Battery Prognostics and Health Management: AI and Big Data
In the Industry 4.0 era, integrating artificial intelligence (AI) with battery prognostics and health management (PHM) offers transformative solutions to the challenges posed by the complex nature of battery systems. These systems, known for their dynamic and nonl*-inear behavior, often exceed the c...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/16/1/10 |
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| author | Di Li Jinrui Nan Andrew F. Burke Jingyuan Zhao |
| author_facet | Di Li Jinrui Nan Andrew F. Burke Jingyuan Zhao |
| author_sort | Di Li |
| collection | DOAJ |
| description | In the Industry 4.0 era, integrating artificial intelligence (AI) with battery prognostics and health management (PHM) offers transformative solutions to the challenges posed by the complex nature of battery systems. These systems, known for their dynamic and nonl*-inear behavior, often exceed the capabilities of traditional PHM approaches, which struggle to account for the interplay of multiple physical domains and scales. By harnessing technologies such as big data analytics, cloud computing, the Internet of Things (IoT), and deep learning, AI provides robust, data-driven solutions for capturing and predicting battery degradation. These advancements address long-standing limitations in battery prognostics, enabling more accurate and reliable performance assessments. The convergence of AI with Industry 4.0 technologies not only resolves existing challenges but also introduces innovative approaches that enhance the adaptability and precision of battery health management. This perspective highlights recent progress in battery PHM and explores the shift from traditional methods to AI-powered, data-centric frameworks. By enabling more precise and scalable monitoring and prediction of battery health, this transition marks a significant step forward in advancing the field. |
| format | Article |
| id | doaj-art-576406bf55034e4890ae3968239f4bf5 |
| institution | Kabale University |
| issn | 2032-6653 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj-art-576406bf55034e4890ae3968239f4bf52025-01-24T13:52:45ZengMDPI AGWorld Electric Vehicle Journal2032-66532024-12-011611010.3390/wevj16010010Battery Prognostics and Health Management: AI and Big DataDi Li0Jinrui Nan1Andrew F. Burke2Jingyuan Zhao3Hubei Longzhong Laboratory, Hubei University of Arts and Science, Xiangyang 441000, ChinaShenzhen Automotive Research Institute, Beijing Institute of Technology, Shenzhen 518000, ChinaInstitute of Transportation Studies, University of California Davis, Davis, CA 95616, USAInstitute of Transportation Studies, University of California Davis, Davis, CA 95616, USAIn the Industry 4.0 era, integrating artificial intelligence (AI) with battery prognostics and health management (PHM) offers transformative solutions to the challenges posed by the complex nature of battery systems. These systems, known for their dynamic and nonl*-inear behavior, often exceed the capabilities of traditional PHM approaches, which struggle to account for the interplay of multiple physical domains and scales. By harnessing technologies such as big data analytics, cloud computing, the Internet of Things (IoT), and deep learning, AI provides robust, data-driven solutions for capturing and predicting battery degradation. These advancements address long-standing limitations in battery prognostics, enabling more accurate and reliable performance assessments. The convergence of AI with Industry 4.0 technologies not only resolves existing challenges but also introduces innovative approaches that enhance the adaptability and precision of battery health management. This perspective highlights recent progress in battery PHM and explores the shift from traditional methods to AI-powered, data-centric frameworks. By enabling more precise and scalable monitoring and prediction of battery health, this transition marks a significant step forward in advancing the field.https://www.mdpi.com/2032-6653/16/1/10batteryAIprognosticsbattery healthmachine learningdeep learning |
| spellingShingle | Di Li Jinrui Nan Andrew F. Burke Jingyuan Zhao Battery Prognostics and Health Management: AI and Big Data World Electric Vehicle Journal battery AI prognostics battery health machine learning deep learning |
| title | Battery Prognostics and Health Management: AI and Big Data |
| title_full | Battery Prognostics and Health Management: AI and Big Data |
| title_fullStr | Battery Prognostics and Health Management: AI and Big Data |
| title_full_unstemmed | Battery Prognostics and Health Management: AI and Big Data |
| title_short | Battery Prognostics and Health Management: AI and Big Data |
| title_sort | battery prognostics and health management ai and big data |
| topic | battery AI prognostics battery health machine learning deep learning |
| url | https://www.mdpi.com/2032-6653/16/1/10 |
| work_keys_str_mv | AT dili batteryprognosticsandhealthmanagementaiandbigdata AT jinruinan batteryprognosticsandhealthmanagementaiandbigdata AT andrewfburke batteryprognosticsandhealthmanagementaiandbigdata AT jingyuanzhao batteryprognosticsandhealthmanagementaiandbigdata |