Comparison of data driven algorithms for SoH estimation of Lithium-ion batteries

The Lithium (Li)-ion batteries in Electric Vehicles reach their End of Life (EoL) when their capacity degrades by twenty percent. Circular economy suggests to re-purpose these EoL Li-ion batteries in less demanding applications. In the case of re-purposing, there are multiple second-life applications of a product and it is important to know the State of Health (SoH) in prior to sorting the product efficiently into the above-said applications. In this paper, we propose a data driven method for SoH estimation of Li-ion batteries. The correlation was learnt using three different machine learning models namely Linear Regression, Support vector regression, and Feed-forward Neural network. A use case is created on the NASA AMES open source battery data. The accuracy of the different models has been compared using the indicator of Root mean square error. The result concluded that the feed-forward neural network has higher accuracy compared to the other two models employed.