Machine learning-assisted phase-field simulation for predicting the impact of lithium-ion transport parameters on maximum battery dendrite height and space utilization rate

doi:10.19799/j.cnki.2095-4239.2024.0513

Abstract

Abstract:

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.

Key words: diffusion coefficient, ionic conductivity, dendrite metrics, phase-field simulation, machine learning

CLC Number:

TM 911

Yajie LI, Yiping WANG, Bin CHEN, Hailong LIN, Geng ZHANG, Siqi SHI. Machine learning-assisted phase-field simulation for predicting the impact of lithium-ion transport parameters on maximum battery dendrite height and space utilization rate[J]. Energy Storage Science and Technology, 2024, 13(9): 2864-2870.

Figures/Tables 6

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Table 1

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机器学习模型	最大枝晶生长高度		空间利用率
机器学习模型	R²	RMSE	R²	RMSE
AdaBoost	0.986	0.022	0.976	0.030
bagging	0.994	0.015	0.991	0.019
decision tree	0.983	0.024	0.980	0.028
gradient boosting	0.992	0.016	0.990	0.019
extremely randomized tree	0.995	0.013	0.990	0.020
KNN	0.995	0.013	0.992	0.017
MLP	0.983	0.024	0.979	0.028
random forest	0.994	0.014	0.991	0.019
Ridge	0.973	0.030	0.946	0.045
SVR	0.913	0.054	0.906	0.059

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