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URN to cite this document: urn:nbn:de:bvb:703-epub-9100-1
Title data
Hadzalic, Nejira ; Hamar, Jacob ; Fischer, Marco ; Erhard, Simon ; Schmidt, Jan Philipp:
Semi-supervised battery state of health estimation for field applications.
In: Energy and AI.
Vol. 22
(2025)
.
- 100575.
ISSN 2666-5468
DOI der Verlagsversion: https://doi.org/10.1016/j.egyai.2025.100575
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Abstract
Battery electric vehicles are exposed to highly diverse operating conditions and driving behaviors that strongly influence degradation pathways, yet these real-world complexities are only partially captured in laboratory aging tests. This study investigates a semi-supervised learning approach for robust estimation of battery state of health, defined as the ratio of remaining to nominal capacity. The method integrates a multi-view co-training algorithm with a rule-based pseudo labeling mechanism and is developed and validated using field data from 3000 BMW i3 vehicles with battery capacity of 60Ah, collected since 2013 across 34 countries. The available data comprises standardized full charge capacity measurements, which serve as ground truth labels. The proposed training and validation pipeline is designed to address challenges inherent in real-world data generation and is particularly advantageous during early deployment of new battery technologies, when labeled data is scarce. By incrementally incorporating newly available labeled data into both evaluation and retraining, the model adapts to heterogeneous aging patterns observed in the field. Comparative analysis demonstrates that, relative to a supervised benchmark, the proposed method reduces estimation error by 28 under limited-label conditions and by 6 under optimally labeled scenarios, highlighting its robustness for field applications.
Further data
| Item Type: | Article in a journal |
|---|---|
| Keywords: | Lithium-ion battery; State of health estimation; Semi-supervised learning; Field data; Machine learning |
| DDC Subjects: | 600 Technology, medicine, applied sciences > 620 Engineering |
| Institutions of the University: | Faculties > Faculty of Engineering Science > Chair Systems Engineering for Electrical Energy Storage > Chair Systems Engineering for Electrical Energy Storage - Univ.-Prof. Dr.-Ing. Jan Philipp Schmidt Research Institutions > Central research institutes > Bayerisches Zentrum für Batterietechnik - BayBatt Faculties Faculties > Faculty of Engineering Science Faculties > Faculty of Engineering Science > Chair Systems Engineering for Electrical Energy Storage Research Institutions Research Institutions > Central research institutes |
| Language: | English |
| Originates at UBT: | Yes |
| URN: | urn:nbn:de:bvb:703-epub-9100-1 |
| Date Deposited: | 10 Apr 2026 12:20 |
| Last Modified: | 10 Apr 2026 12:21 |
| URI: | https://epub.uni-bayreuth.de/id/eprint/9100 |
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