  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.
