State of health estimation for lithium‐ion batteries using modern heuristic algorithm optimized multiple kernel extreme learning

Abstract Accurate state of health (SOH) estimation is crucial for the safe operation of lithium‐ion batteries. To address the limited capability of traditional models in characterizing nonlinear degradation, this study proposes a novel data‐driven SOH estimation framework. First, an indirect health indicator (IHI) extraction strategy constructs aging‐sensitive features directly from operational data, avoiding complex electrochemical modeling. Second, a hybrid multiple kernel extreme learning machine (HMKELM) integrates radial basis, polynomial, and sigmoid kernels with adaptive mixing weights to capture both local and global characteristics of degradation trajectories. Furthermore, to alleviate the hyperparameter‐tuning bottleneck of HMKELM, a gold rush‐inspired grey wolf optimizer (GRGWO), incorporating a leader‐update strategy and a dynamic convergence factor, is developed to escape local optima and jointly optimize kernel parameters and mixing weights. Validation on the NASA ARC‐FY08Q4 dataset shows that the proposed GRGWO‐HMKELM framework enables accurate long‐horizon SOH estimation using only early‐cycle data for training and consistently outperforms representative baseline models, indicating improved generalization for practical in‐service SOH monitoring.