A convenient method to predict the macroscopic elastic performance of composite containing interphase was developed in this paper based on a new modified random sequential adsorption method and multiple nonlinear regression analysis. First, a three-phase micromechanical model with randomly distributed fibers was established with a new modified random sequential adsorption method named the Moving Window Method. Second, the macroscopic elastic behaviors of T300/914C were predicted based on micromechanical parameters of constituent materials using energy method, and the influence of the randomness of fiber distribution on the prediction was analyzed. Third, the effects of interphase thickness, interphase modulus and Poisson’s ratio on the macroscopic elastic properties of the composite were studied by changing these interphase micromechanical parameters gradually within certain ranges. Finally, the multiple nonlinear regression models that describe the relationship between the macroscopic elastic properties of T300/914C and micromechanical characteristic parameters of the interphase were established using a limited number of data from numerical simulation. Results indicate that the relative errors for the longitudinal modulus and the major Poisson's ratio are within ±1% while for the transverse modulus, shear modulus and Poisson’s ratio at cross section, the relative errors are within 5%.

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In order to study the degradation mechanism of Lithium-ion batteries subjected to vibration aging in actual use and also to achieve capacity estimation and prediction, the following work has been done: First, the road spectra of two commonly seen domestic roads in China are collected in the field and modeled on a six-degree-of-freedom motion platform as the vibration working conditions of the batteries. Secondly, aging cycle experiments were conducted on batteries with different placement directions (X-axis direction, Y-axis direction, and Z-axis direction) under two vibration conditions, and the effects of experimental conditions on the decline results were analyzed. Thirdly, quantification of battery decline patterns to analyze the main causes of battery capacity decline. Then, through further analysis of the two vibration conditions on the lithium battery by in-situ and ex-situ methods as its internal mechanisms. Finally, the quantified results were input into the GAN-LSTM prediction model to predict the capacity, and the errors of 20 predictions are: The average values are 2.8561% for group X, 2.7997% for group Y, 3.0182% for group Z, and 2.9478% for group N, which meet the requirements of battery management system estimation. This paper provides a basis for the study of aging mechanism and capacity estimation of lithium-ion batteries under vibration aging conditions, which helps manufacturers to package batteries more rationally to extend battery life and develop BMS-related strategies.

Non-member He Mingze, Non-member Wang Yangyang, Non-member In recent years, electric vehicles have developed rapidly. However, as the power source of electric vehicles, lithium battery has poor performance at low temperature, and has some problems such as reduced capacity and increased internal resistance. In this paper, two thermal environments, including natural convection condition and near-adiabatic condition, are established, and discharge capacity test and cyclic dynamic stress test (DST) are conducted for lithium iron phosphate batteries. The actual effect of near-adiabatic conditions is analyzed from three perspectives: battery surface temperature, terminal voltage and EIS. The results show that the near-adiabatic working condition has almost no effect on the discharge capacity at the standard discharge current (0.5 • C) under the same temperature conditions, ranging from −15 to 25 • C. In the low-temperature − 10 and − 15 • C cyclic dynamic stress test, compared with the natural convection working condition, the near-adiabatic condition has a rapid rise in terminal voltage one cycle earlier, causing the cycle to stop. Also, the polarization impedance increases at least twice after the cycle under the near-adiabatic condition. Finally, according to the analysis of variance (ANOVA), the near-adiabatic condition has a significant effect on voltage and polarization resistance at low temperatures. Still, it has almost no effect on ohmic resistance and discharge capacity.

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