A Holistic Lifecycle-Oriented Analysis of High-Performance Lithium-Ion Batteries: Aging, Advanced Diagnostics, Thermal Management, and Digitalized Education

Abstract

Lithium-ion batteries (LIBs) have become central to modern energy storage systems, yet their increasing material and electrochemical complexity introduces significant challenges across their entire lifecycle. During first-life operation, high-performance applications such as fast charging require advanced and adaptive battery management systems (BMS) capable of real-time control, thermal regulation, and safety assurance. As batteries age, performance degradation mechanisms such as capacity fade, impedance growth, and thermal imbalance necessitate sophisticated diagnostic techniques, state-of-health estimation, and robust balancing strategies to enable reliable second-life applications. Given this growing complexity, traditional hardware-based training and evaluation methods are no longer sufficient. Instead, there is a critical need for data-driven, physics-informed digital environments that replicate real-world battery behavior. Digital twin frameworks and virtual battery laboratories provide a safe, scalable, and interactive platform for both system optimization and workforce education. This paper proposes a unified, three-tier framework encompassing a physical aging layer, a custom edge-computing hardware layer, and a virtual reality educational layer to achieve holistic lifecycle battery management. Within the physical-aging layer, spinel LiMn2O4 (LMO) is used as a chemistry-specific case study linking cathode degradation mechanisms and materials-level mitigation strategies to full-cell lifecycle behavior.