Pramoth Varsan Madhavan , Samaneh Shahgaldi , Xianguo Li
{"title":"Long short-term memory time series modelling of pressure valves for hydrogen-powered vehicles and infrastructure","authors":"Pramoth Varsan Madhavan , Samaneh Shahgaldi , Xianguo Li","doi":"10.1016/j.ijhydene.2025.04.028","DOIUrl":null,"url":null,"abstract":"<div><div>Long-term reliability and accuracy of pressure valves are critical for hydrogen infrastructure and applications, particularly in hydrogen-powered vehicles exposed to extreme weather conditions like cold winters and hot summers. This study evaluates such valves using the Endurance Test specified in European Commission Regulation (EU) No 406/2010, fulfilling Regulation (EC) No 79/2009 requirements for hydrogen vehicle type approval. A long short-term memory (LSTM) network accelerates valve development and validation by simulating endurance tests. The LSTM model, with three inputs and one output, predicts valve outlet pressure responses using experimental data collected at 25 °C, 85 °C, and −40 °C, simulating a 20-year lifecycle of 75,000 cycles. At 25 °C, the model achieves optimal performance with 40,000 training cycles and an R<sup>2</sup> of 0.969, with R<sup>2</sup> values exceeding 0.960 across all temperatures. This efficient, robust approach accelerates testing, enabling real-time diagnostics and advancing hydrogen technologies for a sustainable future.</div></div>","PeriodicalId":337,"journal":{"name":"International Journal of Hydrogen Energy","volume":"124 ","pages":"Pages 67-83"},"PeriodicalIF":8.1000,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Hydrogen Energy","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0360319925016386","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Long-term reliability and accuracy of pressure valves are critical for hydrogen infrastructure and applications, particularly in hydrogen-powered vehicles exposed to extreme weather conditions like cold winters and hot summers. This study evaluates such valves using the Endurance Test specified in European Commission Regulation (EU) No 406/2010, fulfilling Regulation (EC) No 79/2009 requirements for hydrogen vehicle type approval. A long short-term memory (LSTM) network accelerates valve development and validation by simulating endurance tests. The LSTM model, with three inputs and one output, predicts valve outlet pressure responses using experimental data collected at 25 °C, 85 °C, and −40 °C, simulating a 20-year lifecycle of 75,000 cycles. At 25 °C, the model achieves optimal performance with 40,000 training cycles and an R2 of 0.969, with R2 values exceeding 0.960 across all temperatures. This efficient, robust approach accelerates testing, enabling real-time diagnostics and advancing hydrogen technologies for a sustainable future.
期刊介绍:
The objective of the International Journal of Hydrogen Energy is to facilitate the exchange of new ideas, technological advancements, and research findings in the field of Hydrogen Energy among scientists and engineers worldwide. This journal showcases original research, both analytical and experimental, covering various aspects of Hydrogen Energy. These include production, storage, transmission, utilization, enabling technologies, environmental impact, economic considerations, and global perspectives on hydrogen and its carriers such as NH3, CH4, alcohols, etc.
The utilization aspect encompasses various methods such as thermochemical (combustion), photochemical, electrochemical (fuel cells), and nuclear conversion of hydrogen, hydrogen isotopes, and hydrogen carriers into thermal, mechanical, and electrical energies. The applications of these energies can be found in transportation (including aerospace), industrial, commercial, and residential sectors.