Each one has its characteristics, and its accuracy depends on the characteristics of the database to be analyzed. In, a grid long short-term memory (G-LSTM) recurrent neural network (RNN) was used to predict the lifetime of fuel cells.ĭimensionality reduction techniques can be classified into two groups: feature selection and feature extraction. Recurrent neural networks were used to develop degradation prognostic models. They concluded that simulators based on ANN are reliably able to predict voltage and temperature behavior, saving time and resources. developed a simulator, based on ANN, to predict the stack voltage and cathode output temperature. Some parameters are difficult to measure, or it is very expensive to measure them, especially in fuel cell stacks. However, the GMDH model had less deviation. Both methods presented high accuracy in predicting the voltage. The system inputs were gas pressure, fuel cell temperature, and input current. Parametric neural network (PNN) and group method of data handling (GMDH) techniques were used to predict and control the output voltage of a PEM fuel cell of 25 W. This difference is mainly due to the selection of the hyperparameters. Their results showed that SVM has better accuracy in predicting the power curve, approximately 99%, whereas ANN reached an accuracy of 97%. proposed a different approach for predicting the performance of an electric bicycle using SVM and ANN. The NN model presented excellent performance in predicting the polarization curves of the stack with R 2 = 0.999 the SVM model exhibited a slightly inferior performance with R 2 = 0.980. In, the performance of an artificial neural network (ANN) and a support vector machine (SVM) in predicting fuel cell output voltage was compared. Different methods have been tested to construct nonlinear empirical models. 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. In, 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 stacking approach using partial least squares as a combining algorithm obtained the best prediction. In, the performance of classical neural network (NN) models and stacked models was compared. The control-oriented black box model obtained was implemented in embedded hardware with limited capacity for memory and processing. In, a methodology was presented for systems identification using NARX (nonlinear autoregressive network with exogenous inputs) and NOE (nonlinear output error) neural networks. This section presents papers related to the modeling and control of PEM fuel cells using artificial intelligence techniques. This practical data-driven approach is reliably able to reduce the cost of the control system by the elimination of non-significant measures. 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. The results showed that fuel cell performance does not only depend on the supply of the reactants. Based on these variables, an inverse neural network model was developed to emulate and control the fuel cell output voltage under transient conditions. Principal component analysis (PCA) obtained better results than other algorithms. Several feature selection algorithms were tested for dimensionality reduction, aiming to eliminate non-significant variables with respect to the control objective. In this paper is proposed a hybrid scheme to model and control fuel cell systems using neural networks. One alternative to overcome this issue is the use of modeling methods based on artificial intelligence techniques. 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.
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