Researchers in China have used electrochemical impedance spectroscopy to analyze the health status of sodium batteries. By extracting four characteristics from the measurements, they were able to make a machine learning model for a temperature-resistant status of health estimation method.
A research team of scientists from Chinese Chongqing University and the State Grid Corporation of China has experimentally investigated the aging mechanism of sodium batteries (SIBs). With the help of the results of this study, the group was able to develop an estimation method of the temperature -resistant condition (SOH) with an average average absolute error (Mae) of 0.96.
“These SIBs, similar to lithium ion batteries (LIBs), experience a gradual decrease in usable capacity compared to cycles in the long term due to irreversible phenomena, such as side reactions, demolition of active materials and increased internal resistance,” the researchers said. “The current research into the aging mechanisms of SIBs is limited and aging data during long -term bicycles are scarce, which means that the similarities and differences in the degradation mechanisms of SIBs and Libs are unclear.”
The aging mechanism of the SIBs was investigated with the help of electrochemical impedance spectroscopy (requirement). This non-invasive technique analyzes the reaction of a battery to small AC signals at various frequencies, which measures health. Until now, however, the method was highly dependent on the ambient temperature. To solve this problem, the Academics requires the reasons of cargo (SOC) and temperature conditions, so that the most valuable characteristics were extracted that remained relatively constant with temperature.
Their experiment was conducted on commercial 26700 SIBs using the layered O3 type (CU1/9NI2/9FE1/3MN1/3) O2 Cathode and a hard carbon anode, which use NAPF6 as the electrolyte salt dissolved in Ester solvents. According to the manufacturer’s data sheet, the cargo cutting voltage was 3.95 V, the discharge cutting voltage was 1.5 V, the nominal capacity was 3.3 Ah and the maximum continuous loading speed was 1c. The requirement value was obtained every 20 cycles at load levels of 10%, 50%and 100%at temperatures of 10 ° C, 25 ° C and 30 C.
“Compared to LIBs, the large jet of sodium ions leads to pronounced volume changes and volume accumulation in multi -layered oxide sections during phase transitions, which cause breakdown of cathode material and pulverization over long -term cycling, leading to a rapid relegation of the battery,” emphasizes. “Moreover, the manufacturers’ strategies lead to expand the plateau with low potential to achieve a high energy density into a low business potential of hard carbon anodes and a high risk of sodium deposit. The formation of sharp sodium belts could be able to pierce the separator and activate the thermal gone -runner in SIBs.”
Based on their analysis, the scientists identified four temperature-proof indicators: one reflects changes in ohmic resistance, another reflects the diffusion impedance of sodiumion and the last two reflective fixed electrolytes interpass (SEI) impedance. Based on the EIS function extraction, the group used a machine learning method, in particular a Gaussian-Kernnel Support Vector Regression (SVR) model, to estimate the health of the battery. Ten batteries were used for training and two for testing.
“The Gaussian-Kernel SVR model showed robust adaptability over a wide temperature range. For the test set samples collected in 10 C, 25 C and 30 ° C, the average root-average-Square abnormality (RMSE) was 1.14%, and the average Mae was then 1,5,” all RMSE and MAEARDEN. “These results indicate that the model has maintained stable performance despite the temperature variations.”
Their findings and the model were introduced in “Demolition mechanism of sodium batteries and the estimate of health status via electrochemical impedance spectroscopy under temperature disorders“Published in Energy.
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