Prediction and Optimization of Bolt Parameters for Automotive Fuel Cell Stack under Random Vibration
Proton exchange membrane fuel cells (PEMFCs) directly convert chemical energy into electrical energy, offering high energy conversion efficiency, zero emissions, low noise, and rapid response, making them highly promising for vehicular applications. However, during vehicle operation, PEMFC stacks ar...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/25/e3sconf_iceree2025_01019.pdf |
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| _version_ | 1849730419241517056 |
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| author | Yao Junqi Yin Yan Wang Bowen Zhang Fan Qin Zhikun Wang Zixuan Guo Ting |
| author_facet | Yao Junqi Yin Yan Wang Bowen Zhang Fan Qin Zhikun Wang Zixuan Guo Ting |
| author_sort | Yao Junqi |
| collection | DOAJ |
| description | Proton exchange membrane fuel cells (PEMFCs) directly convert chemical energy into electrical energy, offering high energy conversion efficiency, zero emissions, low noise, and rapid response, making them highly promising for vehicular applications. However, during vehicle operation, PEMFC stacks are inevitably subjected to random vibration loads due to road unevenness and internal system vibrations. These mechanical disturbances can induce structural displacement and deformation within the fuel cell stack, significantly affecting its durability and operational safety. Thus, this study conducts a random vibration analysis of fuel cell stacks under varying fastening bolt parameters. A dataset is constructed, comprising bolt parameters and the corresponding stress responses, which is then used to train an extreme gradient boosting (XGBoost) surrogate model. By integrating XGBoost with a genetic algorithm (GA), a GA-XGBoost predictive model is developed to optimize the structural parameters of the fastening bolts. The optimization results indicate that the optimal bolt diameter is 5.5 mm, the optimal nut thickness is 6.5 mm, and the optimal bolt-to-stack side distance is 10 mm. The predicted minimum stress is 52.351 Pa, representing a reduction of 2.849 Pa compared to the lowest stress observed in the dataset, thereby enhancing the structural durability of the fuel cell stack. |
| format | Article |
| id | doaj-art-15cbd5b6b67f4ead9c04f0d3fbd0ac25 |
| institution | DOAJ |
| issn | 2267-1242 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj-art-15cbd5b6b67f4ead9c04f0d3fbd0ac252025-08-20T03:08:52ZengEDP SciencesE3S Web of Conferences2267-12422025-01-016250101910.1051/e3sconf/202562501019e3sconf_iceree2025_01019Prediction and Optimization of Bolt Parameters for Automotive Fuel Cell Stack under Random VibrationYao Junqi0Yin Yan1Wang Bowen2Zhang Fan3Qin Zhikun4Wang Zixuan5Guo Ting6State Key Laboratory of Engines, Tianjin UniversityState Key Laboratory of Engines, Tianjin UniversityState Key Laboratory of Engines, Tianjin UniversityState Key Laboratory of Engines, Tianjin UniversityState Key Laboratory of Engines, Tianjin UniversityState Key Laboratory of Engines, Tianjin UniversityChina Automotive Technology & Research Center Co LtdProton exchange membrane fuel cells (PEMFCs) directly convert chemical energy into electrical energy, offering high energy conversion efficiency, zero emissions, low noise, and rapid response, making them highly promising for vehicular applications. However, during vehicle operation, PEMFC stacks are inevitably subjected to random vibration loads due to road unevenness and internal system vibrations. These mechanical disturbances can induce structural displacement and deformation within the fuel cell stack, significantly affecting its durability and operational safety. Thus, this study conducts a random vibration analysis of fuel cell stacks under varying fastening bolt parameters. A dataset is constructed, comprising bolt parameters and the corresponding stress responses, which is then used to train an extreme gradient boosting (XGBoost) surrogate model. By integrating XGBoost with a genetic algorithm (GA), a GA-XGBoost predictive model is developed to optimize the structural parameters of the fastening bolts. The optimization results indicate that the optimal bolt diameter is 5.5 mm, the optimal nut thickness is 6.5 mm, and the optimal bolt-to-stack side distance is 10 mm. The predicted minimum stress is 52.351 Pa, representing a reduction of 2.849 Pa compared to the lowest stress observed in the dataset, thereby enhancing the structural durability of the fuel cell stack.https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/25/e3sconf_iceree2025_01019.pdf |
| spellingShingle | Yao Junqi Yin Yan Wang Bowen Zhang Fan Qin Zhikun Wang Zixuan Guo Ting Prediction and Optimization of Bolt Parameters for Automotive Fuel Cell Stack under Random Vibration E3S Web of Conferences |
| title | Prediction and Optimization of Bolt Parameters for Automotive Fuel Cell Stack under Random Vibration |
| title_full | Prediction and Optimization of Bolt Parameters for Automotive Fuel Cell Stack under Random Vibration |
| title_fullStr | Prediction and Optimization of Bolt Parameters for Automotive Fuel Cell Stack under Random Vibration |
| title_full_unstemmed | Prediction and Optimization of Bolt Parameters for Automotive Fuel Cell Stack under Random Vibration |
| title_short | Prediction and Optimization of Bolt Parameters for Automotive Fuel Cell Stack under Random Vibration |
| title_sort | prediction and optimization of bolt parameters for automotive fuel cell stack under random vibration |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/25/e3sconf_iceree2025_01019.pdf |
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