Advancing Li-plating detection: Motivating a multi-signal correlation approach

Abstract

Facilitating fast charging in lithium-ion batteries (LiBs) is often linked to Li-plating, which harms performance, longevity, and safety. Early detection of Li-plating is essential for rapid technological development and for preventing performance deterioration and ensuring safety during operation. Fast and real-time detection, using commonly collected measurements like voltage (V), current (I), temperature (T), and pressure (P), is highly desirable. Existing standalone methods relying on electrochemical and mechanical signatures, using half, smaller, or specially designed cells often operated at lower temperatures, fail to account for real-world fast-charging conditions. These signatures may also have inherent unreliability in aged LiBs, a phenomenon currently not-well understood. All these uncertainties have complicated practical implementation of a robust Li-plating detection technique. This study, through multiple case studies involving real-world fast-charging conditions using automotive-grade 11.6 Ah LiBs, shows that many single signal-based diagnostic techniques may be inadequate to detect Li-plating. Among various signatures, end-of-charge rest pressure, differential pressure-sensing, and end-of-charge rest voltage were identified as particularly useful in detecting Li-plating. Furthermore, A multi-signal-based detection technique is shown to be more robust in detecting Li-plating. Using both fresh and aged cells, the results and analysis highlight how detection capabilities are influenced by various factors, including battery design, size, charging speed, operating temperature, and degradation level. Adopting such a multi-signal Li-plating detection approach may be instrumental in the rapid development of battery technology in laboratories, as well as ensuring the enhanced safety of next-generation LiBs in real-world fast-charging applications.