Performance prediction and power density maximization of a proton exchange membrane fuel cell based on deep belief network

Li, Hongwei; Xu, Boshi; Du, Changqing; Yang, Yue

doi:10.1016/j.jpowsour.2020.228154

Cited by 54 publications

(26 citation statements)

References 44 publications

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“…The value of v and h is assumed to takes only 0 or 1. Thus, when the states of visible layer and hidden layer are given, the RBM energy function can be defined as [33]:…”

Section: Rbmmentioning

confidence: 99%

“…The joint probability distribution between the visible layer and the hidden layer can be expressed as [2,33]:…”

Section: Rbmmentioning

confidence: 99%

“…The value of v and h is assumed to takes only 0 or 1. Thus, when the states of visible layer and hidden layer are given, the RBM energy function can be defined as [33]:

\begin{equation}E(v,h) = - \sum_{i = 1}^m {{a_i}{v_i} - \sum_{j = 1}^n {{b_j}{h_j}} } - \sum_{j = 1}^n {\sum_{i = 1}^m {{w_{ij}}{v_i}{h_j}} } \end{equation}

where

{v_i}

and

{h_j}

denote the i‐ th visible node and the j‐ th hidden node, respectively;

{a_i}

and

{b_j}

represent the offsets of the i‐ th visible node and the j ‐th hidden node, respectively;

{w_{ij}}

represents the weight value between the i ‐th visible node and the j ‐th hidden node.…”

Section: Deep Belief Networkmentioning

confidence: 99%

See 2 more Smart Citations

Type identification and time location of multiple power quality disturbances based on KF‐ML‐aided DBN

Yang

Chen

Tang

et al. 2021

IET Generation Trans & Dist

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Type identification and time location of power quality disturbances (PQDs) is the key to adopting corresponding measures to suppress disturbances. More complex multiple disturbances caused by the overlapping of different micro-grids make it a challenging task. The paper proposes a hybrid approach combing KF-ML (Kalman filter based on maximum likelihood) with deep belief network (DBN) for dealing with PQDs. To be specific, the KF-ML is firstly applied to reduce noise from the original distorted signal, and the innovation sequence obtained by KF-ML can be used to locate starting-ending times of PQDs. Then, the DBN, which fuses feature extraction and classification into a single block, is capable of recognizing the type of PQDs accurately. To verify the effectiveness of the proposed method, 20 classes of PQDs with noise interference are tested, and experiment results show that the detection time of the proposed method is very close to the set time, and the absolute error of time location is less than 0.3 ms. The average classification accuracy at different noise levels reaches about 95%, and is very high even with more disturbances combined. Thus, the proposed method is immune to noise and less affected with more disturbances combined relative to other methods.1 PQDs obviously emerged [3].Multiple power quality disturbances (MPQDs), which are composed of several single disturbances with different type, dif-This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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“…The value of v and h is assumed to takes only 0 or 1. Thus, when the states of visible layer and hidden layer are given, the RBM energy function can be defined as [33]:…”

Section: Rbmmentioning

confidence: 99%

“…The joint probability distribution between the visible layer and the hidden layer can be expressed as [2,33]:…”

Section: Rbmmentioning

confidence: 99%

“…The value of v and h is assumed to takes only 0 or 1. Thus, when the states of visible layer and hidden layer are given, the RBM energy function can be defined as [33]:

\begin{equation}E(v,h) = - \sum_{i = 1}^m {{a_i}{v_i} - \sum_{j = 1}^n {{b_j}{h_j}} } - \sum_{j = 1}^n {\sum_{i = 1}^m {{w_{ij}}{v_i}{h_j}} } \end{equation}

where

{v_i}

and

{h_j}

denote the i‐ th visible node and the j‐ th hidden node, respectively;

{a_i}

and

{b_j}

represent the offsets of the i‐ th visible node and the j ‐th hidden node, respectively;

{w_{ij}}

represents the weight value between the i ‐th visible node and the j ‐th hidden node.…”

Section: Deep Belief Networkmentioning

confidence: 99%

See 1 more Smart Citation

Type identification and time location of multiple power quality disturbances based on KF‐ML‐aided DBN

Yang

Chen

Tang

et al. 2021

IET Generation Trans & Dist

View full text Add to dashboard Cite

show abstract

“…For successful PEMFC's applications in vehicles, an appropriate model is needed to estimate the overall performance of PEMFC under dynamic operations. Once the PEMFC performance is obtained, designers can make targeted adjustments to improve the performance of the fuel cell system in terms of operating parameters 3 . Based on the previous researches, the performance prediction can be mainly classified into two categories: data‐driven and model‐driven methods.…”

Section: Introductionmentioning

confidence: 99%

“…In Reference 17, ANFIS was successfully implemented to predict polarization curves of PEMFC under different operational conditions. Li et al 3 constructed a data‐driven model based on the deep belief network (DBN) to predict PEMFC performance. The prediction result of DBN model was in good agreement with the simulation data from a three‐dimensional PEMFC numerical model.…”

Section: Introductionmentioning

confidence: 99%

Performance prediction of fuel cells using long short‐term memory recurrent neural network

Hou

Tao

et al. 2021

Int J Energy Res

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Performance prediction of proton-exchange membrane fuel cell (PEMFC) under dynamic conditions, especially for vehicle applications, is increasingly become the focus of attention. This article proposes a performance prediction method of PEMFC using long short-term memory (LSTM) recurrent neural network (RNN). In this article, polarization curve (current-voltage curve) and voltage degradation curve (current-time curve) are adopted as the main performance indexes of PEMFC. Both polarization curve prediction and performance degradation prediction of PEMFC can be effectively implemented based on the LSTM method. To investigate the voltage losses law of experimental and predicted results, the paper introduces an empirical equation of polarization curve. The perfect match between the experimental and predicted polarization losses of PEMFC can further validate the prediction performance of LSTM method. The proposed prediction method is validated by the PEMFC polarization curve data obtained from the designed aging experiment of a 4 kW stack operated under dynamic loading cycling situation during 600 hours. Then, LSTM network is compared with traditional RNN and back-propagation neural network (BPNN) to prove its superiority. The minimum values of rootmean-square error (RMSE) and the mean absolute percentage error (MAPE) of LSTM network with different training data are 0.0088 and 0.0101, respectively. All the coefficient of determination (R 2 ) of LSTM model with different training data is over 0.95, which is close to 1.0. The prediction accuracy of LSTM network is higher than that of two other networks. The result indicates that LSTM network outperforms two other networks in PEMFC performance prediction.Hence, the prediction method based on LSTM network is very suitable for PEMFC performance prediction.

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Advancements in Ni‐based Catalysts for Direct Urea Fuel Cells: A Comprehensive Review

Putri,

Syauqi,

Rahmawati

et al. 2024

ChemElectroChem

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The growing demand for sustainable energy sources has spurred significant studies to optimize the potency of fuel cell technology. Direct urea fuel cell (DUFC) has gained attention due to their energy density, eco‐friendliness, and potential applications in power generation using urea from various sources, including wastewater and urine. Efficient electrocatalysts are pivotal in DUFCs, and nickel‐based catalysts, particularly in the form of NiOOH, have demonstrated cost effectiveness, excellent stability in alkaline media, and good activity for urea oxidation reaction. However, low‐density and durability are still the major limitation in the overall DUFC performance. This review provides a comprehensive analysis of the latest development in Ni‐based catalysts, covering synthesis methods, factors influencing the catalytic activity as well as their implications for DUFC performance durability and commercial viability. In addition, another important factor including the use of different oxidant and electrolyte medium is also elaborated. Based on this review, 2D‐3D Ni‐based materials with the addition of other metals and the use of non‐oxide nickel binary compound are predicted to be the future evolution of the effective nickel‐based catalysts.

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Performance prediction and power density maximization of a proton exchange membrane fuel cell based on deep belief network

Cited by 54 publications

References 44 publications

Type identification and time location of multiple power quality disturbances based on KF‐ML‐aided DBN

Type identification and time location of multiple power quality disturbances based on KF‐ML‐aided DBN

Performance prediction of fuel cells using long short‐term memory recurrent neural network

Advancements in Ni‐based Catalysts for Direct Urea Fuel Cells: A Comprehensive Review

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