Battery Housing for Electric Vehicles, a Durability Assessment Review
Recent research emphasizes the growing use of advanced composite materials in modern transportation, highlighting their superior weight-to-strength ratio. These materials are increasingly replacing steel and aluminium in housings to enhance sustainability, improve efficiency, and reduce emissions. C...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Designs |
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| Online Access: | https://www.mdpi.com/2411-9660/8/6/113 |
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| author | Moises Jimenez-Martinez José Luis Valencia-Sánchez Sergio G. Torres-Cedillo Jacinto Cortés-Pérez |
| author_facet | Moises Jimenez-Martinez José Luis Valencia-Sánchez Sergio G. Torres-Cedillo Jacinto Cortés-Pérez |
| author_sort | Moises Jimenez-Martinez |
| collection | DOAJ |
| description | Recent research emphasizes the growing use of advanced composite materials in modern transportation, highlighting their superior weight-to-strength ratio. These materials are increasingly replacing steel and aluminium in housings to enhance sustainability, improve efficiency, and reduce emissions. Considering these advancements, this article reviews recent studies on composite materials, focusing on fatigue life assessment models. These models, which include performance degradation, progressive damage, and S–N curve models, are essential for ensuring the reliability of composite materials. It is noted that the fatigue damage process in composite materials is complex, as failure can occur in the matrix, reinforcement, or transitions such as interlaminar and intralaminar delamination. Additionally, the article critically examines the integration of artificial intelligence techniques for predicting the fatigue life of composite materials, offering a comprehensive analysis of methods used to indicate the mechanical properties of battery shell composites. Incorporating neural networks into fatigue life analysis significantly enhances prediction reliability. However, the model’s accuracy depends heavily on the comprehensive data it includes, including material properties, loading conditions, and manufacturing processes, which help to reduce variability and ensure the precision of the predictions. This research underscores the importance of continued advancements and their significant scientific contributions to transportation sustainability, especially in the context of emerging artificial intelligence technologies. |
| format | Article |
| id | doaj-art-b2f5957a3f5b47fa8cc8a60dacb8aa19 |
| institution | Kabale University |
| issn | 2411-9660 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Designs |
| spelling | doaj-art-b2f5957a3f5b47fa8cc8a60dacb8aa192024-12-27T14:20:34ZengMDPI AGDesigns2411-96602024-10-018611310.3390/designs8060113Battery Housing for Electric Vehicles, a Durability Assessment ReviewMoises Jimenez-Martinez0José Luis Valencia-Sánchez1Sergio G. Torres-Cedillo2Jacinto Cortés-Pérez3Tecnologico de Monterrey, School of Engineering and Sciences, Via Atlixcayotl 5718, Puebla 72453, MexicoTecnologico de Monterrey, School of Engineering and Sciences, Via Atlixcayotl 5718, Puebla 72453, MexicoCentro Tecnológico FES Aragón, Universidad Nacional Autónoma de México, Nezahualcóyotl 57171, MexicoCentro Tecnológico FES Aragón, Universidad Nacional Autónoma de México, Nezahualcóyotl 57171, MexicoRecent research emphasizes the growing use of advanced composite materials in modern transportation, highlighting their superior weight-to-strength ratio. These materials are increasingly replacing steel and aluminium in housings to enhance sustainability, improve efficiency, and reduce emissions. Considering these advancements, this article reviews recent studies on composite materials, focusing on fatigue life assessment models. These models, which include performance degradation, progressive damage, and S–N curve models, are essential for ensuring the reliability of composite materials. It is noted that the fatigue damage process in composite materials is complex, as failure can occur in the matrix, reinforcement, or transitions such as interlaminar and intralaminar delamination. Additionally, the article critically examines the integration of artificial intelligence techniques for predicting the fatigue life of composite materials, offering a comprehensive analysis of methods used to indicate the mechanical properties of battery shell composites. Incorporating neural networks into fatigue life analysis significantly enhances prediction reliability. However, the model’s accuracy depends heavily on the comprehensive data it includes, including material properties, loading conditions, and manufacturing processes, which help to reduce variability and ensure the precision of the predictions. This research underscores the importance of continued advancements and their significant scientific contributions to transportation sustainability, especially in the context of emerging artificial intelligence technologies.https://www.mdpi.com/2411-9660/8/6/113battery housingartificial intelligencecomposites materialsfatigue |
| spellingShingle | Moises Jimenez-Martinez José Luis Valencia-Sánchez Sergio G. Torres-Cedillo Jacinto Cortés-Pérez Battery Housing for Electric Vehicles, a Durability Assessment Review Designs battery housing artificial intelligence composites materials fatigue |
| title | Battery Housing for Electric Vehicles, a Durability Assessment Review |
| title_full | Battery Housing for Electric Vehicles, a Durability Assessment Review |
| title_fullStr | Battery Housing for Electric Vehicles, a Durability Assessment Review |
| title_full_unstemmed | Battery Housing for Electric Vehicles, a Durability Assessment Review |
| title_short | Battery Housing for Electric Vehicles, a Durability Assessment Review |
| title_sort | battery housing for electric vehicles a durability assessment review |
| topic | battery housing artificial intelligence composites materials fatigue |
| url | https://www.mdpi.com/2411-9660/8/6/113 |
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