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

Yumin Zhang; Yongtao Liu; Pingfeng Ye; Xingquan Ji; Yunqi Wang; Kezhuang Xue; Xuchen Ma

doi:10.1049/enc2.70041

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

更新时间：2026-07-05

- State of health estimation for lithium-ion batteries using modern heuristic algorithm optimized multiple kernel extreme learning
- Energy Conversion and Economics Vol. 7, Issue 3, (2026)
- 作者机构：
  
  1. College of Electrical Engineering and Automation, Shandong University of Science and Technology,Qingdao,China
  2. Contribution: Conceptualization, Funding acquisition, Methodology, Supervision, Writing - review & editing
  3. Contribution: Formal analysis, Investigation, Methodology, Visualization, Writing - original draft
  4. College of Electrical and Information Engineering, Quzhou University,Quzhou,China
  5. Contribution: Investigation, Resources, Validation, Writing - review & editing
  6. Contribution: Project administration, Supervision, Validation, Writing - review & editing
  7. Department of Electrical and Electronics Engineering, RMIT,Melbourne,Australia
  8. Contribution: Resources, Visualization
  9. Contribution: Data curation, Software
- 作者简介：
- 基金信息：
- DOI：10.1049/enc2.70041
  CLC：
- Published：2026
- 稿件说明：
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Yumin Zhang, Yongtao Liu, Pingfeng Ye, et al. State of health estimation for lithium-ion batteries using modern heuristic algorithm optimized multiple kernel extreme learning[J]. Energy Conversion and Economics, 2026, 7(3).
DOI：

Yumin Zhang, Yongtao Liu, Pingfeng Ye, et al. State of health estimation for lithium-ion batteries using modern heuristic algorithm optimized multiple kernel extreme learning[J]. Energy Conversion and Economics, 2026, 7(3). DOI： 10.1049/enc2.70041.

摘要

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.

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