Capacity estimation of lithium-ion batteries based on data aggregation and feature fusion via graph neural network

Zhe Wang, Fangfang Yang, Qiang Xu, Yongjian Wang, Hong Yan and Min Xie

Applied Energy, 2023, vol. 336, issue C, No S0306261923001721

Abstract: Lithium-ion batteries in electrical devices face inevitable degradation along with the long-term usage. The accompanying battery capacity estimation is crucial for battery health management. However, the hand-crafted feature engineering in traditional methods and complicated network design followed by the laborious trial in data-driven methods hinder efficient capacity estimation. In this work, the battery measurements from different sensors are organized as the graph structure and comprehensively utilized based on graph neural network. The feature fusion is further designed to enhance the network capacity. The specific data aggregation and feature fusion operations are selected by neural architecture search, which relieves the network design and increases the adaptability. Two public datasets are adopted to verify the effectiveness of the proposed scheme. Additional discussions are conducted to emphasize the capability of the graph neural network and the necessity of architecture searching. The comparison analysis and the performance under noisy environment further demonstrate the superiority of proposed scheme.

Keywords: Capacity estimation; Deep learning; Feature fusion; Graph neural network; Lithium-ion battery (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (10)

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DOI: 10.1016/j.apenergy.2023.120808

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