| Neural network modeling and control of proton exchange membrane fuel cell |
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| 作者姓名： | 陈跃华 曹广益 朱新坚 |
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| 作者单位： | Department of Automation Shanghai Jiaotong University，Department of Automation，Shanghai Jiaotong University，Department of Automation，Shanghai Jiaotong University，Shanghai 200030，China，Shanghai 200030，China，Shanghai 200030，China |
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| 基金项目： | 国家高技术研究发展计划(863计划) |
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| 摘 要： | A neural network model and fuzzy neural network controller was designed to control the inner impedance of a proton exchange membrane fuel cell (PEMFC) stack. A radial basis function (RBF) neural network model was trained by the input-output data of impedance. A fuzzy neural network controller was designed to control the impedance response. The RBF neural network model was used to test the fuzzy neural network controller. The results show that the RBF model output can imitate actual output well, the maximal error is not beyond 20 m-, the training time is about 1 s by using 20 neurons, and the mean squared errors is 141.9 m-2. The impedance of the PEMFC stack is controlled within the optimum range when the load changes, and the adjustive time is about 3 min.
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| 关 键 词： | 质子交换薄膜 燃烧 功能函数 神经网络 |
| 收稿时间： | 30 April 2006 |
| 修稿时间： | 2006-04-30 |
Neural network modeling and control of proton exchange membrane fuel cell |
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| Chen Yue-hua , Cao Guang-yi and Zhu Xin-jian.Neural network modeling and control of proton exchange membrane fuel cell[J].Journal of Central South University of Technology,2007,14(1):84-87. |
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| Authors: | Chen Yue-hua Cao Guang-yi and Zhu Xin-jian |
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| Affiliation: | Department of Automation, Shanghai Jiaotong University, Shanghai 200030, China |
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| Abstract: | A neural network model and fuzzy neural network controller was designed to control the inner impedance of a proton exchange membrane fuel cell (PEMFC) stack. A radial basis function (RBF) neural network model was trained by the input-output data of impedance. A fuzzy neural network controller was designed to control the impedance response. The RBF neural network model was used to test the fuzzy neural network controller. The results show that the RBF model output can imitate actual output well, the maximal error is not beyond 20 m-, the training time is about 1 s by using 20 neurons, and the mean squared errors is 141.9 m-2. The impedance of the PEMFC stack is controlled within the optimum range when the load changes, and the adjustive time is about 3 min. |
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| Keywords: | proton exchange membrane fuel cell radial basis function neural network fuzzy neural network |
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