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.

研究论文探讨了锂离子电池（Li-on）与预测与健康管理的无缝集成，提供了一种多学科方法，增强了这些动力源的可靠性、安全性和性能。重点深入探讨了剩余寿命（RUL），强调其在预测组件故障之前的作用。论文综述了各种 RUL 预测方法，从传统模型到尖端的数据驱动技术。此外，它强调了深度学习架构在锂离子电池健康预测领域中的转变，解释了深度学习在解决电池系统复杂性中的关键作用。还探讨了 PHM 在各行业的实际应用，为读者提供了对实际实施的洞察。该论文旨在成为锂离子电池 PHM 领域研究人员和实践者的全面指南。