Research on Battery Health Status Estimation and Remaining Life Prediction Based on a Novel Inception-BiLSTM Model

Jiangjiang  Li; Lijuan  Feng; Qingmin  Song; Xianan  Song; Jiaxiang  Wang

doi:10.6180/jase.202608_31.009

Research on Battery Health Status Estimation and Remaining Life Prediction Based on a Novel Inception-BiLSTM Model

Jiangjiang Li, Lijuan FengThis email address is being protected from spambots. You need JavaScript enabled to view it., Qingmin Song, Xianan Song, and Jiaxiang Wang

School of Electronics and Electrical Engineering, Zhengzhou University of Science and Technology, Zhengzhou 450064 China

Received: October 11, 2025
Accepted: December 11, 2025
Publication Date: February 3, 2026

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202608_31.009

To address the issues such as the single extraction of health features and the low estimation accuracy in the process of estimating the health status and predicting the remaining service life of lithium batteries, this paper proposes a novel battery health status estimation and remaining life prediction method based on Inception BiLSTM model. By analyzing the state during the charging process of lithium batteries, nine health factors are extracted. The strongly correlated health factors are selected through the Pearson correlation coefficient and are used as the input of the model. The initial position of the dung beetle is generated using the chaotic initialization tent mapping, and the position of the thief dung beetle is optimized using the sine and cosine strategy, which solves the local convergence problem caused by the initialization of the DBO (Dung Beetle Optimizer) algorithm and optimizes the balance of the DBO algorithm, thereby improving the stability of the prediction. Practical trials manifest that, against the backdrop of current sophisticated prognostic models, the fresh design yields more accurate residual-life outlooks for lithium battery packs.

Keywords: battery health status estimation, remaining life prediction, Inception-BiLSTM, Pearson correlation coefficient, DBO

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