ABSTRACT
The phenomenon of fire or even explosion caused by thermal runaway of lithium-ion power batteries poses a serious threat to the safety of electric vehicles. An in-depth study of the core-material thermal runaway reaction mechanism and reaction chain is a prerequisite for proposing a mechanism to prevent battery thermal runaway and enhance battery safety. In this study, based on a 24 Ah commercial Li(Ni0.6Co0.2Mn0.2)O2/graphite soft pack battery, the heat production characteristics of different state of charge (SOC) cathode and anode materials, the separator, the electrolyte, and their combinations of the battery were investigated using differential scanning calorimetry. The results show that the reaction between the negative electrode and the electrolyte is the main mode of heat accumulation in the early stage of thermal runaway, and when the heat accumulation causes the temperature to reach a certain critical value, the violent reaction between the positive electrode and the electrolyte is triggered. The extent and timing of the heat production behaviour of the battery host material is closely related to the SOC, and with limited electrolyte content, there is a competitive relationship between the positive and negative electrodes and the electrolyte reaction, leading to different SOC batteries exhibiting different heat production characteristics. In addition, the above findings are correlated with the battery failure mechanisms through heating experiments of the battery monomer. The study of the electro-thermal properties of the main materials in this paper provides a strategy for achieving early warning and suppression of thermal runaway in batteries.
ABSTRACT
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.
