Analysis of Long-Term and Thermal Cycling Tests for a Commercial Solid Oxide Fuel Cell

Lee, Dustin; Lin, Jingkai; Tsai, Chung-En; Wu, Shaw‐Chii; Cheng, Yung-Neng; Lee, Ruey‐Yi

doi:10.1115/1.4037232

Cited by 6 publications

(4 citation statements)

References 11 publications

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“…Second, in order to monitor the effect of anode degradation on the overall performance of the cell, more than 3700 h for the stability test is needed. Lee et al [127] studied 5000 h of SOFC operation at 700 • C for a commercial stack with 60 thermal cycles (table 1, row 2). Cells are composed of Ni-YSZ anode, YSZ electrolyte and GDC-LSCF cathode.…”

Section: Long-term Degradation Inside the Ni-ysz Electrodementioning

confidence: 99%

Microstructure and long-term stability of Ni–YSZ anode supported fuel cells: a review

et al. 2022

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Nickel-yttria stabilized zirconia (Ni-YSZ) cermet is the most commonly used anode in solid oxide fuel cells (SOFCs). The current article provides an insight into parameters which affect cell performance and stability by reviewing and discussing the related publications in this field. Understanding the parameters which affect the microstructure of Ni-YSZ such as grain size and ratio of Ni to YSZ, volume fraction of porosity, pore size and its distribution, tortuosity factor, characteristic pathway diameter and density of triple phase boundaries (TPBs) is the key to design a fuel cell which shows high electrochemical performance. Lack of stability has been the main barrier to commercialization of SOFC technology. Parameters influencing the degradation of Ni-YSZ supported SOFCs such as Ni migration inside the anode during prolonged operation are discussed. The longest Ni-supported SOFC tests reported so far are examined and the crucial role of chromium poisoning due to interconnects (ICs), stack design and operating conditions in degradation of SOFCs is highlighted. The importance of calcination and milling of YSZ on development of porous structures suitable for Ni infiltration is explained and several methods to improve the electrochemical performance and stability of Ni-YSZ anode supported SOFCs are suggested.

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Section: Long-term Degradation Inside the Ni-ysz Electrodementioning

confidence: 99%

Microstructure and long-term stability of Ni–YSZ anode supported fuel cells: a review

et al. 2022

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“…In this section, the experimental setting and data implementation of the commercial anode-supported cell (ASC) type of the SOFC system were derived in the first. Specifically, the aging effects for the long-term degradation test and thermal cycle test were carried out based on the non-sealed cell test facility, as shown in Figure 1 [26]. The test cell of the SOFC was placed between the fuel and oxidant housings to separate the gases and facilitate electricity generation, and mixed gases burning occurred when the gases escaped the region of the cell.…”

Section: Experiments Setting and Data Implementationmentioning

confidence: 99%

Data-Driven Prognostics of the SOFC System Based on Dynamic Neural Network Models

Cheng

Chang

et al. 2021

Energies

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Prognostics technology is important for the sustainability of solid oxide fuel cell (SOFC) system commercialization, i.e., through failure prevention, reliability assessment, and the remaining useful life (RUL) estimation. To solve SOFC system issues, data-driven prognostics methods based on the dynamic neural network (DNN), one of non-linear models, were investigated in this study. Based on DNN model types, the neural network autoregressive (NNARX) model with external inputs, the neural network autoregressive moving average (NNARMAX) model with external inputs, and the neural network output error (NNOE) were utilized to predict the degradation trend and estimate the RUL. First, the degradation trend prediction was executed to evaluate the correctness of the proposed DNN model structures in the first learning phase. Then, the RUL was estimated on the basis of the degradation trend of the NN models in the second inference phase. The comparison test results show the prediction accuracy of the NNARX model is higher and the RUL estimation can be given within a smaller relative error than the NNARMAX and NNOE models. The evaluation criteria of the root mean square error and mean absolute error of the NNARX model are the smallest among these three models. Therefore, the proposed NNARX model can effectively and precisely provide degradation trend prediction and RUL estimation of the SOFC system.

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“…In this work, the commercial Anode-Supported Cell (ASC) SOFC cells within the size of 10 × 10 cm 2 and a reactive area of 9 × 9 cm 2 conducted [31]. The main compositions of anode, electrolyte and cathode are NiO/YSZ, YSZ and LSC within total 550 µm thickness.…”

Section: Data Description and Test Systemmentioning

confidence: 99%

“…During the long-term testing period, the polarization curve measurement and the AC impedance were recorded within a certain interval. The detail analysis results of experimental had been published in literature [31]. The major fluctuations output voltage data of SOFC system could present the significant effect during operation and generally utilized to evaluate the degree of performance since easily measured.…”

Section: Data Description and Test Systemmentioning

confidence: 99%

Performance Prediction Model of Solid Oxide Fuel Cell System Based on Neural Network Autoregressive with External Input Method

Cheng

Lin

2020

Processes

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An accurate performance prediction model for the solid oxide fuel cell (SOFC) system not only contributes to the realization of the operating condition but also plays a role in long-term prediction performance. Accordingly, a research study has been developed to suitably deal with the time-series model and accurately build the performance prediction model of SOFC system based on neural network autoregressive with external input (NNARX) method. The architecture regressor parameters of the NNARX model were efficiently determined using the Taguchi orthogonal array (OA) method for optimal sets. The identified and evaluated optimal parameter levels were used to conduct an analysis of variance (ANOVA) to prove correctness. Moreover, a series of statistics criteria and multi-step prediction were also employed for investigating the uncertainty of the predicted model and solve the overfitting and under fitting problems; further. These criteria were also used to determine the performance of the proposed model architecture. The predicted results of the current study indicated that the developed optimal model level parameters consistently had the least statistics errors and reduced workload of the trial-and-error processes.

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Analysis of Long-Term and Thermal Cycling Tests for a Commercial Solid Oxide Fuel Cell

Cited by 6 publications

References 11 publications

Microstructure and long-term stability of Ni–YSZ anode supported fuel cells: a review

Microstructure and long-term stability of Ni–YSZ anode supported fuel cells: a review

Data-Driven Prognostics of the SOFC System Based on Dynamic Neural Network Models

Performance Prediction Model of Solid Oxide Fuel Cell System Based on Neural Network Autoregressive with External Input Method

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