Graph neural network-based lithium-ion battery state of health estimation using partial discharging curve
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 o...
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Main Authors: | , , |
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Format: | Article |
Language: | English |
Published: |
2024
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Subjects: | |
Online Access: | https://hdl.handle.net/10356/180800 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | 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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