Battery State of Health Estimation Using LLM Framework

TL;DR

This paper presents a transformer-based framework for estimating battery health and predicting lifespan in electric vehicles, demonstrating high accuracy and real-time capabilities through extensive testing on lithium titanate cells.

Contribution

Introduces a novel transformer-based model for battery SoH and RUL estimation using cycle and instantaneous data, with superior accuracy and real-time performance.

Findings

Achieves MAE as low as 0.87% in SoH estimation

Demonstrates effective early degradation detection via anomaly analysis

Supports real-time battery health monitoring in EVs

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.