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

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

doi:10.3390/en15093099

Cited by 14 publications

(3 citation statements)

References 41 publications

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“…LSTM networks were designed to be even better suited than RNNs for classifying, processing, and making predictions based on time series data. In recent years, notable publications revealed that using the LSTM algorithm for the prediction and evaluation of a solid oxide fuel cell (SOFC) system's operating state [137], learning the congestion level of a power distribution system which designates the loading of a distribution feeder [138], and fouling prediction in heat exchangers to avoid inefficiencies and shutdowns [139], provided improved performance compared to the benchmark autoregressive integrated moving average and regression algorithms by between 19.1 and 200% using 629,873, 86,400, and 23,040 real data samples, respectively.…”

Section: Long Short-term Memorymentioning

confidence: 99%

Artificial Intelligence and Machine Learning in Energy Conversion and Management

Mira,

Bugiotti,

Morosuk

2023

Energies

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In the modern era, where the global energy sector is transforming to meet the decarbonization goal, cutting-edge information technology integration, artificial intelligence, and machine learning have emerged to boost energy conversion and management innovations. Incorporating artificial intelligence and machine learning into energy conversion, storage, and distribution fields presents exciting prospects for optimizing energy conversion processes and shaping national and global energy markets. This integration rapidly grows and demonstrates promising advancements and successful practical implementations. This paper comprehensively examines the current state of applying artificial intelligence and machine learning algorithms in energy conversion and management evaluation and optimization tasks. It highlights the latest developments and the most promising algorithms and assesses their merits and drawbacks, encompassing specific applications and relevant scenarios. Furthermore, the authors propose recommendations to emphasize the prioritization of acquiring real-world experimental and simulated data and adopting standardized, explicit reporting in research publications. This review paper includes details on data size, accuracy, error rates achieved, and comparisons of algorithm performance against established benchmarks.

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Section: Long Short-term Memorymentioning

confidence: 99%

Artificial Intelligence and Machine Learning in Energy Conversion and Management

Mira,

Bugiotti,

Morosuk

2023

Energies

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“…Although fuel cell technology has made significant progress in the past few decades, its commercialization process still faces many challenges, one of which is the issue of fuel cells' short lifespan [8][9][10][11][12]. Specifically, fuel cells will experience a gradual decline in performance during long-term operation.…”

Section: Introductionmentioning

confidence: 99%

The Degradation Prediction of Proton Exchange Membrane Fuel Cell Performance Based on a Transformer Model

Meng,

Mei,

Tang

et al. 2024

Energies

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Proton exchange membrane fuel cells have attracted widespread attention due to their cleanliness and high energy density, but the performance degradation during operation greatly limits their commercialization. Therefore, the reliable degradation prediction of fuel cell performance is of great significance. The recovery phenomenon of the reversible voltage loss that occurs during the operation of fuel cells has posed great difficulties for model training and prediction. Moreover, the models may easily and erroneously learn the combined trends in the recovery of reversible voltage loss and performance degradation. To address this issue, this paper employs the Transformer model to predict the performance degradation of fuel cells. By utilizing the unique self-attention structure and masking mechanism of the Transformer model, the signal for the recovery of the reversible voltage loss is adopted as the input for the model to avoid interference from information before voltage recovery on subsequent predictions. Experimental results show that the model has the highest prediction accuracy at various prediction starting points. Meanwhile, it can predict the accelerated performance degradation of fuel cells, which has positive implications for health management.

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“…These perturbation factors pose a challenge for accurately predicting the power output of a SOFC system [24,25]. Currently, in the system environment, the data-driven model of SOFCs often involves gas flow rate at the anode and cathode inlets and outlets, load voltage, and stack temperature [26]. However, there still exist discrepancies between these parameters and the requirements of the entire system.…”

Section: Introductionmentioning

confidence: 99%

A Single-Stack Output Power Prediction Method for High-Power, Multi-Stack SOFC System Requirements

Zhang,

Hu,

Zhao

et al. 2023

Inorganics

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The prediction of stack output power in solid oxide fuel cell (SOFC) systems is a key technology that urgently needs improvement, which will promote SOFC systems towards high-power multi-stack applications. The accuracy of power prediction directly determines the control effect and working condition recognition accuracy of the SOFC system controller. In order to achieve this goal, a genetic algorithm back propagation (GA-BP) neural network is constructed to predict output power in the SOFC system. By testing 40 sets of sample data collected from the experimental platform, it is found that the GA-BP method overcomes the limitation of the traditional back propagation (BP) method—falling into local optima. Further analysis shows that the average relative error of GA-BP has decreased to 1%. The reduction of the relative error improves the accuracy of the prediction results and the average prediction accuracy. Compared with the long short-term memory (LSTM) and BP algorithm, the GA-BP prediction model significantly reduces the relative error of power output prediction, which provides a solid foundation for multi-stack SOFC systems.

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

Cited by 14 publications

References 41 publications

Artificial Intelligence and Machine Learning in Energy Conversion and Management

Artificial Intelligence and Machine Learning in Energy Conversion and Management

The Degradation Prediction of Proton Exchange Membrane Fuel Cell Performance Based on a Transformer Model

A Single-Stack Output Power Prediction Method for High-Power, Multi-Stack SOFC System Requirements

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