RUL Prediction Method for Lithium-Ion Batteries Based on the SOA-ELM Algorithm

Abstract

With the rapid advancement of electrochemical energy storage power stations and electric vehicles, lithium-ion batteries have gained widespread adoption due to their high specific energy and superior power performance. However, the increasing frequency of safety incidents in electrochemical energy storage facilities in recent years has raised significant concerns. Effective monitoring of lithium-ion battery conditions is crucial to ensure the safety of power systems and support the sustainable growth of the electrochemical energy storage industry. Among the key challenges, accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is essential for maintaining the safe and reliable operation of battery management systems. This paper proposes an advanced RUL prediction model that combines the seagull optimization algorithm (SOA) with the extreme learning machine (ELM) to enhance prediction accuracy. The proposed SOA-ELM model is validated using the NASA dataset, and the results demonstrate its effectiveness and potential in improving RUL prediction for lithium-ion batteries. This study contributes to the development of more reliable and efficient battery management systems, paving the way for safer and more sustainable energy storage solutions.