Secondary Structural Ensemble Learning Cluster for Estimating the State of Health of Lithium-Ion Batteries

Abstract

Accurate online state-of-health (SOH) estimation can improve the operational efficiency of lithium-ion batteries (LIBs) and ensure the safety of energy storage systems. However, the complex electrochemical properties of LIBs make accurate SOH estimation challenging. To overcome this challenge, we propose a secondary structural ensemble learning (SSEL) cluster. The proposed SSEL cluster includes multiple SSEL frameworks established separately within different SOH data intervals, allowing the identification of stable feature−SOH relationships. The adaptability and basic accuracy of each SSEL framework are guaranteed by various base learners and the corresponding stacking model and bagging model fusion. Each framework remains unique and specialized owing to the adoption of back propagation neural networks, which adjust learner weights based on the feature−SOH relationship at each interval. The effectiveness of the SSEL cluster was verified using the Oxford Battery Degradation Dataset 1. Comparisons showed that the proposed estimation method performs better than traditional machine learning methods.