State of health estimation of Lithium-ion batteries: A review of data-driven methods

This paper presents a systematic review of data-driven methods for state of health estimation of lithium-ion batteries. The growing interest in these methods is due to their capability to retrieve nonlinear relationships between data. Future research directions are presented to improve the accuracy and stability of machine learning methods for state of health estimation.