Remaining useful life prediction for lithium-ion battery storage system: A comprehensive review of methods, key factors, issues and future outlook

Abstract

Developing battery storage systems for clean energy applications is fundamental for addressing carbon emissions problems. Consequently, battery remaining useful life prognostics must be established to gauge battery reliability to mitigate battery failure and risks. Nonetheless, the remaining useful life prediction is challenging because the factors that lead to capacity degradation are not entirely understood but are known to complex internal battery mechanism and external environmental factor. Therefore, the aim of this review is to provide a critical discussion and analysis of remaining useful life prediction of lithium-ion battery storage system. In line with that, various methods and techniques have been investigated comprehensively highlighting outcomes, advantages, disadvantages, and research limitations. Besides, the review explores numerous crucial implementation factors concerning experiments, battery data, features, training, and computation capability. Furthermore, several key issues and challenges are outlined to identify the existing research gaps. Finally, this review delivers effective suggestions, opportunities and improvements which would be favourable to the researchers to develop an appropriate and robust remaining useful life prediction method for sustainable operation and management of future battery storage system.

Keywords

• Remaining useful life
• Lithium-ion battery
• Mode-based methods
• Data-driven methods
• Optimization
• Battery aging