Feature Extraction from Electrochemical Impedance Spectroscopy for State of Health Estimation of Lithium-Ion Batteries Under Different Temperatures

Abstract

The State of Health (SOH) is a crucial component of battery management systems (BMSs), offering important health information and protection against unsafe usage. In this paper, an accurate model for SOH estimation of Li-ion batteries was developed, which is uniquely characterized by using only the imaginary part of impedance at a specific frequency for precise SOH estimation. Through the identification of the relationship between impedance at a specific frequency and capacity degradation using correlation coefficients, the feature data most closely related to battery aging was selected. Next, the battery aging modeling and SOH estimation were validated on nine batteries across three different temperatures using a Feed-forward Neural Network (FNN). The validation results indicated that the proposed method has a high estimation accuracy, achieving a Mean Absolute Percentage Error (MAPE) of merely 2.05% throughout the entire lifecycle of the battery 45C02 during tests at a temperature of 45°C.