Abstract

For new energy vehicle components, remaining service life requires crucial maintenance, but operating conditions and incomplete fault records still limit the sustainable development of models. This study constructs a data-driven framework for batteries and traction motors by integrating fault analysis, reliability parameter estimation, and machine learning-based prediction. This framework can support maintenance planning and spare parts planning to some extent. However, these results should be interpreted with caution because component type, brand coverage, and data quality may introduce unobserved biases. More extensive cross-brand datasets, prediction ranges that account for uncertainty, and real-time validation are needed before reliable large-scale deployment.

Keywords

New Energy Vehicle ComponentsRemaining Useful LifeReliability AnalysisMachine LearningCondition-based Maintenance

Publication

Accepted 2026 July 31 ; Published 2026 August 7

Journal of Intelligence and Engineering Technology, 2026, 1(3), 3136-0939

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Metadata

DOI

10.70393/6a696574.343332

ARK

ark:/40704/JIET.v1n3a02

Pages

6–14

References

11

Disciplines

Intelligent Systems

Subjects

Other

Cite This Article

Acknowledgments

Not Applicable.

Funding

Not Applicable.

Institutional Review Board Statement

Not Applicable.

Data Availability Statement

Not Applicable.

Informed Consent Statement

Not Applicable.

Conflict of Interest

Not Applicable.

Author Contributions

Not application.

References

1. Wang, Z. (2024). Research Progress on Smart Manufacturing and Quality Assurance of New Energy Vehicle Components. JOURNAL OF PROGRESS IN ENGINEERING AND PHYSICAL SCIENCE Учредители: Aurora Publishing House Limited, 3(4), 56-65.
2. DOE, U. (2010). Multi-year research, development and demonstration plan: planned program activities for 2005-2015. US department of energy, office of energy efficiency and renewable energy, hydrogen, fuel cells and infrastructure technologies program (HFCIT), 1-34.
3. Zhang, S. S., Xu, K., & Jow, T. R. (2004). Electrochemical impedance study on the low temperature of Li-ion batteries. Electrochimica acta, 49(7), 1057-1061.
4. Duan, X., & Wang, Y. (2025). A Study on the Impact of Local Policy Response on the Technological Innovation of the New Energy Vehicle Industry. Sustainability, 17(19), 8873.
5. Xiong, R., Cao, J., Yu, Q., He, H., & Sun, F. (2017). Critical review on the battery state of charge estimation methods for electric vehicles. Ieee Access, 6, 1832-1843.
6. Jardine, A. K., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical systems and signal processing, 20(7), 1483-1510.
7. Chan, C. C. (2002). The state of the art of electric and hybrid vehicles. Proceedings of the IEEE, 90(2), 247-275.
8. Zhang, J., & Lee, J. (2011). A review on prognostics and health monitoring of Li-ion battery. Journal of power sources, 196(15), 6007-6014.
9. Huang, S. (2025). Measuring supply chain resilience with foundation time-series models. European Journal of Engineering and Technologies, 1(2), 49-56.
10. Luo, M., Du, B., Zhang, W., Song, T., Liu, K., Zhu, H., ... & Wen, H. (2023). Fleet rebalancing for expanding shared e-mobility systems: A multi-agent deep reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems, 24(4), 3868-3881.
11. Hao, Z. (2026). Low-Overhead Scheduling for Real-Time AI Workloads on Multi-Core Edge Chips. International Journal of Advance in Applied Science Research, 5(3), 15-25.

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Copyright © 2026 The Author(s). Published by Southern United Academy of Sciences.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.