Energies

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20 pages, 5636 KiB

Open AccessArticle

Transient Multi-Physics Modeling and Performance Degradation Evaluation of Direct Internal Reforming Solid Oxide Fuel Cell Focusing on Carbon Deposition Effect

by Zheng Li, Guogang Yang, Qiuwan Shen, Shian Li, Hao Wang, Jiadong Liao, Ziheng Jiang and Guoling Zhang

Energies 2023, 16(1), 124; https://doi.org/10.3390/en16010124 - 22 Dec 2022

Viewed by 1224

Abstract

The performance degradation issue caused by carbon deposition has limited the commercial application of natural-gas-fueled solid oxide fuel cells. Most previous corresponding studies are based on thermodynamic equilibrium analyses, while long-term transient evaluation work is lacking. Therefore, a transient multi-physics numerical model is [...] Read more.

The performance degradation issue caused by carbon deposition has limited the commercial application of natural-gas-fueled solid oxide fuel cells. Most previous corresponding studies are based on thermodynamic equilibrium analyses, while long-term transient evaluation work is lacking. Therefore, a transient multi-physics numerical model is developed in present work. The corresponding long-term performance degradation evaluation is then conducted. The results show that, for a direct internal reforming solid oxide fuel cell, the increase in carbon deposition and deterioration of performance degradation were concentrated in the first 180 days of steady−state operation and slowed down at the later stage. The electrode inlet rapidly developed a high concentration of carbon deposition after 180 days of steady−state operation. The deposited carbon deteriorated the gas transport and decayed reaction activity within the porous electrode, eventually inducing a deactivation zone with 0 current density at the inlet. Key measures to inhibit carbon deposition should be implemented within the first 180 days of operation, and the pre-reformed operation of natural gas is encouraged for natural-gas-fueled solid oxide fuel cells. Full article

(This article belongs to the Special Issue Advanced Methodology and Technique for Solid Oxide Fuel Cell (SOFC): Control, Diagnosis, and Evaluation)

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20 pages, 4588 KiB

Open AccessArticle

Data-Driven Voltage Prognostic for Solid Oxide Fuel Cell System Based on Deep Learning

by Mingfei Li, Jiajian Wu, Zhengpeng Chen, Jiangbo Dong, Zhiping Peng, Kai Xiong, Mumin Rao, Chuangting Chen and Xi Li

Energies 2022, 15(17), 6294; https://doi.org/10.3390/en15176294 - 29 Aug 2022

Cited by 4 | Viewed by 1642

Abstract

A solid oxide fuel cell (SOFC) is an innovative power generation system that is green, efficient, and promising for a wide range of applications. The prediction and evaluation of the operation state of a solid oxide fuel cell system is of great significance [...] Read more.

A solid oxide fuel cell (SOFC) is an innovative power generation system that is green, efficient, and promising for a wide range of applications. The prediction and evaluation of the operation state of a solid oxide fuel cell system is of great significance for the stable and long-term operation of the power generation system. Prognostics and Health Management (PHM) technology is widely used to perform preventive and predictive maintenance on equipment. Unlike prediction based on the SOFC mechanistic model, the combination of PHM and deep learning has shown wide application prospects. Therefore, this study first obtains an experimental dataset through short-term degradation experiments of a 1 kW SOFC system, and then proposes an encoder-decoder RNN-based SOFC state prediction model. Based on the experimental dataset, the model can accurately predict the voltage variation of the SOFC system. The prediction results of the four different prediction models developed are compared and analyzed, namely, long short-term memory (LSTM), gated recurrent unit (GRU), encoder–decoder LSTM, and encoder–decoder GRU. The results show that for the SOFC test set, the mean square error of encoder–decoder LSTM and encoder–decoder GRU are 0.015121 and 0.014966, respectively, whereas the corresponding error results of LSTM and GRU are 0.017050 and 0.017456, respectively. The encoder–decoder RNN model displays high prediction precision, which proves that it can improve the accuracy of prediction, which is expected to be combined with control strategies and further help the implementation of PHM in fuel cells. Full article

(This article belongs to the Special Issue Advanced Methodology and Technique for Solid Oxide Fuel Cell (SOFC): Control, Diagnosis, and Evaluation)

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14 pages, 1577 KiB

Open AccessArticle

Cooperative Control of a Steam Reformer Solid Oxide Fuel Cell System for Stable Reformer Operation

by Hongchuan Qin, Zhonghua Deng and Xi Li

Energies 2022, 15(9), 3336; https://doi.org/10.3390/en15093336 - 04 May 2022

Cited by 3 | Viewed by 1759

Abstract

Solid oxide fuel cells (SOFCs) have complex characteristics, including a long time delay, strong thermoelectrical coupling, and multiple constraints. This leads to multiple control objectives, such as efficiently controlling the power output of the stack and considering the temperature constraints of multiple high-temperature [...] Read more.

Solid oxide fuel cells (SOFCs) have complex characteristics, including a long time delay, strong thermoelectrical coupling, and multiple constraints. This leads to multiple control objectives, such as efficiently controlling the power output of the stack and considering the temperature constraints of multiple high-temperature components. Dealing with multiple objectives at the same time brings challenges to the design of SOFC system control. Based on the verified high-precision system model and aiming to achieve fast response, high efficiency, and thermal management, this paper first designs a generalized predictive controller (GPC) to realize the global optimization of the system. Then, through the actual test of the individual reformer, the reformer characteristics are analyzed, the standby controller to control the reformer temperature is designed, and the thermoelectric cooperative controller is constricted with the GPC. The results show that while fast power tracking, high efficiency, and multiple temperature constraints are realized by the controller, the temperature and methane conversion rate (MCR) of the reformer are stably controlled, providing a basis for further practical experiments of the SOFC system. Full article

(This article belongs to the Special Issue Advanced Methodology and Technique for Solid Oxide Fuel Cell (SOFC): Control, Diagnosis, and Evaluation)

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15 pages, 6883 KiB

Open AccessArticle

Data-Driven State Prediction and Analysis of SOFC System Based on Deep Learning Method

by Mumin Rao, Li Wang, Chuangting Chen, Kai Xiong, Mingfei Li, Zhengpeng Chen, Jiangbo Dong, Junli Xu and Xi Li

Energies 2022, 15(9), 3099; https://doi.org/10.3390/en15093099 - 24 Apr 2022

Cited by 10 | Viewed by 1898

Abstract

A solid oxide fuel cell (SOFC) system is a kind of green chemical-energy–electric-energy conversion equipment with broad application prospects. In order to ensure the long-term stable operation of the SOFC power-generation system, prediction and evaluation of the system’s operating state are required. The [...] Read more.

A solid oxide fuel cell (SOFC) system is a kind of green chemical-energy–electric-energy conversion equipment with broad application prospects. In order to ensure the long-term stable operation of the SOFC power-generation system, prediction and evaluation of the system’s operating state are required. The mechanism of the SOFC system has not been fully revealed, and data-driven single-step prediction is of little value for practical applications. The state-prediction problem can be regarded as a time series prediction problem. Therefore, an innovative deep learning model for SOFC system state prediction is proposed in this study. The model uses a two-layer LSTM network structure that supports multiple sequence feature inputs and flexible multi-step prediction outputs, which allows multi-step prediction of system states using SOFC system experimental data. Comparing the proposed model with the traditional ARIMA model and LSTM recursive prediction model, it is shown that the multi-step LSTM prediction model performs better than the ARIMA and LSTM recursive prediction models in terms of two evaluation criteria: root mean square error and mean absolute error. Thus, the proposed multi-step LSTM prediction model can effectively and accurately predict and evaluate the SOFC system’s state. Full article

(This article belongs to the Special Issue Advanced Methodology and Technique for Solid Oxide Fuel Cell (SOFC): Control, Diagnosis, and Evaluation)

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19 pages, 8986 KiB

Open AccessArticle

Research on Application Characteristics of Zirconia-Based High-Temperature NOx Sensors

by Jie Wang, Xi Li, Zhen Wang, Jiangtao Feng, Weixun Lin and Jingxuan Peng

Energies 2022, 15(8), 2919; https://doi.org/10.3390/en15082919 - 15 Apr 2022

Cited by 4 | Viewed by 1968

Abstract

The zirconia solid electrolyte SOFC (solid oxide fuel cell) has the characteristics of oxygen ion conduction function, high-temperature resistance, thermoelectric coupling effect, etc. A NOx sensor based on zirconia solid electrolyte has common characteristics and problems with the SOFC in principle and application. [...] Read more.

The zirconia solid electrolyte SOFC (solid oxide fuel cell) has the characteristics of oxygen ion conduction function, high-temperature resistance, thermoelectric coupling effect, etc. A NOx sensor based on zirconia solid electrolyte has common characteristics and problems with the SOFC in principle and application. The research objective of this paper is to solve the application problems of smart NOx sensors in diesel vehicles or gasoline vehicles. Improvements in the application performance of the NOx sensor can help the NOx emissions of gasoline vehicles or diesel vehicles better meet the requirements of emission regulations. The smart NOx sensor is a regulatory sensor required by vehicles for China’s Phase VI Vehicle Exhaust Emission Regulations or Euro Phase VI Vehicle Exhaust Emission Regulations. The smart NOx sensor is a key sensor device for improving fuel efficiency and reducing pollution. Moreover, its measurement performance includes dynamic immunity to interference, response speed, and measurement accuracy, which are key factors affecting vehicle emissions. This paper focuses on the impact of the physical structure, electrode characteristics, and control strategies of the sensor on its performance during the application. An excellent sensor structure, electrode structure, and control strategy are given based on application analysis and experimental testing. The results show that the application performance of this smart NOx sensor meets the requirements of exhaust aftertreatment systems. Full article

(This article belongs to the Special Issue Advanced Methodology and Technique for Solid Oxide Fuel Cell (SOFC): Control, Diagnosis, and Evaluation)

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11 pages, 2560 KiB

Open AccessArticle

Thermal Stress Simulation and Structure Failure Analyses of Nitrogen–Oxygen Sensors under a Gradual Temperature Field

by Jiangtao Feng, Jiaqi Geng, Hangyu She, Tao Zhang, Bo Chi and Jian Pu

Energies 2022, 15(8), 2799; https://doi.org/10.3390/en15082799 - 11 Apr 2022

Viewed by 1245

Abstract

Nitrogen–oxygen sensors are pivotal for NO_X emission detection, and they have been designed as key components in vehicles’ exhaust systems. However, severe thermal stress concentrations during thermal cycling in the sensors create knotty structural damage issues, which are inevitable during the frequent [...] Read more.

Nitrogen–oxygen sensors are pivotal for NO_X emission detection, and they have been designed as key components in vehicles’ exhaust systems. However, severe thermal stress concentrations during thermal cycling in the sensors create knotty structural damage issues, which are inevitable during the frequent start–stop events of the vehicles. Herein, to illustrate the effect of thermal concentration on a sensor’s structure, we simulated the temperature and stress field of a sensor through finite element analysis. The failure modes of the sensor based on the multilayer structure model were analyzed. Our simulation indicated that the thermal deformation and stress of the sensors increased significantly when the heating temperature in the sensors increased from 200 to 800 °C. High stress regions were located at the joint between the layers and the right angle of the air chamber. These results are consistent with the sensor failure locations that were observed by SEM, and the sensor’s failures mainly manifested in the form of cracks and delamination. The results suggest that both the multilayer interfaces and the shape of the air chamber could be optimized to reduce the thermal stress concentrations of the sensors. It is beneficial to improve the reliability of the sensor under thermal cycling operation. Full article

(This article belongs to the Special Issue Advanced Methodology and Technique for Solid Oxide Fuel Cell (SOFC): Control, Diagnosis, and Evaluation)

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16 pages, 27769 KiB

Open AccessArticle

A Data-Driven Fault Diagnosis Method for Solid Oxide Fuel Cell Systems

by Mingfei Li, Zhengpeng Chen, Jiangbo Dong, Kai Xiong, Chuangting Chen, Mumin Rao, Zhiping Peng, Xi Li and Jingxuan Peng

Energies 2022, 15(7), 2556; https://doi.org/10.3390/en15072556 - 31 Mar 2022

Cited by 5 | Viewed by 1516

Abstract

In this study, a data-driven fault diagnosis method was developed for solid oxide fuel cell (SOFC) systems. First, the complete experimental data was obtained following the design of the SOFC system experiments. Then, principal component analysis (PCA) was performed to reduce the dimensionality [...] Read more.

In this study, a data-driven fault diagnosis method was developed for solid oxide fuel cell (SOFC) systems. First, the complete experimental data was obtained following the design of the SOFC system experiments. Then, principal component analysis (PCA) was performed to reduce the dimensionality of the obtained experimental data. Finally, the fault diagnosis algorithms were designed by support vector machine (SVM) and BP neural network to identify and prevent the reformer carbon deposition and heat exchanger rupture faults, respectively. The research results show that both SVM and BP fault diagnosis algorithms can achieve online fault identification. The PCA + SVM algorithm was compared with the SVM algorithm, BP algorithm, and PCA + BP algorithm, and the results show that the PCA + SVM algorithm is superior in terms of running time and accuracy, the diagnosis accuracy reached more than 99%, and the running time was within 20 s. The corresponding system optimization scheme is also proposed. Full article

(This article belongs to the Special Issue Advanced Methodology and Technique for Solid Oxide Fuel Cell (SOFC): Control, Diagnosis, and Evaluation)

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17 pages, 6542 KiB

Open AccessArticle

Real-Time State of Health Estimation for Solid Oxide Fuel Cells Based on Unscented Kalman Filter

by Yuanwu Xu, Hao Shu, Hongchuan Qin, Xiaolong Wu, Jingxuan Peng, Chang Jiang, Zhiping Xia, Yongan Wang and Xi Li

Energies 2022, 15(7), 2534; https://doi.org/10.3390/en15072534 - 30 Mar 2022

Cited by 7 | Viewed by 1916

Abstract

The evolution of performance degradation has become a major obstacle to the long-life operation of the Solid Oxide Fuel Cell (SOFC) system. The feasibility of employing degradation resistance to assess the State of Health (SOH) is proposed and verified. In addition, a real-time [...] Read more.

The evolution of performance degradation has become a major obstacle to the long-life operation of the Solid Oxide Fuel Cell (SOFC) system. The feasibility of employing degradation resistance to assess the State of Health (SOH) is proposed and verified. In addition, a real-time Unscented Kalman Filter (UKF) based SOH estimation method is further proposed to eliminate the disturbance of calculating the SOH directly utilizing measurement and electric balance model. The results of real-time SOH estimation with an UKF under constant and varying load conditions demonstrate the feasibility and effectiveness of the SOFC performance degradation assessment method. Full article

(This article belongs to the Special Issue Advanced Methodology and Technique for Solid Oxide Fuel Cell (SOFC): Control, Diagnosis, and Evaluation)

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