### Abstract

This paper proposes a neural network model of a 500-W proton exchange membrane (PEM) fuel cell. The nonlinear autoregressive moving average model of the PEM fuel cell with external inputs is developed using the recurrent neural networks. The data required to train the neural network model is generated by simulating the nonlinear state space model of the 500-W PEM fuel cell. It is shown that the two-layer neural network, with a hyperbolic tangent sigmoid function, as an activation function, in the first layer, and a pure linear function, as an activation function, in the second layer can effectively model the nonlinear dynamics of the PEM fuel cell. After model is trained and validated, it is used to analyze the dynamic behavior of the PEM fuel cell. Finally, the effect of measurement noise on the performance of the neural network model is investigated, and the results are shown.

Original language | English (US) |
---|---|

Article number | 5437311 |

Pages (from-to) | 474-483 |

Number of pages | 10 |

Journal | IEEE Transactions on Energy Conversion |

Volume | 25 |

Issue number | 2 |

DOIs | |

State | Published - Jun 2010 |

### Fingerprint

### Keywords

- Fuel cells
- Modeling
- Recurrent neural networks

### ASJC Scopus subject areas

- Electrical and Electronic Engineering
- Energy Engineering and Power Technology

### Cite this

*IEEE Transactions on Energy Conversion*,

*25*(2), 474-483. [5437311]. https://doi.org/10.1109/TEC.2009.2035691

**Neural network modeling of proton exchange membrane fuel cell.** / Puranik, Sachin V.; Keyhani, Ali; Khorrami, Farshad.

Research output: Contribution to journal › Article

*IEEE Transactions on Energy Conversion*, vol. 25, no. 2, 5437311, pp. 474-483. https://doi.org/10.1109/TEC.2009.2035691

}

TY - JOUR

T1 - Neural network modeling of proton exchange membrane fuel cell

AU - Puranik, Sachin V.

AU - Keyhani, Ali

AU - Khorrami, Farshad

PY - 2010/6

Y1 - 2010/6

N2 - This paper proposes a neural network model of a 500-W proton exchange membrane (PEM) fuel cell. The nonlinear autoregressive moving average model of the PEM fuel cell with external inputs is developed using the recurrent neural networks. The data required to train the neural network model is generated by simulating the nonlinear state space model of the 500-W PEM fuel cell. It is shown that the two-layer neural network, with a hyperbolic tangent sigmoid function, as an activation function, in the first layer, and a pure linear function, as an activation function, in the second layer can effectively model the nonlinear dynamics of the PEM fuel cell. After model is trained and validated, it is used to analyze the dynamic behavior of the PEM fuel cell. Finally, the effect of measurement noise on the performance of the neural network model is investigated, and the results are shown.

AB - This paper proposes a neural network model of a 500-W proton exchange membrane (PEM) fuel cell. The nonlinear autoregressive moving average model of the PEM fuel cell with external inputs is developed using the recurrent neural networks. The data required to train the neural network model is generated by simulating the nonlinear state space model of the 500-W PEM fuel cell. It is shown that the two-layer neural network, with a hyperbolic tangent sigmoid function, as an activation function, in the first layer, and a pure linear function, as an activation function, in the second layer can effectively model the nonlinear dynamics of the PEM fuel cell. After model is trained and validated, it is used to analyze the dynamic behavior of the PEM fuel cell. Finally, the effect of measurement noise on the performance of the neural network model is investigated, and the results are shown.

KW - Fuel cells

KW - Modeling

KW - Recurrent neural networks

UR - http://www.scopus.com/inward/record.url?scp=77952958342&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77952958342&partnerID=8YFLogxK

U2 - 10.1109/TEC.2009.2035691

DO - 10.1109/TEC.2009.2035691

M3 - Article

AN - SCOPUS:77952958342

VL - 25

SP - 474

EP - 483

JO - IEEE Transactions on Energy Conversion

JF - IEEE Transactions on Energy Conversion

SN - 0885-8969

IS - 2

M1 - 5437311

ER -