Multi-modal framework for battery state of health evaluation using open-source electric vehicle data
Abstract Accurate, practical, and robust evaluation of the battery state of health is crucial to the efficient and reliable operation of electric vehicles. However, the limited availability of large-scale, high-quality field data hinders the development of the battery management system for state of...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-56485-7 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832571546769555456 |
|---|---|
| author | Hongao Liu Chang Li Xiaosong Hu Jinwen Li Kai Zhang Yang Xie Ranglei Wu Ziyou Song |
| author_facet | Hongao Liu Chang Li Xiaosong Hu Jinwen Li Kai Zhang Yang Xie Ranglei Wu Ziyou Song |
| author_sort | Hongao Liu |
| collection | DOAJ |
| description | Abstract Accurate, practical, and robust evaluation of the battery state of health is crucial to the efficient and reliable operation of electric vehicles. However, the limited availability of large-scale, high-quality field data hinders the development of the battery management system for state of health estimation, lifetime prediction, and fault detection in various applications. In this work, to gain insights into underlying factors limiting battery management system performance in real-world vehicles, we analyze the operational data of 300 diverse electric vehicles over three years to understand the disparities between field data and laboratory battery test data and their effect on state of health estimation. Furthermore, we propose a deep learning-based multi-modal framework to effectively leverage historical vehicle data for efficient, accurate, and cost-effective state of health estimation. The proposed paradigm exhibits considerable potential for numerous applications in state estimation and diagnostics in multi-sensor systems. Furthermore, we make the field data of these electric vehicles publicly available aiming to promote further research on the development of effective and reliable battery management systems for real-world vehicles. |
| format | Article |
| id | doaj-art-d03a228023ee40809c8efed47d09a8f9 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-d03a228023ee40809c8efed47d09a8f92025-02-02T12:31:53ZengNature PortfolioNature Communications2041-17232025-01-0116111210.1038/s41467-025-56485-7Multi-modal framework for battery state of health evaluation using open-source electric vehicle dataHongao Liu0Chang Li1Xiaosong Hu2Jinwen Li3Kai Zhang4Yang Xie5Ranglei Wu6Ziyou Song7State Key Laboratory of Intelligent Vehicle Safety TechnologyState Key Laboratory of Intelligent Vehicle Safety TechnologyCollege of Mechanical and Vehicle Engineering, Chongqing UniversityCollege of Mechanical and Vehicle Engineering, Chongqing UniversitySchool of Energy and Power Engineering, Chongqing UniversityState Key Laboratory of Intelligent Vehicle Safety TechnologyState Key Laboratory of Intelligent Vehicle Safety TechnologyDepartment of Mechanical Engineering, National University of SingaporeAbstract Accurate, practical, and robust evaluation of the battery state of health is crucial to the efficient and reliable operation of electric vehicles. However, the limited availability of large-scale, high-quality field data hinders the development of the battery management system for state of health estimation, lifetime prediction, and fault detection in various applications. In this work, to gain insights into underlying factors limiting battery management system performance in real-world vehicles, we analyze the operational data of 300 diverse electric vehicles over three years to understand the disparities between field data and laboratory battery test data and their effect on state of health estimation. Furthermore, we propose a deep learning-based multi-modal framework to effectively leverage historical vehicle data for efficient, accurate, and cost-effective state of health estimation. The proposed paradigm exhibits considerable potential for numerous applications in state estimation and diagnostics in multi-sensor systems. Furthermore, we make the field data of these electric vehicles publicly available aiming to promote further research on the development of effective and reliable battery management systems for real-world vehicles.https://doi.org/10.1038/s41467-025-56485-7 |
| spellingShingle | Hongao Liu Chang Li Xiaosong Hu Jinwen Li Kai Zhang Yang Xie Ranglei Wu Ziyou Song Multi-modal framework for battery state of health evaluation using open-source electric vehicle data Nature Communications |
| title | Multi-modal framework for battery state of health evaluation using open-source electric vehicle data |
| title_full | Multi-modal framework for battery state of health evaluation using open-source electric vehicle data |
| title_fullStr | Multi-modal framework for battery state of health evaluation using open-source electric vehicle data |
| title_full_unstemmed | Multi-modal framework for battery state of health evaluation using open-source electric vehicle data |
| title_short | Multi-modal framework for battery state of health evaluation using open-source electric vehicle data |
| title_sort | multi modal framework for battery state of health evaluation using open source electric vehicle data |
| url | https://doi.org/10.1038/s41467-025-56485-7 |
| work_keys_str_mv | AT hongaoliu multimodalframeworkforbatterystateofhealthevaluationusingopensourceelectricvehicledata AT changli multimodalframeworkforbatterystateofhealthevaluationusingopensourceelectricvehicledata AT xiaosonghu multimodalframeworkforbatterystateofhealthevaluationusingopensourceelectricvehicledata AT jinwenli multimodalframeworkforbatterystateofhealthevaluationusingopensourceelectricvehicledata AT kaizhang multimodalframeworkforbatterystateofhealthevaluationusingopensourceelectricvehicledata AT yangxie multimodalframeworkforbatterystateofhealthevaluationusingopensourceelectricvehicledata AT rangleiwu multimodalframeworkforbatterystateofhealthevaluationusingopensourceelectricvehicledata AT ziyousong multimodalframeworkforbatterystateofhealthevaluationusingopensourceelectricvehicledata |