Data driven health and life prognosis management of supercapacitor and lithium-ion battery storage systems: Developments, implementation aspects, limitations, and future directions

Health and life prognosis research contributes to the development of next-generation energy storage solutions with enhanced performance, longer lifespans, and improved sustainability. Nonetheless, energy storage systems experience a range of deterioration processes over time, such as chemical reactions, mechanical strain, and degradation of electrode materials, posing difficulties in precise modeling and prediction. Additionally, the performance of energy storage systems can be influenced by factors such as temperature, operating conditions, and usage patterns, further complicating prognosis accuracy. In this paper, the developments made in the field of data-driven health and life prognosis management with regard to state of health (SOH) and remaining useful life (RUL) of supercapacitor and lithium-ion battery storage systems has been reviewed. Accordingly, this paper investigates the advancements in the SOH and RUL estimation of supercapacitors and lithium-ion battery storage systems through data-driven approaches involves analyzing significant findings, contributions, benefits, drawbacks, and research gaps. Moreover, the various implementation aspects of these approaches, including data processing, feature extraction, computation capacity, experiments, and validation are discussed. In addition, the paper outlines the limitations and challenges of data-driven approaches for assessing the SOH and RUL of supercapacitor and lithium-ion battery storage systems, as well as proposing future research paths and opportunities aimed at improving this prognosis using data-driven methods. Overall, data-driven approaches empower precise SOH estimation and RUL prognosis, significantly contributing to reliability, efficiency, and costeffectiveness in for diverse energy storage needs.