Battery health prognostic using efficient and robust aging trajectory matching with ensemble deep transfer learning

Guangcai Zhao, Yongzhe Kang, Peng Huang, Bin Duan and Chenghui Zhang

Energy, 2023, vol. 282, issue C

Abstract: Accurate aging trajectory and lifetime prediction are key objectives for battery prognostic and health management. However, effective health prognostic is difficult to realize due to the different aging characteristics at different aging stages and incomplete aging data for practical applications. To embrace these challenges, we propose an intelligent battery health prognostic method using efficient and robust aging trajectory matching with ensemble deep transfer learning (EDTL). Firstly, dispensing with expertise in battery degradation, spectral clustering is offline performed for unsupervised degradation pattern recognition. Secondly, to ensure the accuracy of aging trajectory matching, lifetime indicator-based candidate cell selection fusing with similarity assessment using dynamic time warping provides multi-dimensional information in finding the optimally matched battery aging trajectory from the historical data. Finally, to robustly and accurately capture the long-term dependencies of battery aging, the bi-directional long short-term memory network with EDTL is employed. Experimental results demonstrate that the proposed method could achieve accurate and reliable health prognostic at different battery aging stages or in the presence of incomplete data with a maximum lifetime prediction error of 6.35%. Compared with traditional methods, our method could provide a more accurate battery health prognostic, which is of great significance for battery predictive maintenance.

Keywords: Lithium-ion battery; Prognostic and health management (PHM); Aging trajectory matching; Ensemble deep transfer learning; Remaining useful life (RUL) (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:282:y:2023:i:c:s0360544223016225

DOI: 10.1016/j.energy.2023.128228

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