ReviewiScience2025
Energy advancements and integration strategies in hydrogen and battery storage for renewable energy systems.
Review in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The long term and large scale energy storage operations require quick response time and round-trip efficiency, which are not feasible with conventional battery systems. To address this issue while endorsing high energy density, long term storage, and grid adaptability, the hydrogen energy storage (HES) is preferred. This proposed work makes a comprehensive review on HES while synthesizing recent research on energy storage technologies and integration into renewable energy (RE) applications. The proposed research also identifies critical challenges related to system optimization, energy management strategies, and economic viability while featuring emerging technologies like artificial intelligence (AI) and machine learning (ML) for energy management. The proposed survey also discusses key advancements in battery technologies (lithium-ion, Ni-Cd, Ni/MH, and flow batteries) that are examined alongside innovations in HES methods. The proposed survey utilizes an extensive list of publications to date in the open literature to canvass and portray various developments in this area.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.