Chinese researchers have successfully predicted lithium metal anode failures with the help of a predictive model. The tool uses electrochemical data from the initial cycles of lithium metal batteries (LMBs) to forecast potential failures.
Developed by researchers at Tsinghua Shenzhen International Graduate School and the Shenzhen Institute of Advanced Technology, the model identifies early indicators that correlate with different types of anode failure.
Researchers also revealed that the failure signatures in the initial cycles serve as “electrochemical fingerprints”—providing key insights into the failure mechanisms of lithium metal anodes.
Early-stage lithium plating and stripping behaviors
“We discovered that early-stage lithium plating and stripping behaviors during the first two cycles are highly indicative of the battery’s eventual failure mode,” said first author Zhihong Piao. “This breakthrough model not only predicts the failure type accurately but also reduces the time and resources needed for testing.”
The latest technology could play a significant role as lithium metal batteries (LMBs) are a promising technology for next-generation energy storage, offering higher energy density than traditional lithium-ion batteries.
Researchers have long struggled with understanding the underlying causes of battery failure, often relying on post-mortem analysis that only reveals outcomes but fails to capture the dynamic processes leading to failure. However, the latest model appears to address multiple issues.
High-accuracy classification model
The Chinese team leveraged machine learning algorithms and extensive datasets to develop a high-accuracy classification model. Their model distinguishes between three main failure types: kinetics degradation failure, reversibility degradation failure, and co-degradation failure.
The model also demonstrates generalizability, accurately classifying failures in cells using a wide range of published electrolyte formulations—including low- and high-concentration systems based on carbonates, ethers, and siloxanes.
Instead of waiting weeks or even months for a battery to degrade, engineers and researchers can now forecast its future in a matter of days.
This offers a major advantage in both speed and cost, especially when developing and testing new electrolyte formulas or battery designs. What makes this approach even more powerful is that it doesn’t require any disassembly or special instruments—it works using cycling data that the battery produces, according to a press release.
Highly practical and easy to adopt
Researchers pointed out that this makes it highly practical and easy to adopt in both academic labs and commercial production lines. It also opens new door to design more robust batteries, optimizing electrolytes, and accelerating the development of LMB technologies.
The study that revealed the significance of the solid electrolyte interphase (SEI) and the lithium deposit microstructure in determining battery performance was validated with experiments and simulations, linking observed electrochemical behaviors to specific SEI properties and lithium morphology.
Researchers stressed that these two features influence the formation of ineffective interphase regions (lacking intimate contact with the lithium metal) and inactive lithium at the lithium metal anode, which in turn lengthen charge carrier (lithium-ion and electron) transport paths, leading to poorer kinetics and reversibility.
“This study using a pre-mortem prediction method deepens understanding of lithium metal anode failure mechanisms by uncovering the root causes of kinetics and reversibility degradation from fingerprints hidden in initial cycles instead of a post-mortem manner, facilitating the rapid assessment of battery reliability and development of electrolytes,” said researchers in the study.
