research-article

Support-based Neural Network Ensemble Method for Predicting the SoH of Lithium-ion Battery

Authors:

Hengshan Zhang,

Yun WangAuthors Info & Claims

AIPR '22: Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition

Pages 102 - 108

https://doi.org/10.1145/3573942.3573958

Published: 16 May 2023 Publication History

Abstract

Lithium-ion batteries are widely used in industrial and domestic applications because of their high energy ratio and low self-discharge rate. It is important to accurately predict the State of Health (SoH) of lithium-ion batteries as they degrade during use, which can lead to serious safety hazards. We propose a support-based neural network ensemble method, which incorporates the prediction results of several basic neural network models. First, a set of better initial integration weights is calculated and the initial integration result is obtained, then the support degree between this result and the prediction result of each basic neural network is calculated, and the final integration weights are calculated by the weight iterative update ensemble algorithm and the integration prediction result of lithium-ion batteries SoH is obtained. This method avoids the risk of the "majority principle" which does not guarantee that most models perform better, and removes the constraint of positive integration weights, which can further reduce the adverse effects of poorly performing models on the integration results. We demonstrate the effectiveness of the proposed ensemble method for the lithium-ion batteries SoH prediction problem through a 5-fold cross-validation experiment on two datasets.

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Cited By

Naresh VGayathri PTejaswi PInduja PReddy CSudheer Y(2025)Optimizing electric vehicle battery health monitoring: A resilient ensemble learning approach for state-of-health predictionSustainable Energy, Grids and Networks10.1016/j.segan.2025.10165542(101655)Online publication date: Jun-2025
https://doi.org/10.1016/j.segan.2025.101655

Index Terms

Support-based Neural Network Ensemble Method for Predicting the SoH of Lithium-ion Battery
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
      1. Ensemble methods

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AIPR '22: Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition

September 2022

1221 pages

ISBN:9781450396899

DOI:10.1145/3573942

Copyright © 2022 ACM.

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Publication History

Published: 16 May 2023

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AIPR 2022

AIPR 2022: 2022 5th International Conference on Artificial Intelligence and Pattern Recognition

September 23 - 25, 2022

Xiamen, China

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Cited By

Naresh VGayathri PTejaswi PInduja PReddy CSudheer Y(2025)Optimizing electric vehicle battery health monitoring: A resilient ensemble learning approach for state-of-health predictionSustainable Energy, Grids and Networks10.1016/j.segan.2025.10165542(101655)Online publication date: Jun-2025
https://doi.org/10.1016/j.segan.2025.101655

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