Sodium-ion batteries (SIBs) open up new perspectives for sustainable energy storage. How-ever, to fully leverage their potential, it is crucial to gain a precise understanding of their aging mechanisms and develop accurate lifetime prediction models.
This research focuses on the aging data of 81 commercial sodium-ion cells manufactured by Shenzhen Electronics Co. Ltd., subjected to artificial cyclic aging under controlled conditions. The experimental setup involves aging the cells in a climate chamber maintained at 25°C, with 2C charging and discharging currents. Six distinct aging conditions are employed, vary-ing in depth of discharge (DOD) and mean state of charge (SOC). Regular Reference Performance Tests are conducted to track key electrical characteristics throughout the aging pro-cess, while advanced diagnostic techniques such as CT-scans and electrochemical impedance spectroscopy (EIS) are utilized for in-depth analysis of selected cells. The goal of our work is to compile various approaches for analyzing SIB aging data in order to identify po-tential correlations.
Initial capacity scatter is observed in the range of 10%. With after more than 4.000 equiva-lent full cycles all cells maintain a state of health (SOH) above 87%, while with cells in most aging conditions the loss from initial capacity is around 3 %. The divergence in aging rates across different discharge depths becomes evident early in the cycling process. Accelerated aging is observed with conditions aged at low SOC. We apply common statistical methods to show variances within the aging conditions. CT measurements show the decline in the electrode structure, the increase in the equivalent resistance reflects increasing degrada-tion, and shifts in the dVA indicate material losses. These examples illustrate how subse-quent analyses link the developed parameters with end-of-life measurements to specifically assign ageing mechanisms.
This study presents a detailed analysis of the differential voltage analysis (dVA) method, which compares cells under the same aging conditions (mean20DOD40). We examine the degradation mechanisms, their effects, and their impact on the shift of the voltage curve. The evaluation begins with the visualization of the dVA shift. Additionally, we parameterize this shift by defining selected peaks, which serve as reference points to correlate the ob-served values. This approach allows for a systematic assessment of degradation effects and provides deeper insights into battery aging behavior. The subsequent analysis aims to interpret anomalies and correlate them with aging effects.