PEM fuel cell voltage-tracking using artificial neural network

Rakhtala, Seyed Mehdi; Ghaderi, Reza; Ranjbar, A.; Fadaeian, T.; Niaki, Ali Nabavi

doi:10.1109/epec.2009.5420935

Cited by 7 publications

(1 citation statement)

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“…In this context, over the last decade, numerous fuel cell models based on neural networks have been proposed. Some examples are described in [6][7]. Most of these models are focused on understanding the effect of various operating, geometrical and material parameters on the fuel cell performance and have been successfully used in the optimization of fuel cell design but they are not suitable for analysis and control of the transient behaviour of fuel cells [4,[8][9] or they are very computational demanding.…”

Section: Introductionmentioning

confidence: 99%

Control of a PEM fuel cell based on maximum power tracking using radial basis function neural networks

Ruiz¹,

Jiménez-Hornero²

2024

RE&PQJ

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Abstract. This article presents the proposal of a two-level control approach for a type of commercial PEM fuel cell. Thus, in the external control level a model based on neural networks of the FC is used together with a tracking algorithm to follow the maximum efficiency points as a function of the oxygen excess and in the internal level, a PI control strategy is used to guarantee the compressor motor voltage that satisfies the oxygen excess ratio demanded. The neural model of the FC response is developed through the steady-state FC response provided by the physical modelling using a multimodel approach. This approach allows a good relation between the computational cost of the training and the performance that the network offers. The performance of the global controller and the tracking algorithm are evaluated for variable load conditions by simulations and conclusions are drawn.

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