Isocyanate Additives Improve the Low-Temperature Performance of LiNi0.8Mn0.1Co0.1O2||SiOx@Graphite Lithium-Ion Batteries.

LiNi0.8Mn0.1Co0.1O2||SiOx@graphite (NCM811||SiOx@G)-based lithium-ion batteries (LIBs) exhibit high energy density and have found wide applications in various fields, including electric vehicles. Nonetheless, its low-temperature performance remains a challenge. One of the most efficacious strategies to enhance the low-temperature functionality of battery is the development of appropriate electrolytes with low-temperature suitability. Herein, p-tolyl isocyanate (PTI) and 4-fluorophenyl isocyanate (4-FI) are used as additive substances to integrate into the electrolytes to improve the low-temperature performance of the battery. Theoretical calculations and experimental results indicate that PTI and 4-FI can both preferentially generate a stable SEI on the electrode surface, which is beneficial to reduce the interfacial impedance. As a result, the additive, i.e. 4-FI, is superior to PTI in improving the low-temperature performance of the battery due to the optimization of F in the SEI membrane components. At room temperature, the cyclic stability of the NCM811/SiOx@G pouch cell increases from 92.5% (without additive) to 94.2% (with 1% 4-FI) after 200 cycles at 0.5 C. Under the operating temperature of -20 °C, the cyclic stability of the NCM811/SiOx@G pouch cell increases from 83.2% (without additive) to 88.6% (with 1% 4-FI) after 100 cycles at 0.33 C. Therefore, a rational interphase design involving the modification of the additive structure is a cost-effective way to improve the performance of LIBs.

Parameter estimation of three-parameter Weibull probability model based on outlier detection.

The Weibull probability model used in statistical analysis has become more popular in the inconsistency evaluation of used Li-ion batteries due to its flexibility in fitting asymmetrically distributed data. However, despite its better fitting of data with a non-zero minimum, the three-parameter Weibull model is less used because of its complicated calculation. Additionally, the Weibull family is likely to overfit and shows inference from outliers. Although conventional estimation methods for Weibull parameters based on dispersion and symmetry of the overall distribution lead to derivation from the actual data features, there is little research into methods to solve the contradiction between estimation accuracy and proper outlier detection. In this study, a Weibull parameter estimation method was proposed that features simplified computation and eliminates the interference from outliers. The outliers were identified based on the obtained Weibull parameters and excluded from the sample data. The method was implemented for fitting the capacity distribution of Li-ion batteries, which was verified by a chi-square test at a confidence of 95% and the Anderson-Darling test. It showed a higher goodness-of-fit and less error than the results of the maximum likelihood estimated Weibull model as well as the normal distribution. The optimal presetting of column number and peak reference point selection were determined by parameter discussion.