Neural network model of 100 W portable PEM fuel cell and experimental verification

Sisworahardjo, N.S.; Yalçınöz, Tankut; El-Sharkh, M.Y.; Alam, Mohammad Saad

doi:10.1016/j.ijhydene.2010.05.124

Cited by 69 publications

(23 citation statements)

References 23 publications

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“…The stacking approach using partial least squares as a combining algorithm obtained the best prediction. In [14], the authors compared an NN model against a dynamic model using three statistical indices to validate their performance: the absolute mean error (AME), the root-mean-square error (RMSE), and the standard deviation error (SDE). The maximum value of the three indices indicated that the NN model is more precise and accurate but has bigger variation in predicting the outputs when compared with a dynamic model.…”

Section: Related Workmentioning

confidence: 99%

PEM Fuel Cell Voltage Neural Control Based on Hydrogen Pressure Regulation

et al. 2019

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Fuel cells are promising devices to transform chemical energy into electricity; their behavior is described by principles of electrochemistry and thermodynamics, which are often difficult to model mathematically. One alternative to overcome this issue is the use of modeling methods based on artificial intelligence techniques. In this paper is proposed a hybrid scheme to model and control fuel cell systems using neural networks. Several feature selection algorithms were tested for dimensionality reduction, aiming to eliminate non-significant variables with respect to the control objective. Principal component analysis (PCA) obtained better results than other algorithms. Based on these variables, an inverse neural network model was developed to emulate and control the fuel cell output voltage under transient conditions. The results showed that fuel cell performance does not only depend on the supply of the reactants. A single neuro-proportional–integral–derivative (neuro-PID) controller is not able to stabilize the output voltage without the support of an inverse model control that includes the impact of the other variables on the fuel cell performance. This practical data-driven approach is reliably able to reduce the cost of the control system by the elimination of non-significant measures.

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Section: Related Workmentioning

confidence: 99%

PEM Fuel Cell Voltage Neural Control Based on Hydrogen Pressure Regulation

et al. 2019

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“…Mathematical modeling [24][25][26][27][28] is now the basic method which makes it possible to analyze systems including fuel cells. It is mainly associated with fairly expensive process of producing cells and high cost of materials used.…”

Section: Working Principles Of Solid Oxide Fuel Cellmentioning

confidence: 99%

Recent key technical barriers in solid oxide fuel cell technology

Milewski¹,

Budzianowski²

2014

Archives of Thermodynamics

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High-temperature solid oxide fuel cells (SOFCs) are considered as suitable components of future large-scale clean and efficient power generation systems. However, at its current stage of development some technical barriers exists which limit SOFC's potential for rapid large-scale deployment. The present article aims at providing solutions to key technical barriers in SOFC technology. The focus is on the solutions addressing thermal resistance, fuel reforming, energy conversion efficiency, materials, design, and fuel utilisation issues.

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“…To meet the demand of this, some researchers have attempted to establish novel PCMFC models. Black-box identification technique such as the artificial neural network (ANN) has been used to derive a PEMFC model from the experimental data [4] [5]. The artificial neural network has the ability to learn and approach the nonlinear function, and has been considered as a powerful computing tool for establishing the mathematical relationship of the nonlinear system based on the input-output data.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Proton Exchange Membrane Fuel Cell Using Support Vector Regression Machine

Tang¹,

Huang²

2015

Proceedings of the 5th International Conference on Information Engineering for Mechanics and Materials

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In this paper, a nonlinear offline model of proton exchange membrane fuel cell (PEMFC) is built by using a support vector regression machine (SVRM) based on particle swarm optimization (PSO) algorithm. During the process of modeling, the PSO aims to optimize the parameters of SVRM. Compared with the artificial neural network (ANN) approach, the prediction results show that the SVRM approach is superior to the conventional ANN in predicting the stack voltage with different hydrogen pressure. The mean absolute percentage error (MAPE) of 36 test samples is 0.73%, such that prediction result was provided by leave-one-out cross validation (LOOCV) test of SVRM. So it is feasible to establish the prediction model of PEMFC system by using SVRM identification based on the PSO.

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Neural network model of 100 W portable PEM fuel cell and experimental verification

Cited by 69 publications

References 23 publications

PEM Fuel Cell Voltage Neural Control Based on Hydrogen Pressure Regulation

PEM Fuel Cell Voltage Neural Control Based on Hydrogen Pressure Regulation

Recent key technical barriers in solid oxide fuel cell technology

Modeling of Proton Exchange Membrane Fuel Cell Using Support Vector Regression Machine

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