Battery Electrode Mass Loading Prognostics and Analysis for Lithium-Ion Battery–Based Energy Storage Systems

Tao Chen,Meng Song,Hongxun Hui,Huan Long

doi:10.3389/fenrg.2021.754317

Abstract

With the rapid development of renewable energy, the lithium-ion battery has become one of the most important sources to store energy for many applications such as electrical vehicles and smart grids. As battery performance would be highly and directly affected by its electrode manufacturing process, it is vital to design an effective solution for achieving accurate battery electrode mass loading prognostics at early manufacturing stages and analyzing the effects of manufacturing parameters of interest. To achieve this, this study proposes a hybrid data analysis solution, which integrates the kernel-based support vector machine (SVM) regression model and the linear model–based local interpretable model-agnostic explanation (LIME), to predict battery electrode mass loading and quantify the effects of four manufacturing parameters from mixing and coating stages of the battery manufacturing chain. Illustrative results demonstrate that the derived hybrid data analysis solution is capable of not only providing satisfactory battery electrode mass loading prognostics with over a 0.98 R-squared value but also effectively quantifying the effects of four key parameters (active material mass content, solid-to-liquid ratio, viscosity, and comma-gap) on determining battery electrode properties. Due to the merits of explainability and data-driven nature, the design data–driven solution could assist engineers to obtain battery electrode information at early production cases and understand strongly coupled parameters for producing batteries, further benefiting the improvement of battery performance for wider energy storage applications.

Highlights

The lithium-ion (Li-ion) battery has become a popular energy storage technology for many sustainable energy applications, such as transportation electrification (Su et al, 2011; Chen et al, 2016) and a smart grid (Chen and Su, 2018; Hu et al, 2020; Hu et al, 2021a), due to the advantages of a low discharge rate and high energy density (Wang et al, 2020; Xie et al, 2020)
This study proposes a hybrid data analysis solution by combining the benefits of the support vector machine (SVM) and local interpretable model-agnostic explanation (LIME) to benefit battery electrode property predictions and a parameter effect analysis
A structural risk minimization way is utilized in the SVM to generate the upper bound on the generalization error

Summary

Introduction

The lithium-ion (Li-ion) battery has become a popular energy storage technology for many sustainable energy applications, such as transportation electrification (Su et al, 2011; Chen et al, 2016) and a smart grid (Chen and Su, 2018; Hu et al, 2020; Hu et al, 2021a), due to the advantages of a low discharge rate and high energy density (Wang et al, 2020; Xie et al, 2020). The battery electrode manufacturing process involves multidisciplinary operations from electrical, chemical, thermal, and mechanical engineers, which would contain numerous individual manufacturing stages with lots of strongly coupled parameters, over 600 in total (Wu et al, 2019). The current solutions to analyze the contributions and importance of these parameters are still mainly dependent on long-term experimental experiences and trial and error approaches (Kwade et al, 2018). These conventional solutions are significantly time-consuming and labor-intensive. It is very meaningful to design a suitable data analysis solution that could efficiently and automatically perform an interpretable analysis for explaining the contributions and importance of parameters within the battery manufacturing process

Methods

Results

Conclusion