Battery internal short circuit diagnosis based on vision transformer without real data

Hongchang Cai; Xuyang Liu; Lin Sun; Yuwen Xu; Yanan Wang; Xuebing Han; Yuejiu Zheng; Yuedong Sun; Minggao Ouyang

doi:10.59717/j.xinn-energy.2024.100041

The Innovation Energy > 2024 Vol. 1 > No. 3 > 100041

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Battery internal short circuit diagnosis based on vision transformer without real data

1.
School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
2.
State Key Laboratory of Intelligent Green Vehicle and Mobility, School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
3.
These authors contributed equally

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Corresponding author: yuejiu.zheng@usst.edu.cn
Received Date: May 30, 2024

Accepted Date: July 22, 2024

Published Online: August 15, 2024

The Innovation Energy 1(3), https://doi.org/10.59717/j.xinn-energy.2024.100041 | Cite this article

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1. Developing AI models for ISC diagnosis in lithium-ion batteries is challenging due to limited fault data.
  
  By extracting features from a mechanism model, AI model construction is achieved using only virtual data features.
  
  When applied to real ISC data, the model demonstrates high accuracy, robustness, and generalization.

ABSTRACT

ABSTRACT

The diagnosis of an internal short circuit (ISC) fault is an integral part of thermal runaway warning for lithium-ion batteries. A higher level of accuracy in ISC fault diagnosis needs an artificial intelligence model, but lack of fault data and label ambiguity present challenges. To address these demands and challenges, features are extracted using a mean difference model to amplify the difference between fault data and normal data, while reduce the inherent error between virtual data and real data. Additionally, the model considers the influence of other faults and the variability within the real data. The Vision Transformer model is then trained with only this virtual feature to achieve 100% accuracy when verifying real ISC fault data under constant current charging condition. By breaking away from the reliance on real fault data for modeling, this approach greatly reduces the cost of human labor, materials, time, and carbon emissions, and also provides a reference for other projects facing similar challenges.

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Cite this article:

Cai H., Liu X., Sun L., et al., (2024). Battery internal short circuit diagnosis based on vision transformer without real data. The Innovation Energy 1(3): 100041. https://doi.org/10.59717/j.xinn-energy.2024.100041

Cai H., Liu X., Sun L., et al., (2024). Battery internal short circuit diagnosis based on vision transformer without real data. The Innovation Energy 1(3): 100041. https://doi.org/10.59717/j.xinn-energy.2024.100041

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Battery internal short circuit diagnosis based on vision transformer without real data

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Battery internal short circuit diagnosis based on vision transformer without real data

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