A Survey on Deep Learning-Based RUL Prediction Techniques for Lithium-Ion Batteries

DOI

10.2991/978-94-6239-616-6_49How to use a DOI?

Keywords

Remaining Useful Life; Lithium-Ion Batteries; Data-Driven Prognostics; Deep Learning; Transformer; Attention Mechanism; Battery Health Prediction

Abstract

In modern technology, lithium-ion batteries play a key role in powering critical applications from portable electronics and electric vehicles to large-scale renewable energy. To ensure the safety, reliability, and cost-effectiveness of these systems, accurately predicting their Remaining Useful Life (RUL) for predictive maintenance and optimal lifecycle management is essential. However, precise RUL estimation is especially challenging due to complex, non-linear degradation mechanisms heavily influenced by variable real-world operating conditions. Deep Learning (DL) based approaches have recently emerged as a powerful solution, demonstrating a superior ability to capture these intricate degradation patterns and overcome the limitations of traditional physics-based or conventional data-driven models. This survey provides a comprehensive review of the evolution of RUL prediction techniques, classifying them into model-based or physics-based, traditional data-driven filter and ML, and advanced deep learning-based methods. Furthermore, this study reviews commonly available open-access datasets and discusses the various performance metrics used to benchmark and validate model accuracy. Finally, key research gaps, and future research directions are identified to guide further development in this rapidly growing domain.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - M. Murali
AU  - N. P. Subramaniam
PY  - 2026
DA  - 2026/03/31
TI  - A Survey on Deep Learning-Based RUL Prediction Techniques for Lithium-Ion Batteries
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 651
EP  - 662
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_49
DO  - 10.2991/978-94-6239-616-6_49
ID  - Murali2026
ER  -