On-line state of charge estimation of embedded metal hydride hydrogen storage tank based on state classification

Zhu, Dan; Amirat, Youcef; N’Diaye, Abdoul; Djerdir, Abdesslem

doi:10.1016/j.est.2021.102950

Cited by 9 publications

(3 citation statements)

References 24 publications

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“…Furthermore, metal hydrides can densely store hydrogen in moderate temperature and pressure conditions 1 , 13 , 19 . Metal hydride materials have some essential features such as fast hydrogen absorption and desorption kinetics, the ability to tolerate poisonous materials and contaminations in the hydrogen feedstock, produced efficiently with minimum cost, easy activation, and stability 16 , 20 – 23 .…”

Section: Introductionmentioning

confidence: 99%

“…Metal Hydrides are classified into elemental, intermetallic, and complex hydrides 14 . The commonly used hydrogen-storing hydrides are classified based on their stoichiometries; They include but are not limited to AB 5 -type, AB 2 -type, A 2 B-type, and AB-type 7 , 14 , 20 . In these structures, A and B are metals with high and low affinity for hydrogen, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Connectionist technique estimates of hydrogen storage capacity on metal hydrides using hybrid GAPSO-LSSVM approach

Maghsoudy,

Zakerabbasi,

Baghban

et al. 2024

Sci Rep

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The AB2 metal hydrides are one of the preferred choices for hydrogen storage. Meanwhile, the estimation of hydrogen storage capacity will accelerate their development procedure. Machine learning algorithms can predict the correlation between the metal hydride chemical composition and its hydrogen storage capacity. With this purpose, a total number of 244 pairs of AB2 alloys including the elements and their respective hydrogen storage capacity were collected from the literature. In the present study, three machine learning algorithms including GA-LSSVM, PSO-LSSVM, and HGAPSO-LSSVM were employed. These models were able to appropriately predict the hydrogen storage capacity in the AB2 metal hydrides. So the HGAPSO-LSSVM model had the highest accuracy. In this model, the statistical factors of R2, STD, MSE, RMSE, and MRE were 0.980, 0.043, 0.0020, 0.045, and 0.972%, respectively. The sensitivity analysis of the input variables also illustrated that the Sn, Co, and Ni elements had the highest effect on the amount of hydrogen storage capacity in AB2 metal hydrides.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Connectionist technique estimates of hydrogen storage capacity on metal hydrides using hybrid GAPSO-LSSVM approach

Maghsoudy,

Zakerabbasi,

Baghban

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Hydrogen generation, consumption and storage system where an electrolyzer can be used to obtain hydrogen from water, a fuel cell that will generate electrical and thermal energy and a hydrogen tank to store hydrogen [12]. Zhu, D et al investigated the state of charge (SOC) estimation of embedded MH hydrogen storage tanks, designed a joint multiclassifier to identify the current state of the hydrogen reaction, and indicated that at ambient temperature and pressure, metal hydride ( MH) storage of hydrogen is one of the best solutions for future hydrogen-fueled vehicles [13]. Apostolou, D. found that metal hydride storage has an advantage in the price of dispensing hydrogen fuel and is expected to be a primary means of storing hydrogen for small hydrogen-fueled vehicles through a comparison between metal hydride storage devices and compressed hydrogen storage tanks [14].…”

Section: Introductionmentioning

confidence: 99%

Research on hydrogen fuel cell backup power for metal hydride hydrogen storage system

Zhang,

Pan,

Lei

et al. 2024

Applied Mathematics and Nonlinear Sciences

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Hydrogen fuel cells are characterized by non-pollution, high efficiency and long power supply time, and they are increasingly used as backup power systems in substations, communication base stations and other fields. In this paper, based on the thermodynamic model of the hydride hydrogen storage system, the relationship between pressure, composition, and temperature in metal hydride hydrogen storage is quantitatively analyzed using a PCT curve. The hydrogen fuel power supply is used as the overall backup power supply of the DC system, and the hydrogen-fuel integrated backup power supply is established to realize the uninterrupted switching between the utility power and the backup power supply. Finally, the working process of the backup power supply and the reaction process of hydrogen are analyzed to test the feasibility of a hydrogen fuel cell backup power supply. The results show that the operating current climbs to the end of 80 A under the 5 kW workload demand of the communication equipment. In addition, the hydrogen absorption reaction rate was 0.29 Mpa, and the hydrogen release reaction rate was 0.21 Mpa at a temperature of 291 K. This study has developed a fuel cell backup power system that can provide uninterruptible backup power and has a wide market capacity and application prospects.

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Machine Learning in Solid‐State Hydrogen Storage Materials: Challenges and Perspectives

Zhou,

Xiao

et al. 2024

Advanced Materials

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Machine learning (ML) has emerged as a pioneering tool in advancing the research application of high‐performance solid‐state hydrogen storage materials (HSMs). This review summarizes the state‐of‐the‐art research of ML in resolving crucial issues such as low hydrogen storage capacity and unfavorable de‐/hydrogenation cycling conditions. First, the datasets, feature descriptors, and prevalent ML models tailored for HSMs are described. Specific examples include the successful application of ML in titanium‐based, rare‐earth‐based, solid solution, magnesium‐based, and complex HSMs, showcasing its role in exploiting composition–structure–property relationships and designing novel HSMs for specific applications. One of the representative ML works is the single‐phase Ti‐based HSM with superior cost‐effective and comprehensive properties, tailored to fuel cell hydrogen feeding system at ambient temperature and pressure through high‐throughput composition‐performance scanning. More importantly, this review also identifies and critically analyzes the key challenges faced by ML in this domain, including poor data quality and availability, and the balance between model interpretability and accuracy, together with feasible countermeasures suggested to ameliorate these problems. In summary, this work outlines a roadmap for enhancing ML's utilization in solid‐state hydrogen storage research, promoting more efficient and sustainable energy storage solutions.

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On-line state of charge estimation of embedded metal hydride hydrogen storage tank based on state classification

Cited by 9 publications

References 24 publications

Connectionist technique estimates of hydrogen storage capacity on metal hydrides using hybrid GAPSO-LSSVM approach

Connectionist technique estimates of hydrogen storage capacity on metal hydrides using hybrid GAPSO-LSSVM approach

Research on hydrogen fuel cell backup power for metal hydride hydrogen storage system

Machine Learning in Solid‐State Hydrogen Storage Materials: Challenges and Perspectives

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