A review of selection of health indicators for predicting battery health: Analysis, challenges and future directions

Abstract

The existing literature extensively explores deep learning-based models for battery health forecasting, highlighting their advantageous capacity to effectively model the intricate and nonlinear nature of battery data. Due to the nature of data-driven models, the selection of input factors (in this case, battery health indicators) significantly affects the prediction accuracy of the models. Therefore, this review analyzes the selection of battery health indicators for forecasting State of Health and Remaining Useful Life of a battery in a three-step manner: (i) analyzing recent trends in battery health indicators, (ii) identifying the challenges faced in selecting suitable battery health indicators, and (iii) proposing future directions to address these challenges. By synthesizing the current challenges and future prospects, this review paper provides researchers and engineers with a roadmap for advancing battery health management and enabling the widespread adoption of battery technologies in a sustainable and efficient manner.