Battery Safety Algorithm Function Research Report

Accurately estimating the State of Charge (SOC), State of Health (SOH), and predicting the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for extending their lifespan and ensuring system safety. Researchers worldwide have extensively studied methods for battery state assessment and life prediction, proposing various approaches. This article begins by introducing existing estimation methods for SOC and SOH, analyzing their strengths and weaknesses. Additionally, it explores leveraging machine learning and deep learning to uncover key parameters in battery management algorithms. Subsequently, it defines RUL and categorizes and compares existing methods for RUL estimation, discussing how to judiciously apply RUL estimation in practical battery management. Finally, the paper delves into potential challenges and future directions for lithium-ion battery management systems.