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Battery State of Health Estimation and Incremental Capacity Analysis under Dynamic Charging Profile Using Neural Networks

Qinan Zhou, Gabrielle Vuylsteke

2024enneural networksbattery healthdegradationdynamic charging

Abstract

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Incremental capacity analysis (ICA) and differential voltage analysis (DVA) are recognized methods for battery degradation monitoring, but their real-world implementation has been constrained by the need for constant-current (CC) charging profiles. This research addresses this limitation by proposing a framework that extends ICA/DVA-based monitoring to dynamic charging profiles. A novel concept termed virtual incremental capacity (VIC) and virtual differential voltage (VDV) is introduced. Two convolutional neural networks (CNNs), U-Net and Conv-Net, are devised to generate VIC/VDV curves and accurately estimate the state of health (SOH) based on dynamic charging across various state-of-charge (SOC) ranges. To enhance computational efficiency, adaptations named Mobile U-Net and Mobile-Net serve as streamlined replacements for the original networks without compromising performance. The efficacy of the proposed CNNs is validated using a comprehensive experimental dataset comprising battery modules, demonstrating their ability to produce precise VIC/VDV curves and facilitate ICA/DVA-based battery degradation monitoring across diverse fast-charging protocols and SOC conditions.

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Cite This Work

@article{9ebb6b79-bbc5-49c0-b66c-a2dfcb73c0e6,
  title={Battery State of Health Estimation and Incremental Capacity Analysis under Dynamic Charging Profile Using Neural Networks},
  author={Qinan Zhou and Gabrielle Vuylsteke},
  year={2024},
  language={en}
}
TY  - JOUR
TI  - Battery State of Health Estimation and Incremental Capacity Analysis under Dynamic Charging Profile Using Neural Networks
AU  - Qinan Zhou
AU  - Gabrielle Vuylsteke
PY  - 2024
LA  - en
ER  -

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