Comparison of machine learning techniques for SoC and SoH evaluation from impedance data of an aged lithium ion battery

Abstract

State of charge estimation and ageing evolution of lithium ion (Li-Ion) batteries are key points for their massive applications in the market. However, the battery behavior is very complex to understand because many parameters act in determining their ageing evolution. Therefore, traditional analytical models employed for this purpose are often affected by inaccuracy. In this context, machine learning techniques can provide a viable alternative to traditional models and a useful tool to characterize the batteries behavior.

In this work, different machine learning techniques were applied to model the impedance evolution over time of an aged cobalt based Li-Ion battery, cycled under a stationary frequency regulation profile for grid application. The different ML techniques were compared in terms of accuracy to determine the state of charge and the state of health over the battery ageing phenomena. Experimental results showed that ML based on Random Forest algorithm can be profitably used for this purpose.