# The State of Lithium-Ion Battery Health Prognostics in the CPS Era

arXiv: https://arxiv.org/abs/2403.19816 · HTML: https://mlsanity.com/paper/2403.19816/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 137 references that are in the map.
- First posted on arXiv 28 Mar 2024.

## Builds on (most cited first, 24 of 24)

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- [De-SaTE: Denoising Self-attention Transformer Encoders for Li-ion Battery Health Prognostics](https://mlsanity.com/paper/2310.00023/) (2023)

## Abstract (arXiv)

Lithium-ion batteries (Li-ion) have revolutionized energy storage technology, becoming integral to our daily lives by powering a diverse range of devices and applications. Their high energy density, fast power response, recyclability, and mobility advantages have made them the preferred choice for numerous sectors. This paper explores the seamless integration of Prognostics and Health Management within batteries, presenting a multidisciplinary approach that enhances the reliability, safety, and performance of these powerhouses. Remaining useful life (RUL), a critical concept in prognostics, is examined in depth, emphasizing its role in predicting component failure before it occurs. The paper reviews various RUL prediction methods, from traditional models to cutting-edge data-driven techniques. Furthermore, it highlights the paradigm shift toward deep learning architectures within the field of Li-ion battery health prognostics, elucidating the pivotal role of deep learning in addressing battery system complexities. Practical applications of PHM across industries are also explored, offering readers insights into real-world implementations.This paper serves as a comprehensive guide, catering to both researchers and practitioners in the field of Li-ion battery PHM.