikova, Y. Zheng et al, An Approach to Multimodal BiomedicalImage Registration Utilizing Particle Swarm Optimization [J]. IEEE Transactions onEvolutionary Computation,2004,8(3):289-301.
[47] T. Souda, A. Silva, A. Neves, Particle Swarm based Data Mining Algorithms forclassification task [J]. Parallel. Comput,2004,30:767-783.
[48] D. W. Merwe, A. P. Engelbrecht, Data Clustering using Particle Swarm Optimization. In:IEEE Congress on Evolutionary Computation [J]. Piscataway, NJ: IEEE Service Center,2003,215-220.
[49] H. Yoshida, K. Kawata, Y. Fukuyama et al., A Particle Swarm Optimization for ReactivePower and Voltage Control Considering Voltage Stability [J]. In: Proc of the InternationalConference on Intelligent System Application to Power Systems. Rio de Janeiro, Brazil,1999,1l7-121.
[50] Y. Liu, H. Zhang, Particle swarm optimization for fault state power supply reliabilityenhancement [J]. In: Proc of the IEEE International Conference on Intelligent SystemsApplication to Power Systems. Budapest, Hungary,2001,172-176.
[51] T. Ray, K. M. Liew, A swarm with an effective information sharing mechanism forunconstrained and constrained single objective optimization problems [J]. Proc IEEE Confon Evolutionary Computation, Seoul,2001,75-80.
[52] G. Ciuprina, D. Ioan, I. Munteanu, Use of intelligent-particle swarm optimization inelectromagnetics [J]. IEEE transactions on magnetics,2002,38(2):1037-1040.
[53] M. A. Abido, Optimal power flow using particle swarm optimization [J], Int. J. Elec Power,2002,24,563-571.
[54] R. Eberhart, Y. H. Shi, Tracking and optimizing dynamic systems with particle swarms [J],Proc IEEE Int Conf on Evolutionary Computation, Hawaii,2001,94-100.
[55] K. Parsopoulos, M. Vrahatis, Particle swarm optimization in noisy and continuouslychanging environments [J]. Artificial Intelligence and Soft Computing,2001,289-294.
[56] M. Omran, A. Salman and A.P. Engebrecht, Image Classification Using Particle SwarmOptimization [J]. In: The4th Asia-Pacific Conference on Simulated Evolution and Learning.Piscataway, NJ: IEEE Press,2002,370-374.
[57] L. N. Kassabalidis, M. A. EI-Sharkawi, R. J. Marks et al., Dynamic security borderidentification using enhanced particle swarm optimization [J], IEEE Transactions on PowerSystems,2002,17(3):723-729.
[58] R. C. Eberhart, Y. H. Shi, Particle swarm optimization: Developments, applications andresources [J]. In Proceedings of the2001Congress on Evolutionary Computation,2001,81-86.
[59] S. Doctor, G. Venayagamoorthy, Unmanned vehicle navigation using swarm intelligence [J].In: The2004International Conference on Intelligent Sensing and Information Processing.Piscataway, NJ: IEEE Press,249-253.
[60] L. Messerschmidt, P. E. Engelbrecht, Learning to play games using a PSO-based competitivelearning approach [J], IEEE Transactions on Evolutionary Computation,2004,8(3):280-288.
[61] A. Lunts, V. Brailovskiy, Evaluation of attributes obtained in statistical decision rules [J],Engineering Cybenretics,1967,3(l):98-109.
[62] V. Vapnik, O. Chapelle, Boundson, Error expectation for support vector machine [J].Advances in Large Margin Classifiers, Cambridge, M A: MIT Press,2000:5-26.
[63] T. Jaakkola, D. Haussler, Probabilistic kernel regression models [J]. Proceedings of the7thWorkshop on AI and Statistics, San Francisco,1999:26-34.
[64] G. Wahba, Y. Lin, H. Zhang, Generalized approximate cross-validation for support vectormachines: Another way to look at margin-like quantities [J]. Advances in Large MargeClassifiers, Cambridge, M A: MIT Press,2000:397-309.
[65] T. Joachims, Estimating the generalization performance of a SVM efficiently [J]. In:Proceedings of the17th International Conference on Machine Leaning, Morgan Kaufman,2000,431-438.
[66] R. Kohavi, A study of cross-validation and bootstrap for accuracy estimation and modelselection [J]. Proceedings of the Fourteenth International Joint Conference on ArtificialIntelligence,1995,2(12):1137-1143.
[67] J. S. Chang, Y. F. Luo, K. Y. Su, GPSM: a generalized probabilistic semantic model forambiguity resolution [C]. In Proceedings of the30th Annual Meeting on Association forComputational Linguistics (Newark, Delaware, June28-July02,1992) Annual Meeting ofthe ACL Association for Computational Linguistics, Morristown, NJ,177-184.
[68] A. R. Ahmad, M. Khalid, R. Yusof, Kernel methods and support vector machines forhandwriting recognition[C]. Student Conference on Research and Development, Shah, Alam,Selangor,2002:309-312.
[69] A. V. Anghelescu, I. B. Muchnik, Combinatorial PCA and SVM methods for feature selectionin learning classifications (Applications to text categorization)[J], International Conferenceon Integration of Knowledge Intensive Multi-Agent Systems,2003,491-496.
[70] V. Wan, W. M Campbell, Support vector machines for speaker verification and identification[J]. Proceedings of IEEE Signal Processing Society Workshop on Neural Networks for SignalProcessing,2000,2(2):775-784.
[71] W. M. Campbell, A SVM/HMM system for speaker recognition [J]. In Proceedings of IEEEInternational Conference on Acoustics, Speech, and Signal Processing,2003,2: II-209-212.
[72] A. Tefas, C. Kotropoulos, I. Pitas, Using support vector machines to enhance theperformance of elastic graph matching for frontal face authentication [J]. IEEE Transactionson Pattern Analysis and Machine Intelligence,2001,23(7):735-746.
[73] K. H. Lee, Y. W. Chung, H. Byun, SVM-based face verification with feature set of small size[J]. Electronics Letters,2002,38(15):787-789.
[74] H. Sahbi, D. Geman, N. Boujemaa. Face detection using coarse-to-fine support vectorclassifiers [J]. Proceeding of the2002International Conference on Image Processing,2002,3:925-928.
[75] X. Dihua, L. S. Whan, Face detection and facial feature extraction using support vectormachines [J]. Proceedings of16th International Conference on Pattern Recognition,2002,4(4):209-212.
[76] K. Lee, H. Byun, A new face authentication system for memory-constrained devices [J].IEEE Transactions on Consumer Electronics,2003,49(4):1214-1222.
[77] Y. H. Liu, Y. T. Chen, Face recognition using total margin-based adaptive fuzzy supportvector machines [J]. IEEE Transactions on Neural Networks,2007,18(l):178-192.
[78] G. D. Guo, A. K. Jain, W. Y. Ma, et al., Learning similarity measure for natural imageretrieval with relevance feedback [J]. IEEE Transactions on Neural Networks,2002,13(4):811-820.
[79] F. Melgani, L. Bruzzone, Classification of hyperspectral remote sensing Images with supportvector machines [J]. IEEE Transactions on Geoscience and Remote Sensing,2004,42(8):1778-1790.
[80] Y. Bazi, F. Melgani, Toward an optimal SVM classification system for hyperspectral remotesensing images [J]. IEEE Transactions on Geoscience and Remote Sensing,2006,44(11):3452-3461.
[81] J. Li, N. Allinson, D. Tao, et al., Multitraining support vector machine for image retrieval[J],IEEE Transactions on Image Processing,2006,15(11):3597-3601.
[82] M. M. Chi, L. Bruzzone, Semisupervised classification of hyperspectral images by SVMsoptimized in the primal. Transactions on Geoscience and Remote Sensingm [J],2007,45(6):1870-1880.
[83] W. Liu, P. Shen, Y. Qu et al., Fast algorithm of support vector machines in lung cancerdiagnosis [J]. Proceedings of International Workshop on Medical Imaging and AugmentedReality, Hong Kong, China,2001:188-192.
[84] P. K. N. Bhanu, A. G. Ramakrishnan, S. Suresh et al., Fetal lung maturity analysis usingultrasound image features [J]. IEEE Transactions on Information Technology in Biomedicine,2002,6(1):38-45.
[85] S. Osowski, L. T. Hoai, T. Markiewicz, Support vector machine-based expert system forreliable heartbeat recognition [J]. IEEE Transactions on Biomedical Engineering,2004,51(4):582-589.
[86] I. E. Naqa, Y. Y. Yang, A support vector machine approach for detection ofMicro-calcifications[J], IEEE Transactions on Medical Imaging,2002,21(12):1552-1563.
[87] W. H. Land, L. Wong, D. W. Mckee, et al., Breast cancer computer aided diagnosis (CAD)using a recently developed SVM/GRNN Oracle hybrid [J]. IEEE International Conference onSystems, Man and Cybernetics,2003,5:4705-4711.
[88] C. Batur, L. Zhou, C. C. Chan. Support vector machines for fault detection [J]. Proceedingsof the41stIEEE Conference on Decision and Control,2002,2:1355-1356.
[89] M. Rychetsky, S. Ortmann, M. Glesner, Support Vector Approaches for Engine KnockDetection [J]. International Joint Conference on Neural Networks,1999,2:969-974.
[90] S. J. Hong, S. M. Weiss, Advance in predictive models for data mining [J]. Pattern RecognLett,2001,22:55-61.
[91] S. Chen, L. Hanzo. Block-adaptive kernel-based CDMA multiuser detection [J]. IEEEInternational Conference on Communications,2002,2:682-686.
[92] A. Kuh, Adaptive kernel methods for CDMA systems [J]. Proceedings of IJCNN'01International Joint Conference on Neural Networks,2001,4:2404-2409.
[93] J. Gao, W. Shi, J. Tan, et al., Support vector machines based approach for fault diagnosis ofvalves in reciprocating pumps [J]. Proceeding of IEEE Conference on Electrical andComputer Engineering, Canadian,2002,3:1622-1627.
[94] H. Drucker, B. Shahrary, C. G. David, Support vector machine: Relevance feedback andinformation retrieval [J]. Inform. Process and manag,2002,38:305-323.
[95] K. R. Muller, A. J. Smola, Prediction time series with support vector machines [J],Proceeding of the International Conference on Artificial Neural Networks, Lausanne,Switzerland, Springer,1997:999-1004.
[96] J. Sjoberg, Q. Zhang, L. Ljung, Nonlinear black-box modeling in system identification: aunified overview [J]. Automatica,1995,31(12):1691-1724.
[97] M. Brown, S.R. Gunn, Empirical data modelling algorithms: additive spline models andsupport vector machines [J]. Proc of UKACC International Conference on Control, Swansea,UK,1998,1:709-714.
[98] F. E. H. Tay, L. Cau, Application of support vector machines in financial time seriesforecasting [J]. Omega,2001,29:309-317.
[99] S. Mukherjee, E. Osuna, F. Girosi, Nonlinear prediction of chaotic time series using a supportvector machine [J]. Neural Networks for Signal Processing VII-Proceeding of the1997IEEEWorkshop, New York,1997:511520.
[100] D. Mao, Z. Y. Zeng, C. Wang, W. H. Lin, Support vector machine with PSO algorithm forsoil erosion evaluation and prediction [J]. Proceedings of Third International Conference onNatural Computation2007,1:656-660.
[101] K. B. Parter, Solid polymer fuel cell developments at ballard [J]. J. power sources,1992,37:181-188.
[102] P. Rodatz, F. Buchi, F Onder et al., Operational aspects of a large PEFC stack under practicalconditions [J]. J. powder source,2004,128:208-2.
[103] Angelo, U. Dufour, Fuel Cells: A new contributor to stationery power [J]. J. Power Sources,1998,71:15-19.
[104] M. D. Francesco, E. Arato, Star-up analysis for automotive PEMFC systems [J]. J. PowerSources,2002,108:41-52.
[105] M. Zobel, A. Polymer, Electrolyte membrane fuel Ccell system for powering portablecomputers [J]. J. Power Sources,2003,122: l-8.
[106] T. F. Fuller, J. Newman, Water and thermal management in solid-polymer-electrolyte fuelcells [J]. J. Electrochem. Soc,1993,140(5):1218-1224.
[107] S. H. Chan, W. A. Tun, Catalyst layer models for proton exchange membrane fuel cells [J].Chem. Eng. Technol,2001,24(1):51-57.
[108] A. Rowel, X. G. L, Mathematical modeling of proton exchange membrane fuel cells [J]. J.Power Sources,2001,102:82-96.
[109] N. Djilali, D. M. Lu. Influence of heat transfer on gas and water transport in fuel cells [J]. Int.J. Therm. Sci,2002,41(1):29-40.
[110] T. V. Nguyen, R. E. White, A water and heat management model for proton exchangemembrane fuel cells[J]. J. Electrochem. Soc,1993,140(8):2178-2186.
[111] J. S. Yi, T. V. Nguyen, An along-the-channel model for protone exchange membrane fuelcells [J]. J. Electrochem. Soc,1998,145(4):1149-1159.
[112] V. Gurau, H. T. Liu, S. Kakac, Two-dimensional model for proton exchange membrane fuelcells [J]. Aiche J,1998,44(11):2410.
[113] W. He, J. S. Yi, T. V. Nguyen, Two-phase flow model of the cathode of PEM fuel cells usinginterdigitated flow fields [J]. Aiche J,2000,46(10):2053-2064.
[114]周孝锋,陈晓宁,徐晔,张云生蛇形流场PEMFC的三维多物理场数值模拟[J].电池工业,2010,15(6):367-371.
[115] A. Su, Y. M. Femg, J. C. Shih, CFD investigating the effects of different operating conditionson the performance and the characteristics of a high-temperature PEMFC [J]. Energy,2010,35:16-27.
[116] G. L. Hu, J. R. Fan, A three-dimensional, multicomponent, two-phase model for a protonexchange membrane fuel cell with straight channels [J].·Energ. Fuel,2006,20(2):738-747.
[117]杜春雨,史鹏飞.三维质子交换膜燃料电池的完整模型[J].哈尔滨工业大学学报,2006,38(9):1511-1514.
[118]蔡年生.质子交换膜在燃料电池中的应用[J].膜科学与技术,1996,16(4):1-6.
[119] A. J. Appleby, F. R. Foulkes, Fuel cell handbook [M]. Van Nostrand Reinhold, New York,1989.
[120] M. A. Hickner, H. Ghassemi, Y. S. Kim, et al., Alternative polymer systems for protonexchange membranes (PEMs)[J]. Chemical Reviews,2004,104,4587-4612.
[121] M. Wakizoe, O. A. Velev, S. Srinivasan, Analysis of proton exchange membrane fuel cellperformance with alternate membranes [J]. Electrochimica Acta1995,40(3):335-344.
[122] M. Yoshitake, M. Tamura, N. Yoshida, et al., Studies of perfluorinated ion exchangemembranes for polymer elecrtolyte fuel cells [J]. Denki Kagaku,1996,64(6):727-736.
[123] N. Yoshida, T. Ishisaki, A. Watanabe, et al., Characterization of flemion membranes forPEFC [J]. Elecctrochimica Acta,1998,43(24):3749-3754.
[124] O. Savadogo, Emerging membranes for electrochemical sysytems:(I) solid polymerelectrolyte membranes for fuel cell syytems [J]. J. New Mat for Electrochem Systems,1998,1:47-66.
[125] M. Yoshitake, M. Tamura, N. Yoshida, et al., Studies of perfluorinated ion exehangemembranes for polymer electrolyte fuel cells. Denki Kagaku [J].1996,64(6):727-736.
[126] Y. Yin, J. Fang, H. Kita, et al., Novel sulfoalkoxylated polyimide membrane for polymerelectrolyte fuel cells[J]. Chemstry Letters,2003,32(4):328-329.
[127] R. A. Komoroski, K. A. Mauritz, A sodium nuclear magnetic resonance study of ionicmobility and contaction pairing in a perfluorosulfonate ionomer[J]. J. Am. Chem. Sco.,1978,100:7487-7489.
[128] W. Y. Hsu, T. D. Girerke, Ion transport and clustering in Nafion perfluorinated membranes[J]. J. Membrance Sci.,1983,13:307-326.
[129] P. C. Lee, D. Meisel, Luminescence quenching in the cluster network of perfluorosulfonatemembrane [J]. J. Am. Chem. Soc.1980,102:5477-5481.
[130] T. E. Springer, T. A. Zawodzinski, S. Gottesfeld, Polymer electrolyte fuel cell model [J]. J.Electrochem Soc1991,138:2334-2342.
[131] U. Pasaogullari, C. Y. Wang, Two-phase transport and the role of micro-porous layer inpolymer electrolyte fuel cells [J]. Electrochim Acta,2004,49:4359-4369.
[132] D. P. Wilkinson, H. H. Voss, K. Prater, Water management and stack design for solidpolymer fuel cells [J]. J. Power Sources,1994,49:117-127.
[133] F. A. Uribe, T. E. Springer, S. Gottesfeld, Microelectrode study of oxygen reduction at theplatinum/recast-nafion film interface [J]. J. Electrochem. Soc.,1992,139:765-773.
[134] R. Mosdale, S. Srinivasan, Analysis of performance and of water and thermal management inproton-exchange membrane fuel cells [J]. Electrochimica Acta,1995,40(4):413-421.
[135] F. Barbir, H. Gorgun, X. Wang, Relationship between pressure drop and cell resistance as adiagnostic tool for PEM fuel cells (Short communication)[J]. J. Power Sources,2005,141:96-101.
[136] T. A. Zawodzinski, C. Derouin, S. Radzinski, Water up take by and transport through Nafionl17membranes [J]. Electrochem. Soc.,1993,140:1041-1047.
[137] T. E. Springer, M. S. Wilson, S. Gottefsfeld, Modeling and experimental diagnositcs inpolymer electrolyte fuel cells [J]. J. Electrochem Soc,1993,140:3513-3526.
[138] P. C. Rieke, N. E. Vanderborgh, Temperature dependence of water content and protonconductivity in polyperfluorosulfonic acid membranes [J]. J. Membrane Sci,1987,32(2-3):313-328.
[139] Y. Sone, P. Ekdunge, D. Simonsson, Proton conductivity of Nafion117as measured by afour-electrode AC impedance method [J] J. Electrochem Soc,1996,1434:1254-1259.
[140]杜文朝,张立炎,全书海.基于内阻测试的质子交换膜含水量软测量研究[J].2009,26（2）:14-17.
[141] W. L. Yu, S. J. Wu, S. W Shiah, Parametric analysis of the proton exchange membrane fuelcell performance using design of experiments [J] Int. J. Hydrogen energ.,2008,33,2311-2322.
[142] V. Neburchilov, J. Martin, H. J. Wan, J. J. Zhang, A review of polymer electrolyte membranesfor direct methanol fuel cells [J]. J. Power Sources,2007,169(2):221-238.
[143] N. T. Nguyen, S. H. Chan, Micromachined polymer electrolyte membrane and directmethanol fuel cells-A review [J]. J. Micro. Microeng.,2006,16(4):1-12.
[144] A. Heinzel, V. M. Barragan, A review of the state-of-the-art of the methanol crossover indirect methanol fuel cells [J]. J. Power Sources,1999,84(l):70-74.
[145] S. Wasmus, A. Kuver, Methanol oxidation and direct methanol fuel cells: a selective review[J]. J. Elec. Chem.,1999,461(l-2):14-31.
[146] S. Surampudi, Aqueous liquid feed organic fuel cell using solid polymer electrolytemembrane [P] USP:5599638,1997202204:
[147] R. G. Hockaday, Fuel cell [P] USP:4673624,1987206216:
[148] J. Pavio, J. Hallmark, J. Bostaph, et al., Developing micro-fuel cells for wirelesscommunications [J]. Fuel Cells Bulletin,2002,2002(4):8-11.
[149] M. Waidhas, W. Drenckhahn, W. Preidel, et al., Direct-fuelled fuel cell [J]. J. Power Sources,1996,61(1):91-97.
[150] M. Baldauf, W. Preidel, Status of the development of a direct methanol fuel cell [J] J. PowerSources,1999,84(2):161-166.
[151] D. Buttin, M. Dupont, M. Straumann, et al., Development and operation of a150W air-feeddirect methanol fuel cell stack [J]. J. Appl. Electrochem.,2001,31:275-279
[152] P. Piela, R. Fields, P. Zelenay, Electrochemical impedance spectroscopy for direct methanolfuel cell diagnostics [J]. J. Eletroehem Soc,2006,153(10): A1902-A1913.
[153] K. Scott, W. Taama, J. Cruickshank, Performance and modeling of a direct methanol solidpolymer electrolyte fuel cell [J]. J. Power Sources,1997,65:159-171.
[154] K. Scott, P. Argyropoulos, K. Sundamacher, A model for the liquid feed direct methanol fuelcell [J]. J. Electroanal. Chem.,1999,477:97-110.
[155]康明艳,土红星,许莉,土宇新.直接甲醇燃料电池中多物理量分布的一维模拟[J].电源技术,2006,30(11)：890-894.
[156]胡军,衣宝廉.常规流场质子交换膜燃料电池阴极二维两相流模型[J].化工学报,2004,55:967-973.
[157] A. A. Kulikovsky, Two-dimensional numerical modeling of a direct methanol fuel cell [J]. J.Appl. Electrochem.,2000,30:1005-1014.
[158]胡桂林,樊建人,岑可法.直接液体甲醇燃料电池的数学模型[J].动力工程,2003,23(3):2465-2469.
[159]苗政,邹金强,何雅玲,刘增.空气自呼吸式直接甲醇燃料电池传输特性数值研究[J].化工学报,2011,62(s1):66-71
[160]胡桂林,刘永江.质子交换膜燃料电池内传递现象的数值模拟[J].工程热物理学报,2004,25:828-830.
[161] A. A. Kulikovsky, Numerieal simulation of a new operational regime for a polymer eletrolytefuel cell [J] Electrochem Commun,2001,3:460-466.
[162]王胜军,陈吉,邹志青.基于MEMS技术的微型DMFC阳极流场结构的设计与研究[J].功能材料与器件学报,2011,17(5):476-480.
[163]王晓红,谢克文,姜英琪,钟凌燕,刘理天.硅基微型直接甲醇燃料电池的研究[J].半导体学报,2005,26(7):1437-1441.
[164] S. D. Ou, L. E. K. Achenie, A hybrid neural network model for PEM fuel cells [J]. J PowerSources,2005,140:319-330.
[165] Y. F. Yi, A. D. Ra, J. Brouwer et al., Analysis and optimization of a solid oxide fuel cell andintercooled gas turbine (SOFC–ICGT) hybrid cycle [J]. J. power sources,2004,132:77-85.
[166] T. Kawada, N. Sakai, H. Yakolawa, et al. Characteristics of Slurry-coated Nickel ZirconiaCermet Anodes for Solid Oxide Fuel Cell [J]. Electrochem Soc.,1990,137:3042-3049.
[167] B. C. H. Steele, Survey of materials selection for ceramic fuel cells cathodes and anodes [J]Solid State Ionics,1996,86-88:1223-1234.
[168] J. G. Cheng, L. P. Deng, B. Zhang, P. Shi, Properties and microstructure of NIO/SDCmaterials for SOFC anode applications[J]. Rare Metals,2007,26:110-117.
[169] K. C. Wincewicz, J. S. Cooper, Taxonomies of SOFC material and manufacturingalternatives [J]. J. Power Sources,2005,140:280-296.
[170] N. Bonanos, Proton conducting ceramics and their applications [J]. Solid State Ionics,2001,86-88:9-15.
[171] J. Liu, W. Liu, Z. Lv, et al. Study on the properties of YSZ electrolyte made by plaser castingmethod and the applications in solid oxide fuel cells [J]. Solid State Ionics,1999,118:67-72.
[172] S. P. S. Badwal, K. Foger. Materials for solid oxide fuel cells [J] Mater Forum,1997,21:187-224.
[173] S. C. Singhal, Advances in solid oxide fuel cell technology [J]. Solid State Ionics,2000,135:305-313.
[174] W. Z. Zhu, S. C. Deevi, Development of inter connect materials for solid oxide fuel cells [J].Mat. Sci Eng A,2003, A348(l-2):227-243.
[175] S. C. Singhal, Science and technology of solid-oxide fuel cells [J]. MRS Bulletin,2000,25(3):16-21.
[176] J. W. Fergus, Metallic interconnects for solid oxide fuel cells [J]. Mat. Sci. Eng A,2005,397(l-2):271-283.
[177] N. Q. Minh, T. Takahashi, Science and technology of ceramics fuel cells [M]. Elsevierpublishing, Amsterdam, The Netherlands,1995,283-296.
[178] R. Doshi, V. L. Righards, J. D. Carter, et al., Development of Solid-Oxide Fuel Cells ThatOperate at500℃[J]. J. Electrochem Soc.,1999,146:1273-1278.
[179] S. C. Sinhal, Solid oxide fuel cells [J]. Electrochem Soc Interface,2007,16(4):41-44.
[180] W. Bujalskp, C. M. Dikwal, K. Kendall, Cycling of three solid oxide fuel cell types [J]. J.Power Sources,2007,171:96–100.
[181]史翊翔,蔡宁生.固体氧化物燃料电池阴极数学模型与性能分析[J].中国电机工程学报,2006,26(4):82-87.
[182]冯兴强,余晴春.平板式固体氧化物燃料电池的建模与仿真研究[J].微型电脑应用,2010,26(9):7-9.
[183]李晨,史翊翔,蔡宁生.管式固体氧化物燃料电池的数值模拟[J].动力工程,2006,26(5):742-746.
[184]史翊翔,李晨,蔡宁生.管式固体氧化物燃料电池机理模型与性能分析[J].化工学报,2007,58(3):722-727.
[185]李彦,胡桂林,骆仲泱,余春江.固体氧化物燃料电池的三维数值模拟[J].电源技术,2009,133:865-868.
[186]杨国刚,岳丹婷,吕欣荣,袁金良.多孔介质孔隙率对SOFC阳极管道化学/电化学反应与传递过程影响[J].工程热物理学报,2010,35(5):864-866.
[187]罗丹,王关晴,黄雪峰,徐江荣.阳极支撑中温固体氧化物燃料电池的数值模拟[J].杭州电子科技大学学报,2010,30(4):50-54.
[188] S. P. Jlang, J. P. Zhang, X. G. Zhang, et al., A compative investigate of chromium depositionat air electrodes of solid oxide fuel cells [J]. J. Eur Ceram Soc,2002,22(3):361-373.
[189] E. Maguire, B. Gharbage, F. M. B. Marques, et al., Cathode materials for intermediatetemperature SOFCs [J].Solid State Ionies,2000,127(l):329-335.
[190] H. Lou, Y. P. Ge, P. Chen, M. H. Mei, F. T. Ma, G. L. Lu, Preparation, crystal structure andreducibility of K2NiF4type oxides Sm2xSrxNiO4[J]. Mater Chem.,1997,7(10):2097-2101.
[191] S. D. Mark, M. S. Islam, G. W. Watson, F. E. Hancock, Surface structures and defectproperties of pure and doped La2NiO4[J]. J. Mater. Chem.,2001,(11):2597-2602.
[192]李强.类钙钛矿结构中温固体氧化物燃料电池阴极材料的性能研究[D].哈尔滨理工大学,2007.
[193] X. M. Yang, L. T. Luo, H. Zhong. Structure of La2xSrxCoO4±λ(x=0.0-1.0) and their catalyticproperties in the oxidation of CO and C3H8[J]. Applied Catalysis A,2004,272:299-303
[194] H. W. Nie, T. L. Wen, S. R. Wang, Y. S. Wang, U. Guth, V. Vashook. Preparation, thermalexpansion, chemical compatibility, electrical conductivity and polarization of A2-αA’αMO4(A=Pr, Sm; A’=Sr; M=Mn, Ni; α=0.3,0.6) as a new cathode for SOFC [J]. Solid State Ionics,2006,177:1929-1932.
[195]高建峰.中温固体氧化物燃料电池阴极材料及电极过程研究[D].中国科技大学材料科学与工程学院,2003.
[196] R. Maric, S. Ohara, T. Fukui, High-performance Ni-SDC cermet anode forsolid oxide fuelcells at medium operating temperature [J]. Electrochem Solid State Lett,1998,1(5):201-203.
[197] J. Mizusaki, H. Tagawa, K. Tsuneyoshi, et al., Reaction Kinetics and Microstructure of theSOFC Air Electrode Lao.6Cao.4MnO3/YSZ [J]. J. Electrochem Soc.1991,138:1867-1874.
[198] T. Kenjo, M. Nishiya, LaMnO3air cathode containing ZrO2electrolyte or high temperaturesolid oxide fuel cells [J]. Solid State Ionics,1992,57:295-302.
[199] Z. P. Shao, S. M. Haile, A high-performance cathode for the next generation of solid-oxidefuel cells [J]. Nature,2004,431:170-173.
[200] W. X. Zhu, Z. Lu, S. Y. Li, et al., Study on Ba0.5Sr0.5Co0.8Fe0.2O3-δ-Sm0.5Sr0.5CoO3-δcomposite cathode materials for IT-SOFCs[J]. J. Alloy Compd,2008,465:274-279.
[201]朱文霞. Ba0.5Sr0.5Co0.8Fe0.2O3-δ基中温燃料电池复合阴极性能研究[D].哈尔滨工业大学.2007.
[202] Z. P. Shao, W. S. Yang, Y. Cong, H. Dong, J. H. Tong, G. X. Xiong. Investigation of thepermeation behavior and stability of a Ba0.5Sr0.5Co0.8Fe0.2O3-δoxygen memhrane [J]. J.Memhr. Sci,2000,172:177-188.