Kalman filter anomaly values processing meta-model ensemble learning framework for Lithium-ion battery capacity prediction

Xuewei Han, Huimei Yuan and Lifeng Wu

Energy, 2025, vol. 322, issue C

Abstract: To address the low prediction accuracy caused by single-view in Lithium-ion battery capacity prediction, a Kalman filter anomaly values processing-based meta-model ensemble learning (KF-AVP-M2EL) framework is proposed using block resampling and multi-activation extreme learning machine (BR-MA-ELM). First, BR-MA-ELM addresses the feature forgetting problems caused by time-series interference of online sequential extreme learning machine (OS-ELM) through block resampling. Second, BR-MA-ELM introduces a nonlinear activation function set (NLAFS), overcoming the limited nonlinear expression capability of OS-ELM. Third, during meta-dataset construction, the Kalman filter (KF) initializes the model with the capacity degradation data, records anomaly values and their indexes from health indicators (HIs) predictions, and filters corresponding results from another view, effectively reducing interference. Finally, BR-MA-ELM is employed as the meta-model, the anomaly values saved from the HIs view are then reinserted according to their corresponding indexes, preserving HIs’ ability to capture local changes while integrating the overall degradation trend from the capacity degradation data view, thereby comprehensively capturing the multidimensional characteristics of battery aging and ensuring more accurate predictions. In the comparison experiment, KF-AVP-M2EL achieved an RMSE of 0.0034, an MAE of 0.0026, and an MAPE of 0.02, outperforming four state-of-the-art algorithms. Ablation experiments further validate the superiority of the proposed algorithm.

Keywords: Lithium-ion battery; Block resampling; Multi-activation functions; OS-ELM; Kalman filter; Anomaly values processing (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225012678
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:322:y:2025:i:c:s0360544225012678

DOI: 10.1016/j.energy.2025.135625

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().