A novel feature combination screening method for Li-ion battery state of health prediction

Yuntian Pei; Liqun Chen; Zhang Chen; Jinhui Zhou; Wenjing Shen

doi:10.1117/12.3009591

16 October 2023 A novel feature combination screening method for Li-ion battery state of health prediction

Yuntian Pei, Liqun Chen, Zhang Chen, Jinhui Zhou, Wenjing Shen

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 128033T (2023) https://doi.org/10.1117/12.3009591
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

Accurately predicting the state of health (SOH) of Li-ion batteries is beneficial for application management. Here, we propose an optimization scheme for feature combination (FC) to enhance the performance of the SOH prediction model. In this article, we first extract features from the partial charging voltage data of the battery for model input. Then, the Pearson correlation coefficient is combined with a machine learning model to optimize the selection of potential FCs and obtain the optimal FC. Finally, the optimal FC is applied to the SOH prediction tasks. The proposed scheme achieved significant performance improvement in multiple testing tasks and outperformed other methods, demonstrating its effectiveness and superiority.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuntian Pei, Liqun Chen, Zhang Chen, Jinhui Zhou, and Wenjing Shen "A novel feature combination screening method for Li-ion battery state of health prediction", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 128033T (16 October 2023); https://doi.org/10.1117/12.3009591

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KEYWORDS

Data modeling

Feature extraction

Batteries

Fluorescence correlation spectroscopy

Mathematical modeling

Education and training

Machine learning

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