Battery State of Health Estimation Using LLM Framework

2025-01-30

AI Summary

To address the low accuracy in joint state-of-health (SoH) and remaining useful life (RUL) prediction for electric vehicle batteries, as well as weak early-failure warning capability, this paper proposes the first large language model (LLM)-based framework tailored for battery degradation modeling. Built upon the Transformer architecture, it jointly encodes cyclic aging data and instantaneous discharge voltage curves. Innovatively integrating differential voltage analysis (DVA) and high-resolution dQ/dV feature extraction, the method enables deep, multi-source temporal data fusion. The resulting model supports lightweight onboard deployment and real-time inference. Evaluated on eight lithium-titanate-oxide (LTO) battery cells over 500 cycles, it achieves a mean absolute error of only 0.87% in SoH estimation—demonstrating superior generalization and enhanced early anomaly detection. This work validates the feasibility and advancement of LLMs in intelligent battery health management.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Electric Vehicle Battery
• Health Monitoring
• Remaining Useful Life Prediction

Innovation

• Transformer Framework
• Differential Voltage Analysis
• Battery Health Monitoring