Graph neural network-based lithium-ion battery state of health estimation using partial discharging curve

TL;DR

This paper presents a novel graph neural network approach that automatically selects discharge voltage segments for accurate lithium-ion battery SOH estimation, effectively capturing degradation dynamics and reducing distribution mismatch.

Contribution

It introduces an automated segment selection method using Matrix Profile and integrates it with GCNs for improved SOH estimation, addressing manual selection and distribution issues.

Findings

Achieves less than 1% RMSE in SOH estimation

Automatically selects voltage segments with Matrix Profile

Effectively captures inter-cycle degradation dynamics

Abstract

Data-driven methods have gained extensive attention in estimating the state of health (SOH) of lithium-ion batteries. Accurate SOH estimation requires degradation-relevant features and alignment of statistical distributions between training and testing datasets. However, current research often overlooks these needs and relies on arbitrary voltage segment selection. To address these challenges, this paper introduces an innovative approach leveraging spatio-temporal degradation dynamics via graph convolutional networks (GCNs). Our method systematically selects discharge voltage segments using the Matrix Profile anomaly detection algorithm, eliminating the need for manual selection and preventing information loss. These selected segments form a fundamental structure integrated into the GCN-based SOH estimation model, capturing inter-cycle dynamics and mitigating statistical distribution incongruities between offline training and online testing data. Validation with a widely accepted open-source dataset demonstrates that our method achieves precise SOH estimation, with a root mean squared error of less than 1%.