We have proposed a data-driven approach for designing the mesoscale porous structures of Li-ion battery electrodes, using three-dimensional virtual structures and machine learning techniques. Over 2000 artificial 3D structures, assuming a positive electrode composed of randomly packed spheres as the active material particles, are generated, and the charge/discharge specific resistance has been evaluated using a simplified physico-chemical model. The specific resistance from Li diffusion in the active material particles (diffusion resistance), the transfer specific resistance of Li+ in the electrolyte (electrolyte resistance), and the reaction resistance on the interface between the active material and electrolyte are simulated, based on the mass balance of Li, Ohm’s law, and the linearized Butler–Volmer equation, respectively. Using these simulation results, regression models, using an artificial neural network (ANN), have been created in order to predict the charge/discharge specific resistance from porous structure features. In this study, porosity, active material particle size and volume fraction, pressure in the compaction process, electrolyte conductivity, and binder/additives volume fraction are adopted, as features associated with controllable process parameters for manufacturing the battery electrode. As a result, the predicted electrode specific resistance by the ANN regression model is in good agreement with the simulated values. Furthermore, sensitivity analyses and an optimization of the process parameters have been carried out. Although the proposed approach is based only on the simulation results, it could serve as a reference for the determination of process parameters in battery electrode manufacturing.

Lithium (Li)-ion battery thermal management systems play an important role in electric vehicles because the performance and lifespan of the batteries are affected by the battery temperature. This study proposes a framework to establish equivalent circuit models (ECMs) that can reproduce the multi-physics phenomenon of Li-ion battery packs, which includes liquid cooling systems with a unified method. We also demonstrate its utility by establishing an ECM of the thermal management systems of the actual battery packs. Experiments simulating the liquid cooling of a battery pack are performed, and a three-dimensional (3D) model is established. The 3D model reproduces the heat generated by the battery and the heat transfer to the coolant. The results of the 3D model agree well with the experimental data. Further, the relationship between the flow rate and pressure drop or between the flow rate and heat transfer coefficients is predicted with the 3D model, and the data are used for the ECM, which is established using MATLAB Simulink. This investigation confirmed that the ECM’s accuracy is as high as the 3D model even though its computational costs are 96% lower than the 3D model.

Analyzing the structural integrity of ferritic steel structures subjected to large temperature variations requires the collection of the fracture toughness (K_Jc) of ferritic steels in the ductile-to-brittle transition region. Consequently, predicting K_Jc from minimal testing has been of interest for a long time. In this study, a Windows-ready K_Jc predictor based on tensile properties (specifically, yield stress σ_YSRT and tensile strength σ_BRT at room temperature (RT) and σ_YS at K_Jc prediction temperature) was developed by applying an artificial neural network (ANN) to 531 K_Jc data points. If the σ_YS temperature dependence can be adequately described using the Zerilli–Armstrong σ_YS master curve (MC), the necessary data for K_Jc prediction are reduced to σ_YSRT and σ_BRT. The developed K_Jc predictor successfully predicted K_Jc under arbitrary conditions. Compared with the existing ASTM E1921 K_Jc MC, the developed K_Jc predictor was especially effective in cases where σ_B/σ_YS of the material was larger than that of RPV steel.

Lithium dendrite growth in Li-ion batteries is one of the most dangerous phenomena because it can cause inner short circuits and thermal runaways. However, the nucleation and growth of the dendrites are difficult to predict because of their complex behaviors, which depend on several factors such as the charging conditions and electrode-design parameters. In this study, a comprehensive mathematical model has been developed for Li-deposition on Li-ion battery electrodes. The model is based on the Single Particle model (to evaluate the Li-ion concentration fields) combined with dendrite-growth models based on the electrochemical and crystal growth theory. The effect of the SEI thickness distribution, the charging C-rate and cut-off voltage on the growth of dendrite tips have been statistically evaluated, and the risk of short circuit is discussed. The study focuses on the effects of the SEI-thickness distribution on the timing of the SEI breaking and density of the dendrite formation.

Physics-based simulations of lithium-ion battery thermal runaway (TR) and thermal propagation (TP) enable the assessment of diverse temperature behaviors among individual cells. These behaviors are primarily driven by variations in thermal properties and the amount of heat released during thermal decomposition. However, given the inherent variability in thermal property measurements, the specific values adopted can lead to substantial differences in predicted temperature behavior. In this study, we developed a 1-dimensional TP model for an array of three prismatic lithium-ion battery cells, in consideration of the uncertainty of key thermal parameters including specific heat, thermal conductivity, activation energy, and the latent heat of the thermal decomposition reaction. The validity of the model and the identification of calibration parameters are ensured through comparison with experimentally measured temperatures. We evaluated the influence of these parameter variations on the temperature and thermal runaway behavior of each cell. Our findings indicate that the variation in thermal runaway timing increases with distance from the trigger cell, and the probability of thermal runaway in the end cell was significantly higher than in the center cell. A sensitivity analysis using a surrogate model revealed that cell temperature is more sensitive to variations in thermal conductivity and latent heat than to variations in specific heat and activation energy.