The rapid growth of the electric vehicle (EV) and energy storage system (ESS) markets has driven a sharp increase in global demand for lithium-ion batteries (LIBs). While LIBs are recognized for their high energy density, long cycle life, and excellen...
The rapid growth of the electric vehicle (EV) and energy storage system (ESS) markets has driven a sharp increase in global demand for lithium-ion batteries (LIBs). While LIBs are recognized for their high energy density, long cycle life, and excellent charge-discharge efficiency, they are still vulnerable to various degradation phenomena over time. In particular, abnormal degradation, which occurs suddenly and unpredictably, is becoming a major concern due to its potential to cause critical performance loss and severe safety risks. As such, the ability to predict and prevent this abnormal degradation is essential for ensuring the reliability and safety of LIB-based systems.
Unlike gradual and predictable capacity fade caused by normal degradation, abnormal degradation is often triggered under specific operating conditions, leading to rapid performance loss and the initiation of side reactions such as lithium plating or electrolyte decomposition. These phenomena are typically invisible in early stages, making them difficult to diagnose with conventional methods. Therefore, this study aims to establish a comprehensive electrochemical strategy for the early diagnosis and prevention of abnormal degradation in lithium-ion batteries.
To this end, the electrochemical operating principles of LIBs were analyzed in detail to extract diagnostic indicators capable of detecting the onset of abnormal degradation during cycling. By monitoring current-voltage responses under various charge-discharge conditions, specific threshold-based markers were identified. These markers demonstrated superior accuracy and usability compared to conventional diagnostic approaches and enabled early-stage identification of abnormal behavior before observable capacity loss. Moreover, the simplicity and real-time nature of these indicators render them suitable for integration into battery management systems (BMS).
This research further proposes a dual-pronged strategy to prevent abnormal degradation. The first approach is based on the electrochemical characteristics of the anode during charging. It was experimentally confirmed that the solid-state lithium diffusivity in graphite anodes varies nonlinearly depending on the lithium intercalation stage. Such variation accelerates local lithium concentration gradients during fast charging, which in turn promotes lithium plating—a major trigger of abnormal degradation. Based on these findings, an optimized charging protocol was developed that accounts for the electrode’s transport limitations. Experimental validation revealed significant improvements in charging efficiency and long-term cycling performance, confirming the effectiveness of the approach.
The second approach focuses on charge kinetics, where a novel fast-charging protocol incorporating periodic discharge pulses was introduced. Continuous current application during fast charging can lead to excessive lithium accumulation at the anode surface, promoting plating. By embedding controlled discharge pulses into the charging process, the lithium concentration at the electrode surface was dynamically relaxed, thereby effectively suppressing lithium plating. This protocol proved effective across a range of C-rates and, when applied to large-format pouch cells, demonstrated marked improvements in cycle life and suppression of degradation compared to conventional fast-charging methods.
This study presents a unified electrochemical framework for both the early diagnosis and prevention of abnormal degradation in lithium-ion batteries. The proposed diagnostic indicators and charging control methodologies were designed with practical application in mind and hold significant potential for real-world deployment in high-performance, fast-charging LIB systems. Furthermore, the findings provide valuable insights for the development of advanced BMS algorithms and next-generation battery technologies where safety, reliability, and speed are equally prioritized.