Sustainable graphene-based energy storage device technology: Materials, methods, Monitoring and digital twin

Abstract

Energy harvesting is possible through capable energy transfer materials, and one such impressive material is graphene, which has exhibited promising properties like unprecedentedly high theoretical surface area, enhanced electrical conductivity, thermal conductivity, mechanical stability, flexibility, recyclability, and so on. Herein, for the sake of everyone desirous of contributing to the field of graphene materials for high-speed energy storage devices, the fundamentals, analytics, synthesis, prospects, and challenges of energy storage cell design for fast charging of electric vehicles have been reviewed. To overcome the limitations of mechanistic models for energy-storage devices, empiricism conjointly with data-driven machine learning has been quite effective in multiple domains. The limitations in modeling of energy storage devices, in terms of swiftness and accuracy in their state prediction can be surmounted by the aid of machine learning. Conclusively, in the context of energy management, we underscore the significant challenges related to modeling accuracy, performing original computations, and relevant big data generation. This review, by dint of its futuristic insights, will help researchers to develop digital twin approach for sustainable energy management using energy storage technology toward dependable, economic, and scalable optimization processes at the multiple stages of energy conversion and energy management.

Graphical abstract

Keywords:

• Energy storage
• graphene
• machine learning
• state estimation
• digital twin

Acknowledgement

We place on record our eternal indebtedness to our parents, teachers, and well-wishers. Their motivation on the path of pursuit of knowledge has been inspirational in our efforts.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Acronyms used in Table 2.