Machine learning techniques for prediction of capacitance and remaining useful life of supercapacitors: A comprehensive review

Supercapacitors are appealing energy storage devices for their promising features like high power density, outstanding cycling stability, and a quick charge–discharge cycle. The exceptional life cycle and ultimate power capability of supercapacitors are needed in the transportation and renewable energy generation sectors. Hence, predicting the capacitance and lifecycle of supercapacitors is significant for selecting the suitable material and planning replacement intervals for supercapacitors. In addition, system failures can be better addressed by accurately forecasting the lifecycle of SCs. Recently, the use of machine learning for performance prediction of energy storage materials has drawn increasing attention from researchers globally because of its superiority in prediction accuracy, time efficiency, and cost-effectiveness. This article presents a detailed review of the progress and advancement of ML techniques for the prediction of capacitance and remaining useful life (RUL) of supercapacitors. The review starts with an introduction to supercapacitor materials and ML applications in energy storage devices, followed by workflow for ML model building for supercapacitor materials. Then, the summary of machine learning applications for the prediction of capacitance and RUL of different supercapacitor materials including EDLCs (carbon based materials), pesudocapacitive (oxides and composites) and hybrid materials is presented. Finally, the general perspective for future directions is also presented.