Estimating the Remaining Useful Life of Proton Exchange Membrane Fuel Cells under Variable Loading Conditions Online

Wang, Penghao; Liu, Hao; Hou, Ming; Zheng, Li‐Min; Yang, Yue; Geng, Jianxin; Song, Wei; Shao, Zhigang

doi:10.3390/pr9081459

Cited by 4 publications

(3 citation statements)

References 33 publications

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“…Second, other criteria, such as the fitness improvement rate and population diversity, should be considered by MSPSOTLP during the solution update process to preserve the novel particles that can bring long-term success for the algorithm. Finally, it is worth it to investigate the feasibility of ANN optimized by MSPSOTLP to address challenging issues encountered in the intelligent condition monitoring of complex industrial systems [2], such as the remaining useful life prediction of gear pumps [72], the time series prognosis of fuel cells [73], and predictive maintenance of renewable energy systems [74].…”

Section: Discussionmentioning

confidence: 99%

A Modified Particle Swarm Optimization Algorithm for Optimizing Artificial Neural Network in Classification Tasks

et al. 2022

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Artificial neural networks (ANNs) have achieved great success in performing machine learning tasks, including classification, regression, prediction, image processing, image recognition, etc., due to their outstanding training, learning, and organizing of data. Conventionally, a gradient-based algorithm known as backpropagation (BP) is frequently used to train the parameters’ value of ANN. However, this method has inherent drawbacks of slow convergence speed, sensitivity to initial solutions, and high tendency to be trapped into local optima. This paper proposes a modified particle swarm optimization (PSO) variant with two-level learning phases to train ANN for image classification. A multi-swarm approach and a social learning scheme are designed into the primary learning phase to enhance the population diversity and the solution quality, respectively. Two modified search operators with different search characteristics are incorporated into the secondary learning phase to improve the algorithm’s robustness in handling various optimization problems. Finally, the proposed algorithm is formulated as a training algorithm of ANN to optimize its neuron weights, biases, and selection of activation function based on the given classification dataset. The ANN model trained by the proposed algorithm is reported to outperform those trained by existing PSO variants in terms of classification accuracy when solving the majority of selected datasets, suggesting its potential applications in challenging real-world problems, such as intelligent condition monitoring of complex industrial systems.

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Section: Discussionmentioning

confidence: 99%

A Modified Particle Swarm Optimization Algorithm for Optimizing Artificial Neural Network in Classification Tasks

et al. 2022

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“…Ao et al [15] proposed an RUL prediction method based on the Kalman filter in the frequency domain to process aging data of PEMFC in groups. Wang et al [16] established an aging model based on polarization curves, used the particle filter algorithm to estimate the aging parameters online, and used the rated voltage as a new health index. Although model-based methods can analyze the aging process combined with the mechanism, the aging mechanism of PEMFC has not been thoroughly studied, and various aging models are usually unproven, so the prediction accuracy of model-driven methods is usually not guaranteed [4].…”

Section: Literature Reviewmentioning

confidence: 99%

A Compound Framework for Forecasting the Remaining Useful Life of PEMFC

Wu,

Fu,

Shan

et al. 2024

Electronics

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Proton exchange membrane fuel cells (PEMFC) are widely acknowledged as a prospective power source, but durability problems have constrained development. Therefore, a compound prediction framework is proposed in this paper by integrating the locally weighted scatter plot smoothing method (LOESS), uniform information coefficient (UIC), and attention-based stacked generalization model (ASGM) with improved dung beetle optimization (IDBO). Firstly, LOESS is adopted to filter original degraded sequences. Then, UIC is applied to obtain critical information by selecting relevant factors of the processed degraded sequences. Subsequently, the critical information is input into the base models of ASGM, including kernel ridge regression (KRR), extreme learning machine (ELM), and the temporal convolutional network (TCN), to acquire corresponding prediction results. Finally, the prediction results are fused using the meta-model attention-based LSTM of ASGM to obtain future degradation trends (FDT) and the remaining useful life (RUL), in which the attention mechanism is introduced to deduce weight coefficients of the base model prediction results in LSTM. Meanwhile, IDBO based on Levy flight, adaptive mutation, and polynomial mutation strategies are proposed to search for optimal parameters in LSTM. The application of two different datasets and their comparison with five related models shows that the proposed framework is suitable and effective for forecasting the FDT and RUL of PEMFC.

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“…The experiment showed that the technique could accurately forecast the degeneration trend of the PEMFC output tension and aging parameters under different training stages. Focusing on the difficulties in online predictions, Penghao Wang [87] and other scholars proposed a nonlinear empirical degradation model and then employed PF to estimate the degradation state variables online to achieve the online prediction standard of the PEMFC. Taking the rated voltage as the new aging index, it could achieve superb life predictions in variable load and online predictions.…”

Section: Hybrid Approachesmentioning

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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Estimating the Remaining Useful Life of Proton Exchange Membrane Fuel Cells under Variable Loading Conditions Online

Cited by 4 publications

References 33 publications

A Modified Particle Swarm Optimization Algorithm for Optimizing Artificial Neural Network in Classification Tasks

A Modified Particle Swarm Optimization Algorithm for Optimizing Artificial Neural Network in Classification Tasks

A Compound Framework for Forecasting the Remaining Useful Life of PEMFC

A Review of Life Prediction Methods for PEMFCs in Electric Vehicles

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