Artificial Intelligence-based health diagnostic of Lithium-ion battery leveraging transient stage of constant current and constant voltage charging

Haokai Ruan, Zhongbao Wei, Wentao Shang, Xuechao Wang and Hongwen He

Applied Energy, 2023, vol. 336, issue C, No S0306261923001150

Abstract: State of health (SOH) estimation is essential to the health diagnostic of lithium-ion battery. The data-driven approach with charging feature extraction is promising for online SOH estimation and has been widely explored over years. However, their deployment can be barriered by the lack of complete charging data in real-world applications. Motivated by this, this paper proposes an artificial intelligence-based SOH estimator using the transient phase between constant current (CC) and constant voltage (CV) charging, which is easily obtained in real-world charging scenarios. Specifically, a convolutional neural network (CNN) model is proposed to explain the relationship between the charging data and the SOH. Following this endeavor, the transfer learning is exploited for model mitigation and SOH estimation on different battery types, relying on much reduced amount of data for efficient CNN model re-training. The validation experiments are conducted based on the aging data obtained on LiNiCoAlO2 (NCA) and LiCoO2 (LCO) cells. Results suggest that the proposed method realizes accurate SOH estimation requiring only a short segment from the CC-CV transient phase, so that can meet a broad range of real-world charging scenarios. Moreover, the efficient model transfer promises expected performance with different battery types.

Keywords: Lithium-ion battery; State of health; Convolutional neural network; Transfer learning (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (16)

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DOI: 10.1016/j.apenergy.2023.120751

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