Machine learning-assisted phase-field simulation for predicting the impact of lithium-ion transport parameters on maximum battery dendrite height and space utilization rate
During the repeated charging and discharging processes of lithium-ion batteries,the uneven Li+deposition leads to uncontrollable dendrite growth on the electrode surface,severely affecting the battery's safety use.A phase-field simulation is a powerful tool for describing and predicting dendrite growth,while solving partial differential equations that describe the evolution of field variables requires high computational resources.Machine learning approaches that can quickly fit the underlying laws in historical data to predict material performance have been widely used in battery materials.This paper focuses on the effects of lithium-ion transport parameters on dendrite morphology.The corresponding dendrite images were first obtained from the phase-field simulation.The machine learning models were then trained to predict the dendrite metrics(maximum dendrite height and space utilization rate).The results show that the K-nearest neighbors model can accurately capture the relationship between lithium-ion transport parameters and dendrite metrics(R2=0.995 and 0.992).Meanwhile,the choice of machine learning models and the intervals of dendrite metrics all affect the accuracy of the results.This study can effectively save computational costs and contribute to the effective design of battery materials with dendrite suppression performance.
万方数据
维普
