Non-destructive and adaptive negative electrode impedance estimation of lithium-ion batteries using ensemble learning

Guangjun Qian, Zhicheng Zhu, Peng Guo, Lifang Liu, Yuedong Sun, Yuejiu Zheng, Xuebing Han and Minggao Ouyang

Applied Energy, 2026, vol. 402, issue PB, No S0306261925017477

Abstract: The negative electrode (NE) impedance of lithium-ion batteries is a key indicator that reflects their internal electrochemical dynamics. Traditional invasive methods relying on reference electrodes (REs) fail to satisfy the demands of non-destructive, online monitoring in engineering applications. To overcome this limitation, this manuscript proposes a data-driven method based on ensemble learning to achieve non-destructive and adaptive estimation of NE impedance. The research integrates an improved dual-RE experimental design with ensemble learning algorithms. A total of 1050 electrochemical impedance spectroscopy (EIS) datasets are systematically acquired from two battery types within a temperature range of 0–45 °C and a state of charge range of 20 %–80 %. Features are extracted through equivalent circuit model analysis and distribution of relaxation times representation, and a precise mapping model is established to connect battery impedance with NE impedance. The proposed model achieves a coefficient of determination (R2) above 98.5 % for estimating NE polarization resistance. The predicted NE EIS curves yield a mean absolute percentage error (MAPE) below 8.1 %, while performance under unseen conditions maintains MAPE within 9.25 %, demonstrating great generalization ability. Moreover, based on predicted impedance features, a linear internal temperature estimation model is constructed. This approach reduces the mean absolute error by 14.5 % compared with conventional methods and exhibits strong adaptability across different battery capacities. This study provides a novel technical pathway for electrode-level parameter estimation, highlights the essential role of NE impedance in accurate state perception, and contributes to advancing intelligent battery management system.

Keywords: Lithium-ion batteries; NE impedance; Non-destructive estimation; Ensemble learning; Engineering application (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1016/j.apenergy.2025.127017

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