Fault Prediction and Health Assessment of New Energy Lithium-Ion Batteries

Huaijia Deng

doi:10.54254/2755-2721/2026.MH28998

Applied and Computational EngineeringOpen access

Fault Prediction and Health Assessment of New Energy Lithium-Ion Batteries

Research Article

Open Access

Fault Prediction and Health Assessment of New Energy Lithium-Ion Batteries

Huaijia Deng ^1*

¹ Shanghai Thomas School

^*Corresponding author: Rfzzl91@163.com

Published on 5 November 2025

ACE Vol.205

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-521-9

ISBN (Online): 978-1-80590-522-6

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Abstract

As the new energy vehicle sector advances at an accelerated pace, lithium-ion batteries have grown far more prevalent across electric vehicles, energy storage systems, and portable electronic devices. The State of Health (SOH) of lithium batteries bears direct implications for their safety, performance stability, and operational lifespan. For this reason, Prognostics and Health Management (PHM) technology tailored to lithium batteries has steadily become a key area of focus in both academic inquiries and industrial applications. This paper systematically reviews the research progress of lithium battery PHM technology in recent years, mainly covering key methods such as battery thermal state characterization indicators, Physics-Informed Neural Network (PINN), and Integrated Sparse Gaussian Process Regression (SGPR). This paper not only summarized the core principles and applications of each technology, but also analyzed its shortcomings and proposes several improvement directions. This paper provided a reference for future research on SOH prediction and health management of lithium-ion batteries.

Keywords:

Lithiun-ion Batteries, Failure Prediction, PHM Technique, SOH

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References

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References

[3]. Wang, F., Zhai, Z., Zhao, Z., Di, Y. and Chen, X. (2024) Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis. Nature Communications, 15(1), 4332.

[5]. Feng, X., Ouyang, M., Liu, X., Lu, L., Xia, Y. and He, X. (2018) Thermal runaway mechanism of lithium ion battery for electric vehicles: A review. Energy Storage Materials, 10, 246-267.

[6]. Feng, X., Ren, D., He, X. and Ouyang, M. (2020) Mitigating thermal runaway of lithium-ion batteries. Joule, 4(4), 743-770.

Cite this article

Deng,H. (2025). Fault Prediction and Health Assessment of New Energy Lithium-Ion Batteries. Applied and Computational Engineering,205,1-8.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-MCEE 2026 Symposium: Geomaterials and Environmental Engineering

ISBN: 978-1-80590-521-9(Print) / 978-1-80590-522-6(Online)

Editor: Ömer Burak İSTANBULLU, Manoj Khandelwal

Conference website: https://www.confmcee.org/mounthelen.html

Conference date: 21 January 2026

Series: Applied and Computational Engineering

Volume number: Vol.205

ISSN: 2755-2721(Print) / 2755-273X(Online)

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