A Comprehensive Survey of State of Art Methods for Remaining Useful Life Prediction

Rechargeable batteries are vital to contemporary energy systems, making it vital to monitor battery health. The ability to determine the Remaining Useful Life (RUL) of a battery is critical to ensure the safety of batteries and to minimize the costs of batteries. However, it continues to be a challenge due to the impact of numerous complex aging factors such as thermal cycling and usage cycles. The present paper reviews current RUL prediction methods, including physics-based, data-driven, and hybrid methods. The strengths and weaknesses of each of these methods will be critically evaluated for use in real-time applications and will identify the need for research and future directions for the development of more accurate battery health prognostic systems.