A Hybrid Method for Remaining Useful Life Prediction of Proton Exchange Membrane Fuel Cell Stack

Wang, Fu‐Kwun; Amogne, Zemenu Endalamaw; Chou, Jia‐Hong

doi:10.1109/access.2021.3064684

Cited by 12 publications

(4 citation statements)

References 47 publications

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“…These outcomes are measured using the RMSE metric to highlight how well the models reduce the gap between the expected and targeted RUL. The results of the model proposed in the 2014 PHM Data Challenge Dataset are contrasted with various competing models such as Fusion [48], 1 input-ESN [49], 2 input-ESN [49], 3 input-ESN [49], SAE-DNN [50], ML-DNN [50], and RCLMA [51]. The superior efficacy of the system is illustrated through the presentation of findings, as evidenced by the RMSE values detailed in Table 5.…”

Section: Comparison Resultsmentioning

confidence: 99%

Enhancing Prognostics of PEM Fuel Cells with a Dual-Attention LSTM Network for Remaining Useful Life Estimation: A Deep Learning Model

Darwish

2024

Sustain. Mach. Intell. J.

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The Proton Exchange Membrane Fuel Cell (PEMFC) presents itself as a viable and effective technology to consider for transportation purposes. An essential aspect in the realm of electric vehicles is the crucial evaluation of the deterioration of the PEMFC stack. This paper proposed a data-driven deep learning framework that combines a Long Short-Term Memory (LSTM), self-attention, and scaled dot-product attention mechanism in order to enhance the precision of Remaining Useful Life (RUL) prediction for Proton Exchange Membrane Fuel Cells (PEMFCs). The LSTM enables the model to comprehend intricate temporal patterns and produce more abstract data representations. The self-attention mechanism is utilized to detect correlations among various time points. The scaled dot-product attention mechanism is employed to focus on the most crucial features. The effectiveness of the proposed model was evaluated by utilizing the 2014 PHM Data Challenge Dataset and comparing it with different Deep Learning models to showcase its efficacy. The findings from the experiments suggest that DA-LSTM proves to be a reliable choice for predicting the RUL of PEMFCs, as it demonstrated superior performance compared to all other models examined.

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Section: Comparison Resultsmentioning

confidence: 99%

Enhancing Prognostics of PEM Fuel Cells with a Dual-Attention LSTM Network for Remaining Useful Life Estimation: A Deep Learning Model

Darwish

2024

Sustain. Mach. Intell. J.

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“…Machine learning excels at capturing key characteristics and is commonly employed in prediction tasks involving missing or complex models. Wang et al [90] combined the Monte Carlo dropout method and deep neural network to predict the fuel cell health state. Based on wavelet transform, Chen et al [91] developed an extreme learning machine with genetic algorithm optimization.…”

Section: Machine Learning Methodsmentioning

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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“…Many auto sectors are keen on developing clean energy technologies for powering EVs. PEMFC [1] based electric vehicles are considered efficient alternatives to internal combustion (IC) engines because of their greater current density, clean power generation, higher efficacy, and naturally amiable features. Thus, fuel cell vehicles are a significant component of clean energy vehicles and have been used extensively in real-world applications [2,3].…”

Section: Introductionmentioning

confidence: 99%

Implementation of Non-Isolated High Gain Interleaved DC-DC Converter for Fuel Cell Electric Vehicle Using ANN-Based MPPT Controller

Subbulakshmy,

Palanisamy,

Alshahrani

et al. 2024

Sustainability

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A high conversion ratio DC-DC converter is crucial for fuel cell electric vehicles (FCEV). A fuel cell-based non-isolated high gain integrated DC-DC converter for electric vehicles is proposed in this paper. The system comprises an interleaved boost converter (IBC) at the source end, a switched capacitor cell, coupled inductors, a passive clamp circuit, and a voltage multiplier circuit (VMC). Its significance is to achieve the voltage conversion gain of 12.33 at a conversion ratio of 0.45. The idea is to use a proton exchange membrane fuel cell to power electric vehicles through a high-gain DC-DC converter. The use of an ineffective MPPT can result in lower energy conversion efficiency. Thus, this system incorporates a maximum power point tracking (MPPT) controller based on a neural network, which relies on the radial basis function network (RBFN) algorithm to track the maximum power point of the PEMFC accurately. The comparative study of the fuel cell electric vehicle (FCEV) structure with the RBFN-based MPPT technique was evaluated with that of the fuzzy logic technique using the MATLAB/Simulink platform (R2021b (MATLAB 9.11)). A 1.5 kW experimental prototype is designed with a switching frequency of 10 kHz to validate the design analysis, and its pursuance is compared between RBFN and FLC-based controllers. This manuscript will be a significant contribution towards evidencing a sustainable environment.

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A Hybrid Method for Remaining Useful Life Prediction of Proton Exchange Membrane Fuel Cell Stack

Cited by 12 publications

References 47 publications

Enhancing Prognostics of PEM Fuel Cells with a Dual-Attention LSTM Network for Remaining Useful Life Estimation: A Deep Learning Model

Enhancing Prognostics of PEM Fuel Cells with a Dual-Attention LSTM Network for Remaining Useful Life Estimation: A Deep Learning Model

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

Implementation of Non-Isolated High Gain Interleaved DC-DC Converter for Fuel Cell Electric Vehicle Using ANN-Based MPPT Controller

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