Advancing Lithium-Ion Battery Health Prognostics With Deep Learning: A Review and Case Study

Abstract

Lithium-ion battery prognostics and health management (BPHM) systems are vital to the longevity, economy, and environmental friendliness of electric vehicles and energy storage systems. Recent advancements in deep learning (DL) techniques have shown promising results in addressing the challenges faced by the battery research and innovation community. This review article analyzes the mainstream developments in BPHM using DL techniques. The fundamental concepts of BPHM are discussed, followed by a detailed examination of the emerging DL techniques. A case study using a data-driven DLinear model for state of health estimation is introduced, achieving accurate forecasts with minimal data and high computational efficiency. Finally, the potential future pathways for research and development in BPHM are explored. This review offers a holistic understanding of emerging DL techniques in BPHM and provides valuable insights and guidance for future research endeavors.

Keywords

• Deep learning (DL)
• health and life-cycle analysis
• lithium-ion battery (LIB) management system
• prognostics and health management (PHM)
• remaining useful life (RUL) prediction
• state of charge (SOC) estimation