This article presents a classification method that utilizes impedance spectrum features and an enhanced K-means algorithm for Lithium-ion batteries. Additionally, a parameter identification method for the fractional order model is proposed, which is based on the flow direction algorithm (FDA).
This research introduces a battery classification approach that leverages impedance spectrum features and an improved K -means algorithm. The methodology begins with conducting an impedance spectroscopy test on lithium-ion batteries to obtain their electrochemical impedance spectra at various frequencies.
Lithium-ion batteries (LIBs) are currently the primary energy storage devices for modern electric vehicles (EVs). Early-cycle lifetime/quality classification of LIBs is a promising technology for many EV-related applications, such as fast-charging optimization design, production evaluation, battery pack design, second-life recycling, etc.
For example, a LEB describes a cell with liquid electrolyte, a prominent example of which is commonly known as the lithium-ion battery.
By far, considerable researches have been done in modeling and approaches to accurately estimate SoC for lithium-ion batteries (LiBs) used in EVs. Nevertheless, existing reviews are either ambiguous in classifications or incomplete in methodologies, especially lack of detailed evaluation.
1. Introduction Under the global pursuit of the green and low-carbon future, lithium-ion batteries (LIBs) have played significant roles in the energy storage and supply for modern electrical transportation systems, such as new energy electric vehicles (EVs), electric trains, etc. [1, 2].