Conference proceeding
Battery State of Health Estimation Using LLM Framework
Proceedings / IEEE International Symposium on Quality Electronic Design, pp 1-8
23 Apr 2025
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Battery health monitoring is critical for the efficient and reliable operation of electric vehicles (EVs). This study introduces a transformer-based framework for estimating the State of Health (SoH) and predicting the Remaining Useful Life (RUL) of lithium titanate (LTO) battery cells by utilizing both cycle-based and instantaneous discharge data. Testing on eight LTO cells under various cycling conditions over 500 cycles, we demonstrate the impact of charge durations on energy storage trends and apply Differential Voltage Analysis (DVA) to monitor capacity changes (dQ/dV) across voltage ranges. Our LLM model achieves superior performance, with a Mean Absolute Error (MAE) as low as 0.87 % and varied latency metrics that support efficient processing, demonstrating its strong potential for real-time integration into EVs. The framework effectively identifies early signs of degradation through anomaly detection in high-resolution data, facilitating predictive maintenance to prevent sudden battery failures and enhance energy efficiency.
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Details
- Title
- Battery State of Health Estimation Using LLM Framework
- Creators
- Aybars Yunusoglu - Purdue University West LafayetteDexter Le - Drexel UniversityMurat Isik - Stanford UniversityKarn Tiwari - Indian Institute of Science BangaloreI. Can Dikmen - Temsa Research & Development Center,Adana,TurkeyTeoman Karadag - Temsa Research & Development Center,Adana,Turkey
- Publication Details
- Proceedings / IEEE International Symposium on Quality Electronic Design, pp 1-8
- Publisher
- IEEE
- Number of pages
- 8
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering; Computer Science
- Web of Science ID
- WOS:001552227300108
- Scopus ID
- 2-s2.0-105007559376
- Other Identifier
- 991022197428704721
UN Sustainable Development Goals (SDGs)
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Hardware & Architecture
- Engineering, Electrical & Electronic