Advancing Lithium-ion Battery Management: A Comprehensive Approach for Enhanced Remaining Useful Life Prediction

Abstract

Accurately predicting the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for optimizing battery management systems, ensuring reliable performance, and maximizing operational efficiency. This paper presents an advanced approach using a Random Forest Regressor (RFR) combined with sophisticated feature extraction techniques to enhance the accuracy and reliability of battery lifespan predictions. The methodology involves extracting a comprehensive set of features from battery degradation data, carefully selected to capture various aspects of battery health and performance. These features provide a holistic understanding of the battery's condition. Data visualization tools are utilized to aid in the interpretation of these features, allowing stakeholders to gain actionable insights from the prediction results. By integrating RFR with these advanced feature extraction techniques, the proposed approach significantly improves battery RUL predictions. The ensemble learning capabilities of RFR, coupled with the richness of the extracted features, enable the model to capture complex relationships within the data, leading to more accurate and reliable lifespan predictions. This work has practical implications beyond academic interest, offering substantial benefits for improving battery management strategies and enhancing overall system reliability. More precise RUL predictions allow stakeholders to plan maintenance schedules proactively, optimize resource allocation, and mitigate risks associated with battery degradation. This ultimately contributes to prolonged battery lifespans, reduced downtime, and improved operational efficiency across various applications, including electric vehicles and renewable energy storage systems.