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

• URL: http://arxiv.org/abs/2409.00141v1
• Date: Fri, 30 Aug 2024 02:14:53 GMT
• Title: Graph neural network-based lithium-ion battery state of health estimation using partial discharging curve
• Authors: Kate Qi Zhou, Yan Qin, Chau Yuen,
• Abstract summary: This paper introduces an innovative approach leveraging graph-temporal networks (GCNs) to estimate state of health (SOH) of lithium-ion batteries. Our method systematically selects discharge voltage segments using the Matrix Profile anomaly detection algorithm. 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%.
• Score: 16.570091013381266
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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%.

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