Predicting Cycle Life of Lithium Metal Batteries Using Discharge/Charge Capacity and Voltage Relaxation Features Via Machine Learning
Authors: Si Qianli , Matsuda Shoichi , Yamaji Youhei , Momma Toshiyuki , Tateyama Yoshitaka 
Published: December 2024

Introduction

Precise estimation of battery cycle life is crucial for assessing quality and planning long-term battery management systems. Given the nonlinear nature of battery degradation, early and accurate cycle life prediction amid minimal degradation is exceptionally difficult. Machine learning (ML) methods, independent of specific mechanisms, can offer promising alternatives for predicting battery lifespan1. Li metal anode battery (LMB) is a promising next-generation battery because of the high energy density, whereas the safety concern with complex degradation mechanisms is still a crucial issue. For such challenge, ML-based cycle life prediction may play a role, though the LMB data are still limited. Here, we report our study on lifespan prediction of lithium metal-based batteries with high mass loading Ni-rich NMC electrode (NMC811/Li) by ML method through more battery features from various processes in the experimental charge/discharge cycles. We also explore the most crucial feature of NMC811/Li metal anode batteries' cycle life prediction with the feature importance analysis.

Methodology

In our project, 48 batteries are used to build ML model, 35 features are generated from the processes during cycle test, which are divided into discharge, charge, and voltage relaxation related features. The whole dataset is regarded as the training dataset, the testing datasets are generated by 4-fold cross validation of the whole dataset. ElasticNet and XGBoost ML methods are used in our study to predict the cycle life. Pearson’s correlation coefficient is used to define the relationship between features and cycle life. Feature importance analysis is done by XGBoost ML model under exhaustive feature selection method.

Results and Discussion

Firstly, we use discharge related features, charge capacity related features, and voltage relaxation features to predict the battery cycle life separately by ElasticNet linear regression model. We then find that discharge-related features give us the best prediction result compared to the other two feature sets. However, the prediction result is still not satisfactory. Hence, by calculating Pearson’s correlation coefficient we selected 12 features out of the 35 features which have strong or moderate correlation to cycle life. Through exhaustive feature selection, we generated totally 4095 feature subsets from the 12 features. We implement advanced XGBoost ML model on all the possible feature subsets to predict the cycle life. The result shows that XGBoost ML model has a significant increase of the prediction performance and there is one case which contains 6 features out of the 12 features can give us the best prediction result (Fig. (a)) which decrease the RMSE to 8.29, MAE to 6.45 and increase R2 to 0.89. In addition, we find logarithm of the minimum value of discharge capacity difference between 100 cycle and 10 cycle (Log(|min(100-10(V))|)) is the most important feature ranking for the 6 features in lifetime prediction (Fig. (b)). It contributes more than 44% for lifetime prediction. Finallly, we provide our model with 8 new NMC811/Li metal batteries as unseen data to test the prediction performance, The prediction result is shown in Fig. (c), the MAE and RMSE of the testing data are relatively small and the test error (Mean Absolute Percentage Error) equals to 6.6%, which suggests the model provides predictions with reasonable accuracy. This study underscores the successful application of ML in predicting cycle life for LMBs with high energy density design, paving the way for enhanced battery performance and longevity in practical applications.

References

Severson, Kristen A., et al. Nature Energy4(5) (2019): 383-391.

Figure 1