Degradation mechanism of sodium-ion batteries and state of health estimation via electrochemical impedance spectroscopy under temperature disturbances

Yupeng Liu, Lijun Yang, Ruijin Liao, Chengyu Hu, Yanlin Xiao, Chunwang He, Xu Wu, Yuan Zhang and Siquan Li

Energy, 2025, vol. 332, issue C

Abstract: As sodium-ion batteries (SIBs) increasingly penetrate the electrochemical energy storage market, elucidating their degradation mechanisms and precisely assessing the state of health (SOH) become critical to ensure the efficient management and operational safety of SIB systems. This study aims to investigate the aging mechanisms of SIBs and propose a temperature-resistant SOH estimation method using electrochemical impedance spectroscopy (EIS). Firstly, a life cycle aging experiment was carried out with commercially available SIBs, and the aging mechanism of SIBs was analyzed through EIS, incremental capacity analysis, and scanning electron microscopy. Secondly, based on the EIS observations from the life cycle tests and temperature tests, the aging and temperature characteristics of battery impedance were comprehensively investigated, leading to the extraction and construction of aging features from the EIS resistant to temperature disturbances. Finally, leveraging the proposed temperature-resistant EIS health factor, high-precision estimation of battery SOH over a wide temperature range is realized by only using EIS data from a single temperature to train a support vector regression (SVR) model, and there is no need to rely on temperature sensors for correction. Results show that the Gaussian-kernel SVR model has an average root-mean-square error (RMSE) of 1.14 and an average mean absolute error (MAE) of 0.96 for the test set samples collected at 10, 25 and 30 °C. The RMSE and MAE at each temperature are both below 1.5 %, indicating that the model has high estimation accuracy and strong stability.

Keywords: Sodium-ion batteries; Aging mechanism; Electrochemical impedance spectroscopy; State of health; Support vector regression (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:332:y:2025:i:c:s0360544225027069

DOI: 10.1016/j.energy.2025.137064

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