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

arXiv: https://arxiv.org/abs/2409.00141 · HTML: https://mlsanity.com/paper/2409.00141/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 30 Aug 2024.

## Builds on (most cited first, 7 of 7)

- [Graph Neural Network-Based Anomaly Detection in Multivariate Time Series](https://mlsanity.com/paper/2106.06947/) (2021)
- [A Transfer Learning-based State of Charge Estimation for Lithium-Ion Battery at Varying Ambient Temperatures](https://mlsanity.com/paper/2101.03704/) (2021)
- [Nonlinear Traffic Prediction as a Matrix Completion Problem with Ensemble Learning](https://mlsanity.com/paper/2001.02492/) (2020)
- [IIoT-Enabled Health Monitoring for Integrated Heat Pump System Using Mixture Slow Feature Analysis](https://mlsanity.com/paper/2104.09876/) (2021)
- [Lithium-ion Battery State of Health Estimation based on Cycle Synchronization using Dynamic Time Warping](https://mlsanity.com/paper/2109.13448/) (2021)
- [Adaptive Filters in Graph Convolutional Neural Networks](https://mlsanity.com/paper/2105.10377/) (2021)
- [Digital Twin for Real-time Li-ion Battery State of Health Estimation with Partially Discharged Cycling Data](https://mlsanity.com/paper/2212.04622/) (2022)

## Built on by (most cited first, 1 of 1)

- [TIDE: Trustworthy and Interpretable Battery Degradation Estimation with Contextual Learning and Symbolic Distillation](https://mlsanity.com/paper/2607.14640/) (2026)

## Abstract (arXiv)

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%.