The Problem:
The widespread use of electrochemical energy storage devices, particularly high-energy-density Lithium-Ion batteries, in various daily life applications has raised concerns about their unexpected deterioration, posing safety, reliability, and efficiency risks. Existing state-of-health (SOH) estimation methods, including capacity-based, IC-DV-based, and impedance-based approaches, are limited from slow processing, data storage requirements, sensitivity to noise, and accuracy issues, especially in the presence of changing battery conditions like State-Of-Charge (SOC). Consequently, there is a need to rapidly estimate the SOH of energy storage devices.
The Solution:
Researchers at the University of Alabama have developed a method for determining the SOH of energy storage devices, like batteries, by measuring their complex impedance across a range of frequencies and estimating SOH based on the interrelationship between the magnitude and phase of the impedance. This method can improve accuracy, generate phase-magnitude plots, and use machine learning models to predict and adjust SOH, enabling the identification of potential faults or failures. Additionally, this approach can estimate both permanent and temporary degradations in SOH, managing the health of energy storage devices.
Benefits: