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Interpretable machine learning for battery capacities prediction and coating parameters analysis
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Liu, Kailong, Niri, Mona Faraji, Apachitei, Geanina, Lain, Michael J., Greenwood, David G. and Marco, James (2022) Interpretable machine learning for battery capacities prediction and coating parameters analysis. Control Engineering Practice, 124 . 105202. doi:10.1016/j.conengprac.2022.105202
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WRAP-Interpretable-machine-learning-battery-prediction-coating-analysis-2022.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (3013Kb) | Preview |
Official URL: http://dx.doi.org/10.1016/j.conengprac.2022.105202
Abstract
Battery manufacturing plays a direct and pivotal role in determining battery performance, which, in turn, significantly affects the applications of battery-related energy storage systems. As a complicated process that involves chemical, mechanical and electrical operations, effective battery property predictions and reliable analysis of strongly-coupled battery manufacturing parameters or variables become the key but challenging issues for wider battery applications. In this paper, an interpretable machine learning framework that could effectively predict battery product properties and explain dynamic effects, as well as interactions of manufacturing parameters is proposed. Due to the data-driven nature, this framework can be easily adopted by engineers as no specific battery manufacturing mechanism knowledge is required. Reliable battery manufacturing dataset particularly for coating (one key stage) collected from a real battery manufacturing chain is adopted to evaluate the proposed framework. Illustrative results demonstrate that three types of battery capacities including cell capacity, gravimetric capacity, and volumetric capacity can be accurately predicted with over 0.98 at the battery early-manufacturing stage. Besides, information regarding how the variations of coating mass, thickness, and porosity affect these battery capacities is effectively identified, while interactions of these coating parameters can be also quantified. The developed framework makes the data-driven model become more interpretable and opens a promising way to quantify the interactions of battery manufacturing parameters and explain how the variations of these parameters affect final battery properties. This could assist engineers to obtain critical insights to understand the underlying complicated battery material and manufacturing behavior, further benefiting smart control of battery manufacturing.
| Item Type: | Journal Article | ||||||||
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||
| Journal or Publication Title: | Control Engineering Practice | ||||||||
| Publisher: | Elsevier Ltd | ||||||||
| ISSN: | 0967-0661 | ||||||||
| Official Date: | July 2022 | ||||||||
| Dates: |
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| Volume: | 124 | ||||||||
| Article Number: | 105202 | ||||||||
| DOI: | 10.1016/j.conengprac.2022.105202 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Open Access |
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