Battery State of Health Estimation Using LLM Framework

• URL: http://arxiv.org/abs/2501.18123v1
• Date: Thu, 30 Jan 2025 03:55:56 GMT
• Title: Battery State of Health Estimation Using LLM Framework
• Authors: Aybars Yunusoglu, Dexter Le, Karn Tiwari, Murat Isik, I. Can Dikmen,
• Abstract summary: This study introduces a transformer-based framework for estimating the State of Health (SoH) of lithium titanate (LTO) battery cells. We demonstrate the impact of charge durations on energy storage trends and apply Differential Voltage Analysis (DVA) to monitor capacity changes. Our model achieves superior performance, with a Mean Absolute Error (MAE) as low as 0.87% and varied latency metrics.
• Score: 0.0
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• 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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