A Short-Term and Long-Term Prognostic Method for PEM Fuel Cells Based on Gaussian Process Regression

Wang, Tianxiang; Zhou, Hongliang; Zhu, Chengwei

doi:10.3390/en15134844

Cited by 14 publications

(3 citation statements)

References 36 publications

(80 reference statements)

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“…A Gaussian Process [26][27][28] is a collection of continuous random functions, where the values at any finite set of points constitute a multivariate Gaussian distribution. In regression problems, each point in the input space X is considered to map to a random variable in the output space Y.…”

Section: Gaussian Process Regressionmentioning

confidence: 99%

Sensitivity analysis and optimization of operating conditions of proton exchange membrane fuel cell

Liao,

Photong,

2024

J Appl Electrochem

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Power characteristics are important indicators of fuel cell performance. In the actual operation of fuel cells, changes in operating conditions lead to variations in their power characteristics. Therefore, it is imperative to explore the impact of operating conditions on power characteristics. This paper analyzes the factors influencing fuel cell power and uses sensitivity analysis to investigate how different factors affect fuel cell performance. The operating parameters are optimized using a Bayesian-optimized Gaussian process regression model. The research results indicate that temperature has the greatest impact on fuel cell power, followed by stoichiometry and backpressure. The Bayesian-optimized Gaussian process regression model performs the best, reducing its RSME from 0.1 to 0.0556. Residual analysis and regression characteristic analysis verify the optimized model's improved fitting and regression characteristics. Based on the Bayesian-Gaussian process regression model, the optimized operating parameters are obtained for maximum power: a temperature of 80 °C, stoichiometry of 4, and backpressure of 1.7 bar. This paper provides theoretical support for improving fuel cell performance.。 Keyword: Fuel cells, operating conditions, power characteristics, sensitivity analysis, Gaussian process regression model IntroductionProton exchange membrane fuel cell (PEMFC), with their advantages of zero emissions, environmental friendliness, high power density, low noise, and long lifespan, have the potential to promote sustainable transportation and environmental development in various fields [1-2]. However, drawbacks such as relatively poor transient response characteristics and environmental adaptability have limited their commercial development [3]. In light of these challenges, enhancing fuel cell performance has become a focal point of industry research. Power, as a crucial parameter for assessing fuel cell performance and lifespan, is not only related to the performance and feasibility of fuel cells in various applications but also provides an effective basis for judging fuel cell performance degradation. Therefore, research on fuel cell power and its influencing factors is paramount.Factors affecting fuel cell power: Several factors affect system design, operating conditions, and materials. System 2 design primarily includes aspects such as system integration and control. Kim et al. [4] established a transient model of a fuel cell system and proposed a time-delay control strategy to prevent air starvation and flooding. This strategy adjusts the optimal stoichiometry of oxygen and hydrogen. The study verified that this control strategy is crucial in improving the transient performance of PEMFC systems through a comparative analysis with feedforward control and linear quadratic Gaussian control with a state estimator. To comprehensively understand the coupled effects of different factors on the overall performance and durability of PEMFC, Wu et al. [5] reviewed four aspects: fuel cell stack, subsystems, system in...

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Section: Gaussian Process Regressionmentioning

confidence: 99%

Sensitivity analysis and optimization of operating conditions of proton exchange membrane fuel cell

Liao,

Photong,

2024

J Appl Electrochem

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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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“…A large number of studies on life prediction methods also show the development trends and challenges of the PEMFC in this area [14][15][16]. The reliability of long-term life predictions under complex conditions [17] and the influence of different factors on dynamic operating condition predictions [18,19] need to be further explored.…”

Section: Introductionmentioning

confidence: 99%

A Review of Life Prediction Methods for PEMFCs in Electric Vehicles

Tang

Yao

et al. 2022

Sustainability

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The proton-exchange membrane fuel cell (PEMFC) has the advantage of high energy conversion efficiency, environmental friendliness, and zero carbon emissions. Therefore, as an attractive alternative energy, it is widely used in vehicles. Due to its high nonlinearity, strong time variation, and complex failure mechanisms, it is extremely difficult to predict PEMFC life in electric vehicles. The uncertainty of life predictions for the PEMFC limits its wide application. Since it is particularly important to accurately carry out PEMFC life predictions, significant research efforts are directed toward tackling this issue by adopting effective methods. In this paper, a number of PEMFC life prediction methods for electric vehicles are reviewed and summarized. The goal of this review is to render feasible and potential solutions for dealing with PEMFC life issues considering dynamic vehicle conditions. Based on this review, the reader can also easily understand the research status of PEMFC life prediction methods and this review lays a theoretical foundation for future research.

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A Short-Term and Long-Term Prognostic Method for PEM Fuel Cells Based on Gaussian Process Regression

Cited by 14 publications

References 36 publications

Sensitivity analysis and optimization of operating conditions of proton exchange membrane fuel cell

Sensitivity analysis and optimization of operating conditions of proton exchange membrane fuel cell

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

A Review of Life Prediction Methods for PEMFCs in Electric Vehicles

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