Two parameters identification for polarization curve fitting of
            <scp>PEMFC</scp>
            based on genetic algorithm

Shen, Jun; Du, Changqing; Yan, Fuwu; Chen, Ben; Tu, Zhengkai

doi:10.1002/er.7831

Cited by 12 publications

(6 citation statements)

References 48 publications

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“…The conventional approach is to manually adjust parameters until the optimal combination is identified. However, this method is time‐consuming 38 . Bio‐inspired optimization algorithms look to the physical meanings of natural biological systems, human activities, and the group behaviors of animals to develop mathematical algorithms to simplify search procedures 39 …”

Section: Literature Reviewmentioning

confidence: 99%

“…However, this method is time-consuming. 38 Bioinspired optimization algorithms look to the physical meanings of natural biological systems, human activities, and the group behaviors of animals to develop mathematical algorithms to simplify search procedures. 39 Rere et al 40 developed the ant colony optimization (ACO) CNN, which increases the time of training a CNN model and substantially improves its accuracy.…”

Section: Use Of Deep Learning Techniquesmentioning

confidence: 99%

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Metaheuristics‐optimized deep learning to predict generation of sustainable energy from rooftop plant microbial fuel cells

Chou

Cheng

Liu

et al. 2022

Intl J of Energy Research

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Plant microbial fuel cells (PMFCs) are an emergent green-energy technology that continuously converts solar energy into electricity. Placing PMFCs on the roofs of urban buildings can help to create green urban environments even as they generate power. The power generation performance of PMFCs is affected by a range of environmental factors, so their power generation capacity is difficult to estimate. To develop an artificial intelligence model to forecast PMFC power generation accurately, relevant results obtained using shallow and deep learning techniques are compared for the first time. Once deep learning techniques had been identified as superior for this purpose, they were used with a bio-inspired optimization algorithm to dynamically setting the model hyperparameters. The developed model can also be applied to estimate the power generation capacity of PMFC devices in the future. The model was trained using data collected from sensors in a site experiment that was carried out using PMFCs embedded with Chinese pennisetumin (Pennisetum alopecuroides), narrowleaf cattail (Typha angustifolia), dwarf rotala (Rotala rotundifolia), and no plant as a control group. The original data of device parameters, environmental parameters, and the measured power generation of PMFCs in numerical form were applied to train shallow learning and time-series deep learning models. Meanwhile, the state-of-the-art sliding window technique was used to establish a numerical matrix, which was converted into a 2D image-like format to represent inputs for deep convolutional neural network (CNN) models. The accuracy in predicting the power generation capacity of PMFC devices showed that EfficientNet, an advanced type of CNN, was the best model among the shallow and deep learning techniques. These analytical results demonstrate the superior performance of deep CNNs in learning image features and their consequent suitability for constructing PMFC power generation forecasting models. To enhance the generalization performance of CNN, a

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Section: Literature Reviewmentioning

confidence: 99%

Section: Use Of Deep Learning Techniquesmentioning

confidence: 99%

Metaheuristics‐optimized deep learning to predict generation of sustainable energy from rooftop plant microbial fuel cells

Chou

Cheng

Liu

et al. 2022

Intl J of Energy Research

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“…Friede et al [7] built a model to forecast PEMFC transient responses in various operating conditions. Shen et al [8] applied genetic algorithms to fit the polarization curve for a 62 kW PEMFC. The errors of the proposed model and the experimental data were less than 3%.…”

Section: Introductionmentioning

confidence: 99%

Superior Optimization for a Hybrid PEMFC Power System Employing Model Predictions

Wang

2023

International Journal of Energy Research

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This research investigated the impacts of model prediction on the optimization of hybrid energy systems using a system consisting of solar panels, batteries, a proton exchange membrane fuel cell (PEMFC), and a chemical hydrogen generation system. A PEMFC has several advantages, such as low operating temperatures, fast response times, high power density, and environmental friendliness, and it can convert hydrogen into electricity. However, because hydrogen costs are an important consideration, the PEMFC is usually integrated with hybrid energy systems to guarantee system sustainability. Therefore, in this study, a whole-year household load and solar radiation data were applied to optimize the system components and power management, thereby reducing the system cost by 42.43% and improving system sustainability by 7.05%. The system responses showed that some hydrogen consumption might be saved if the solar and load profiles could be foreseen. Two prediction models were developed that could accurately forecast the radiation and load profiles. Next, a second-year dataset was employed to verify the effectiveness of the model prediction. The results showed that the system cost was reduced by 40.20% without model prediction and by 44.06% with model prediction compared to the original system settings. Finally, experiments to illustrate the feasibility of the hybrid energy system were conducted using prediction models. Based on the results, the model prediction was deemed effective in improving the performance of hybrid energy systems.

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“…Among the most recent algorithms used for fuel cell parameter estimation are bio-inspired optimization algorithms such as hybrid artificial bee colony differential optimizers, 6 genetic algorithms, 7 manta ray foraging optimizers, 8 improved chaotic MayFly optimization, 9 hybrid interior search algorithm, 10 modified gorilla troop optimizer, 11 bi-subgroup optimization, 12 adaptive sparrow search, 13 chaos embedded particle swarm optimization, 14 improved monarch butterfly optimizer, 15 water strider algorithm. 16 These bio-inspired optimization algorithms can find the global minimum in the parameter space and are robust in dealing with the nonlinearities in the fuel cell model.…”

Section: Introductionmentioning

confidence: 99%

Control‐oriented estimation of the exchange current density in PEM fuel cells via stochastic filtering

Aguilar

Andrade‐Cetto

Husar

2022

Intl J of Energy Research

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Increasing efficiency and durability of fuel cells can be achieved through advanced model-based optimal control of its operating conditions, and the efficient online estimation of fuel cell parameters and internal states is fundamental for the implementation of such advanced controllers. The exchange current density is a driving parameter of performance for the catalyst layer of proton exchange membrane fuel cells (PEMFC). This study presents a control-oriented, stochastic filtering approach for online, continuous estimation of the exchange current density in low-temperature PEMFCs. The fuel cell is framed as a Markov model where the exchange current density is posed as the stochastic hidden state. The physics-based static equation of the exchange current density is converted into a state transition equation. This transition equation

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Two parameters identification for polarization curve fitting of PEMFC based on genetic algorithm

Cited by 12 publications

References 48 publications

Metaheuristics‐optimized deep learning to predict generation of sustainable energy from rooftop plant microbial fuel cells

Metaheuristics‐optimized deep learning to predict generation of sustainable energy from rooftop plant microbial fuel cells

Superior Optimization for a Hybrid PEMFC Power System Employing Model Predictions

Control‐oriented estimation of the exchange current density in PEM fuel cells via stochastic filtering

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