China’s new tech flags failures before lithium battery fully activates

Chinese researchers from Tsinghua Shenzhen International Graduate School and the Shenzhen Institute of Advanced Technology have developed a predictive model that can forecast lithium metal anode failures within just the first two charging cycles of lithium metal batteries (LMBs). By analyzing electrochemical data from these initial cycles, the model identifies early-stage lithium plating and stripping behaviors that serve as “electrochemical fingerprints” indicative of different failure modes. This approach significantly reduces the time and resources needed for testing compared to traditional post-mortem analyses, which only reveal failure outcomes after degradation has occurred. The model employs machine learning algorithms trained on extensive datasets to classify three main types of battery failure: kinetics degradation, reversibility degradation, and co-degradation. It also demonstrates strong generalizability, accurately predicting failures across various electrolyte formulations, including low- and high-concentration systems based on carbonates, ethers, and siloxanes. Importantly, this predictive method requires no battery disassembly or special instruments, relying solely on cycling data, making