Lithium Plating Diagnosis of Lithium-ion Batteries Based on Clustering with Dual Impedance Models

Lithium-ion batteries (LIBs) have become an important component in today’s energy storage, electric vehicles, and other industries due to their superior performance. However, lithium plating faults in batteries can lead to rapid degradation of battery performance and, in severe cases, cause safety accidents, making it one of the main problems constraining LIBs development. To address this challenge, this paper proposes a clustering based diagnostic method for lithium plating using features from dual impedance models. By extracting parameters from the equivalent circuit model and the electrochemical impedance spectroscopy model, and utilizing a cluster algorithm based on fuzzy and weighted shared neighbor, high-precision diagnosis of lithium plating is achieved. Validated by the capacity decay method, the diagnostic accuracy reaches $\mathbf{9 6. 3 1 \%}$.