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

Gaurav Shinde, Rohan Mohapatra, Pooja Krishan, Harish Garg, Srikanth Prabhu, Sanchari Das, Mohammad Masum, Saptarshi Sengupta

TL;DR

The paper addresses the challenge of prognostics and health management for lithium‑ion batteries in the cyber‑physical systems era by surveying RUL estimation methods across model‑based and data‑driven paradigms. It highlights the shift from physics and traditional ML toward deep learning with emphasis on data denoising, presenting architectures and datasets (e.g., NASA, CALCE) that improve battery health predictions. The work covers industrial applications in transportation, consumer electronics, and healthcare, and provides CPS‑centric insights, including threat landscapes, market dynamics, and vendor ecosystems. This synthesis aims to guide researchers and practitioners in selecting robust RUL approaches, addressing data scarcity, adversarial risks, and future research directions to enhance reliability and safety of Li‑ion battery systems within CPS.

Abstract

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.

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

TL;DR

The paper addresses the challenge of prognostics and health management for lithium‑ion batteries in the cyber‑physical systems era by surveying RUL estimation methods across model‑based and data‑driven paradigms. It highlights the shift from physics and traditional ML toward deep learning with emphasis on data denoising, presenting architectures and datasets (e.g., NASA, CALCE) that improve battery health predictions. The work covers industrial applications in transportation, consumer electronics, and healthcare, and provides CPS‑centric insights, including threat landscapes, market dynamics, and vendor ecosystems. This synthesis aims to guide researchers and practitioners in selecting robust RUL approaches, addressing data scarcity, adversarial risks, and future research directions to enhance reliability and safety of Li‑ion battery systems within CPS.

Abstract

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.
Paper Structure (33 sections, 9 equations, 12 figures, 5 tables)

This paper contains 33 sections, 9 equations, 12 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Lithium-ion Batteries
  3. Prognostics and Health Management(PHM)
  4. Remaining Useful Life
  5. Why is PHM so important?
  6. Review Methodology
  7. RUL Estimation Methods
  8. Model Based
  9. Physical Model
  10. Electrochemical Model
  11. Adaptive Filter
  12. Kalman Filter
  13. Particle Filter
  14. Stochastic Process
  15. Bayesian Estimation
  16. ...and 18 more sections

Figures (12)

  • Figure 1: Prognostics and health management framework
  • Figure 2: Organization of the paper
  • Figure 3: Particle filter process
  • Figure 4: Bayesian estimation process
  • Figure 5: An overview of a process involving Machine Learning for predicting RUL
  • ...and 7 more figures