Towards a smarter battery management system: A critical review on deep learning-based state of charge estimation of lithium-ion batteries
An accurate state of charge (SOC) estimation of lithium-ion batteries underpins a safe and optimized operation of the system. In recent years, deep learning-based SOC estimation has made significant progress. In order to provide researchers in this rapidly advancing field a comprehensive overview of...
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Elsevier
2025-09-01
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| Series: | Energy and AI |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S266654682500117X |
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| author | Jiaqi Yao Julia Kowal |
| author_facet | Jiaqi Yao Julia Kowal |
| author_sort | Jiaqi Yao |
| collection | DOAJ |
| description | An accurate state of charge (SOC) estimation of lithium-ion batteries underpins a safe and optimized operation of the system. In recent years, deep learning-based SOC estimation has made significant progress. In order to provide researchers in this rapidly advancing field a comprehensive overview of the state of the art, this paper carries out a structured review on deep learning-based SOC estimation of lithium-ion batteries. A detailed taxonomy of SOC estimation approaches and popularly used public datasets is provided as an introduction to the technical background. A systematic walk-through of the existing deep learning-based SOC estimation approaches, together with the frequently applied optimization strategies, is presented, where we also appeal for a standardized evaluation protocol in this field. As highlight, the current trends and emerging perspectives are pointed out and discussed in detail, including physics-informed neural networks (PINNs), multi-task learning (MTL), few-shot learning, and continual learning. We believe this work could not only provide the researchers and practitioners new to this topic with a clear and detailed manual to start with, but also point out the emerging perspectives for further cutting-edge studies towards a smarter battery management system. |
| format | Article |
| id | doaj-art-bb85ea370604489ea75dab45af8a95f8 |
| institution | Kabale University |
| issn | 2666-5468 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj-art-bb85ea370604489ea75dab45af8a95f82025-08-20T04:02:27ZengElsevierEnergy and AI2666-54682025-09-012110058510.1016/j.egyai.2025.100585Towards a smarter battery management system: A critical review on deep learning-based state of charge estimation of lithium-ion batteriesJiaqi Yao0Julia Kowal1Corresponding author.; Department of Electrical Energy Storage Technology (EET), Technische Universität Berlin, Einsteinufer 11, 10587, Berlin, GermanyDepartment of Electrical Energy Storage Technology (EET), Technische Universität Berlin, Einsteinufer 11, 10587, Berlin, GermanyAn accurate state of charge (SOC) estimation of lithium-ion batteries underpins a safe and optimized operation of the system. In recent years, deep learning-based SOC estimation has made significant progress. In order to provide researchers in this rapidly advancing field a comprehensive overview of the state of the art, this paper carries out a structured review on deep learning-based SOC estimation of lithium-ion batteries. A detailed taxonomy of SOC estimation approaches and popularly used public datasets is provided as an introduction to the technical background. A systematic walk-through of the existing deep learning-based SOC estimation approaches, together with the frequently applied optimization strategies, is presented, where we also appeal for a standardized evaluation protocol in this field. As highlight, the current trends and emerging perspectives are pointed out and discussed in detail, including physics-informed neural networks (PINNs), multi-task learning (MTL), few-shot learning, and continual learning. We believe this work could not only provide the researchers and practitioners new to this topic with a clear and detailed manual to start with, but also point out the emerging perspectives for further cutting-edge studies towards a smarter battery management system.http://www.sciencedirect.com/science/article/pii/S266654682500117XLithium-ion batteriesBattery management systemsState of charge estimationDeep learningNeural networks |
| spellingShingle | Jiaqi Yao Julia Kowal Towards a smarter battery management system: A critical review on deep learning-based state of charge estimation of lithium-ion batteries Energy and AI Lithium-ion batteries Battery management systems State of charge estimation Deep learning Neural networks |
| title | Towards a smarter battery management system: A critical review on deep learning-based state of charge estimation of lithium-ion batteries |
| title_full | Towards a smarter battery management system: A critical review on deep learning-based state of charge estimation of lithium-ion batteries |
| title_fullStr | Towards a smarter battery management system: A critical review on deep learning-based state of charge estimation of lithium-ion batteries |
| title_full_unstemmed | Towards a smarter battery management system: A critical review on deep learning-based state of charge estimation of lithium-ion batteries |
| title_short | Towards a smarter battery management system: A critical review on deep learning-based state of charge estimation of lithium-ion batteries |
| title_sort | towards a smarter battery management system a critical review on deep learning based state of charge estimation of lithium ion batteries |
| topic | Lithium-ion batteries Battery management systems State of charge estimation Deep learning Neural networks |
| url | http://www.sciencedirect.com/science/article/pii/S266654682500117X |
| work_keys_str_mv | AT jiaqiyao towardsasmarterbatterymanagementsystemacriticalreviewondeeplearningbasedstateofchargeestimationoflithiumionbatteries AT juliakowal towardsasmarterbatterymanagementsystemacriticalreviewondeeplearningbasedstateofchargeestimationoflithiumionbatteries |