Electrochemical impedance spectroscopy (EIS) offers a nondestructive means of diagnosis for the battery's state of health (SoH). However, traditional equivalent circuit‐based approaches—relying on extensive modeling and fitting of complex EIS data such as real and imaginary impedance components, phase shift, and frequency—are time‐consuming and heavily dependent on expert interpretation, which can compromise reliability. In this context, artificial intelligence‐based models present a faster and more reliable alternative for interpreting EIS data. These models can uncover hidden patterns and parameters that may be overlooked by human experts, thereby enabling more accurate prediction of the battery's SoH. In this study, four machine learning algorithms are employed to predict the SoH of lithium metal batteries based on EIS data, achieving predictive accuracies exceeding 95%. Feature importance analysis indicated that phase shift—an often underutilized parameter in conventional EIS interpretation—plays a critical role in the SoH prediction process. Furthermore, the analysis enabled the attribution of specific EIS features to their corresponding electrochemical phenomena, thereby elucidating the physical basis of the model predictions. The resulting models exhibit high precision in forecasting battery discharge capacity and diagnosing degradation mechanisms, demonstrating their potential as powerful tools for advancing battery diagnostics and performance optimization.
Yoon et al. (Wed,) studied this question.
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