A Comprehensive Review of Degradation Prediction Methods for an Automotive Proton Exchange Membrane Fuel Cell

Nguyen, Hoang; Lee, S. M.; Yu, Sangseok

doi:10.3390/en16124772

Cited by 8 publications

(3 citation statements)

References 102 publications

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“…Previous researchers are trying to use ML to design and optimize membranes [44,[144][145][146][147][148][149][150][151], predict membrane properties [36,43,152], diagnose membrane conditions [153,154], and prevent membrane degradation [155][156][157]. Cho et al [43] collected data from a 1.2 kW PEMFC in a MATLAB/Simulink environment and used that data to train a nonlinear autoregressive network (NARX) with Bayesian optimization to predict the voltage, temperature, and membrane water content (Max.…”

Section: In the Field Of Membranementioning

confidence: 99%

Towards Reliable Prediction of Performance for Polymer Electrolyte Membrane Fuel Cells via Machine Learning-Integrated Hybrid Numerical Simulations

Kaiser,

Ahn,

Kim

et al. 2024

Processes

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For mitigating global warming, polymer electrolyte membrane fuel cells have become promising, clean, and sustainable alternatives to existing energy sources. To increase the energy density and efficiency of polymer electrolyte membrane fuel cells (PEMFC), a comprehensive numerical modeling approach that can adequately predict the multiphysics and performance relative to the actual test such as an acceptable depiction of the electrochemistry, mass/species transfer, thermal management, and water generation/transportation is required. However, existing models suffer from reliability issues due to their dependency on several assumptions made for the sake of modeling simplification, as well as poor choices and approximations in material characterization and electrochemical parameters. In this regard, data-driven machine learning models could provide the missing and more appropriate parameters in conventional computational fluid dynamics models. The purpose of the present overview is to explore the state of the art in computational fluid dynamics of individual components of the modeling of PEMFC, their issues and limitations, and how they can be significantly improved by hybrid modeling techniques integrating with machine learning approaches. Furthermore, a detailed future direction of the proposed solution related to PEMFC and its impact on the transportation sector is discussed.

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Section: In the Field Of Membranementioning

confidence: 99%

Towards Reliable Prediction of Performance for Polymer Electrolyte Membrane Fuel Cells via Machine Learning-Integrated Hybrid Numerical Simulations

Kaiser,

Ahn,

Kim

et al. 2024

Processes

View full text Add to dashboard Cite

show abstract

“…The hybrid model method combines the advantages of various models to improve the prediction accuracy of the RUL of the reactor to a greater extent [27]. Cheng [28] proposed a fuel cell RUL prediction method based on the least square support vector machine (LSSVM) and regularized particle filter (RPF).…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction Method of PEM Fuel Cells Based on a Hybrid Model

Tian,

Chen,

Ding

et al. 2023

Electronics

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To predict the remaining useful life (RUL) of the proton exchange membrane fuel cell (PEMFC) in advance, a prediction method based on the voltage recovery model and Bayesian optimization of a multi-kernel relevance vector machine (MK-RVM) is proposed in this paper. First, the empirical mode decomposition (EMD) method was used to preprocess the data, and then MK-RVM was used to train the model. Next, the Bayesian optimization algorithm was used to optimize the weight coefficient of the kernel function to complete the parameter update of the prediction model, and the voltage recovery model was added to the prediction model to realize the rapid and accurate prediction of the RUL of PEMFC. Finally, the method proposed in this paper was applied to the open data set of PEMFC provided by Fuel Cell Laboratory (FCLAB), and the prediction accuracy of RUL for PEMFC was obtained by 95.35%, indicating that this method had good generalization ability and verified the accuracy of the method when predicting the RUL of PEMFC. The realization of long-term projections for PEMFC RUL not only improves the useful life, reliability, and safety of PEMFC but also reduces operating costs and downtime.

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“…Hydrogen, as a pollution-free green energy source, is regarded as a crucial component of new energy [2]. As a novel energy device and the carrier of hydrogen energy, fuel cells have undergone extensive research due to their advantages, including zero carbon emissions and high energy density [3].…”

Section: Introductionmentioning

confidence: 99%

Research Progress on Aging Prediction Methods for Fuel Cells: Mechanism, Methods, and Evaluation Criteria

Tian,

Wei,

Wang

et al. 2023

Energies

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Due to the non-renewable nature and pollution associated with fossil fuels, there is widespread research into alternative energy sources. As a novel energy device, a proton exchange membrane fuel cell (PEMFC) is considered a promising candidate for transportation due to its advantages, including zero carbon emissions, low noise, and high energy density. However, the commercialization of fuel cells faces a significant challenge related to aging and performance degradation during operation. In order to comprehensively address the issue of fuel cell aging and performance decline, this paper provides a detailed review of aging mechanisms and influencing factors from the perspectives of both the PEMFC system and the stack. On this basis, this paper offers targeted solutions to degradation issues stemming from various aging factors and presents research on aging prediction methods to proactively mitigate aging-related problems. Furthermore, to enhance prediction accuracy, this paper categorizes and analyzes the degradation index and accuracy evaluation criteria commonly employed in the existing fuel cell aging research. The results indicate that specific factors leading to aging-related failures are often addressed via targeted solving methods, corresponding to specific degradation indexes. The significance of this study lies in the following aspects: (1) investigating the aging factors in fuel cells and elucidating the multiple aging mechanisms occurring within fuel cells; (2) proposing preventive measures, solutions, and aging prediction methods tailored to address fuel cell aging issues comprehensively, thereby mitigating potential harm; and (3) summarizing the degradation index and accuracy evaluation standards for aging prediction, offering new perspectives for resolving fuel cell aging problems.

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A Comprehensive Review of Degradation Prediction Methods for an Automotive Proton Exchange Membrane Fuel Cell

Cited by 8 publications

References 102 publications

Towards Reliable Prediction of Performance for Polymer Electrolyte Membrane Fuel Cells via Machine Learning-Integrated Hybrid Numerical Simulations

Towards Reliable Prediction of Performance for Polymer Electrolyte Membrane Fuel Cells via Machine Learning-Integrated Hybrid Numerical Simulations

Remaining Useful Life Prediction Method of PEM Fuel Cells Based on a Hybrid Model

Research Progress on Aging Prediction Methods for Fuel Cells: Mechanism, Methods, and Evaluation Criteria

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