Prediction of Freezing Time During Hydrogen Fueling Using Machine Learning
This study presents a method for predicting nozzle surface temperature and the timing of frost formation during hydrogen refueling using machine learning. A continuous refueling system was implemented based on a simulation model that was developed and validated in previous research. Data were collec...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/23/5962 |
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| author | Ji-Ah Choi Ji-Seong Jang Sang-Won Ji |
| author_facet | Ji-Ah Choi Ji-Seong Jang Sang-Won Ji |
| author_sort | Ji-Ah Choi |
| collection | DOAJ |
| description | This study presents a method for predicting nozzle surface temperature and the timing of frost formation during hydrogen refueling using machine learning. A continuous refueling system was implemented based on a simulation model that was developed and validated in previous research. Data were collected under various boundary conditions, and eight regression models were trained and evaluated for their predictive performance. Hyperparameter optimization was performed using random search to enhance model performance. The final models were validated by applying boundary conditions not used during model development and comparing the predicted values with simulation results. The comparison revealed that the maximum error rate occurred after the second refueling, with a value of approximately 4.79%. Currently, nitrogen and heating air are used for defrosting and frost reduction, which can be costly. The developed machine learning models are expected to enable prediction of both frost formation and defrosting timings, potentially allowing for more cost-effective management of defrosting and frost reduction strategies. |
| format | Article |
| id | doaj-art-ffacf0a4bfad48ba9659537ef55df19c |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-ffacf0a4bfad48ba9659537ef55df19c2025-08-20T01:55:41ZengMDPI AGEnergies1996-10732024-11-011723596210.3390/en17235962Prediction of Freezing Time During Hydrogen Fueling Using Machine LearningJi-Ah Choi0Ji-Seong Jang1Sang-Won Ji2Department of Mechanical System Engineering, Grad. School of Pukyong National University, 45, Yongso-ro, Nam-gu, Busan 48513, Republic of KoreaDepartment of Mechanical System Engineering, Pukyong National University, 45, Yongso-ro, Nam-gu, Busan 48513, Republic of KoreaDepartment of Mechanical System Engineering, Pukyong National University, 45, Yongso-ro, Nam-gu, Busan 48513, Republic of KoreaThis study presents a method for predicting nozzle surface temperature and the timing of frost formation during hydrogen refueling using machine learning. A continuous refueling system was implemented based on a simulation model that was developed and validated in previous research. Data were collected under various boundary conditions, and eight regression models were trained and evaluated for their predictive performance. Hyperparameter optimization was performed using random search to enhance model performance. The final models were validated by applying boundary conditions not used during model development and comparing the predicted values with simulation results. The comparison revealed that the maximum error rate occurred after the second refueling, with a value of approximately 4.79%. Currently, nitrogen and heating air are used for defrosting and frost reduction, which can be costly. The developed machine learning models are expected to enable prediction of both frost formation and defrosting timings, potentially allowing for more cost-effective management of defrosting and frost reduction strategies.https://www.mdpi.com/1996-1073/17/23/5962nozzle freezingfrost formationhydrogen vehicle fuelingmachine learningprediction |
| spellingShingle | Ji-Ah Choi Ji-Seong Jang Sang-Won Ji Prediction of Freezing Time During Hydrogen Fueling Using Machine Learning Energies nozzle freezing frost formation hydrogen vehicle fueling machine learning prediction |
| title | Prediction of Freezing Time During Hydrogen Fueling Using Machine Learning |
| title_full | Prediction of Freezing Time During Hydrogen Fueling Using Machine Learning |
| title_fullStr | Prediction of Freezing Time During Hydrogen Fueling Using Machine Learning |
| title_full_unstemmed | Prediction of Freezing Time During Hydrogen Fueling Using Machine Learning |
| title_short | Prediction of Freezing Time During Hydrogen Fueling Using Machine Learning |
| title_sort | prediction of freezing time during hydrogen fueling using machine learning |
| topic | nozzle freezing frost formation hydrogen vehicle fueling machine learning prediction |
| url | https://www.mdpi.com/1996-1073/17/23/5962 |
| work_keys_str_mv | AT jiahchoi predictionoffreezingtimeduringhydrogenfuelingusingmachinelearning AT jiseongjang predictionoffreezingtimeduringhydrogenfuelingusingmachinelearning AT sangwonji predictionoffreezingtimeduringhydrogenfuelingusingmachinelearning |