Machine learning-enhanced electrochemical impedance spectroscopy for lithium-ion battery research
The rapid proliferation of electrification has driven a global surge in the demand for power and energy storage batteries.This rise has intensified concerns regarding battery safety and reliability,emphasizing the need for accurate methods for diagnosing and predicting battery aging,making this a notable area of research in the battery domain.Electrochemical impedance spectroscopy(EIS)is widely used to analyze the complex aging processes of batteries because it can effectively decouple various frequency-domain processes.The integration of machine learning methods not only facilitates the acquisition and analysis of EIS data but also offers deeper insights into battery aging and failure mechanisms.This paper reviews the latest applications of machine learning methods in EIS technique,focusing on machine learning-based acquisition and analysis of EIS data for battery life assessment and prediction.In addition,this paper explores the potential of data fusion methods for analyzing the aging behavior of batteries and predicting their lifespan,discusses the current limitations of applying machine learning to EIS research,and describes the future prospects of EIS-based battery life prediction.
万方数据
维普
