A Machine-Learning-Based Approach to Analyse the Feature Importance and Predict the Electrode Mass Loading of a Solid-State Battery
Solid-state batteries are currently developing into one of the most promising battery types for both the electrification of transport and for energy storage applications due to their high energy density and safe operating behaviour. The performance of solid-state batteries is largely determined by t...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/15/2/72 |
| _version_ | 1797296779550523392 |
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| author | Wenming Dai Yong Xiang Wenyi Zhou Qiao Peng |
| author_facet | Wenming Dai Yong Xiang Wenyi Zhou Qiao Peng |
| author_sort | Wenming Dai |
| collection | DOAJ |
| description | Solid-state batteries are currently developing into one of the most promising battery types for both the electrification of transport and for energy storage applications due to their high energy density and safe operating behaviour. The performance of solid-state batteries is largely determined by the manufacturing process, particularly in the production of electrodes. However, efficiently analysing the effects of key manufacturing features and predicting the mass loading of electrodes in the early stages of battery manufacturing remain a major challenge. In this study, a machine-learning-based approach is proposed to effectively analyse the importance of manufacturing features and accurately predict the mass loading of electrodes. Specifically, the importance of four key features during the manufacturing process of solid-state batteries is first quantified and analysed using a machine-learning-based method to analyse the importance of features. Then, four effective machine-learning-based regression methods, including decision tree, boosted decision tree, support vector regression and Gaussian process regression, are used to predict the mass loading of the electrodes in the mixing and coating stages. The comparative results show that the developed machine-learning-based approach is able to provide a satisfactory prediction of the electrode mass loading of a solid-state battery with 0.995 R<sup>2</sup> while successfully quantifying the importance of four key features in the early manufacturing stages. Due to the advantages of its data-driven nature, the developed machine-learning-based approach can efficiently assist engineers in monitoring/predicting the electrode mass loading of solid-state batteries and analysing/quantifying the importance of manufacturing features of interest. This could benefit the production of solid-state batteries for further energy storage applications. |
| first_indexed | 2024-03-07T22:09:45Z |
| format | Article |
| id | doaj.art-0ae7ecd429a54d26910b1db09f3bbeaa |
| institution | Directory Open Access Journal |
| issn | 2032-6653 |
| language | English |
| last_indexed | 2024-03-07T22:09:45Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj.art-0ae7ecd429a54d26910b1db09f3bbeaa2024-02-23T15:38:16ZengMDPI AGWorld Electric Vehicle Journal2032-66532024-02-011527210.3390/wevj15020072A Machine-Learning-Based Approach to Analyse the Feature Importance and Predict the Electrode Mass Loading of a Solid-State BatteryWenming Dai0Yong Xiang1Wenyi Zhou2Qiao Peng3College of Control Science and Engineering, Zhejiang University, Hangzhou 310000, ChinaAdvanced Energy Technology Research Institute, University of Electronic Science and Technology, Chengdu 611731, ChinaYixin Semiconductor (Jiaxing) Co., Ltd., Zhejiang 200540, ChinaQueen’s Business School, Queen’s University Belfast, Belfast BT7 1NN, UKSolid-state batteries are currently developing into one of the most promising battery types for both the electrification of transport and for energy storage applications due to their high energy density and safe operating behaviour. The performance of solid-state batteries is largely determined by the manufacturing process, particularly in the production of electrodes. However, efficiently analysing the effects of key manufacturing features and predicting the mass loading of electrodes in the early stages of battery manufacturing remain a major challenge. In this study, a machine-learning-based approach is proposed to effectively analyse the importance of manufacturing features and accurately predict the mass loading of electrodes. Specifically, the importance of four key features during the manufacturing process of solid-state batteries is first quantified and analysed using a machine-learning-based method to analyse the importance of features. Then, four effective machine-learning-based regression methods, including decision tree, boosted decision tree, support vector regression and Gaussian process regression, are used to predict the mass loading of the electrodes in the mixing and coating stages. The comparative results show that the developed machine-learning-based approach is able to provide a satisfactory prediction of the electrode mass loading of a solid-state battery with 0.995 R<sup>2</sup> while successfully quantifying the importance of four key features in the early manufacturing stages. Due to the advantages of its data-driven nature, the developed machine-learning-based approach can efficiently assist engineers in monitoring/predicting the electrode mass loading of solid-state batteries and analysing/quantifying the importance of manufacturing features of interest. This could benefit the production of solid-state batteries for further energy storage applications.https://www.mdpi.com/2032-6653/15/2/72battery manufacturingfeature importance analysiselectrode mass loading predictionmachine learningregression model |
| spellingShingle | Wenming Dai Yong Xiang Wenyi Zhou Qiao Peng A Machine-Learning-Based Approach to Analyse the Feature Importance and Predict the Electrode Mass Loading of a Solid-State Battery World Electric Vehicle Journal battery manufacturing feature importance analysis electrode mass loading prediction machine learning regression model |
| title | A Machine-Learning-Based Approach to Analyse the Feature Importance and Predict the Electrode Mass Loading of a Solid-State Battery |
| title_full | A Machine-Learning-Based Approach to Analyse the Feature Importance and Predict the Electrode Mass Loading of a Solid-State Battery |
| title_fullStr | A Machine-Learning-Based Approach to Analyse the Feature Importance and Predict the Electrode Mass Loading of a Solid-State Battery |
| title_full_unstemmed | A Machine-Learning-Based Approach to Analyse the Feature Importance and Predict the Electrode Mass Loading of a Solid-State Battery |
| title_short | A Machine-Learning-Based Approach to Analyse the Feature Importance and Predict the Electrode Mass Loading of a Solid-State Battery |
| title_sort | machine learning based approach to analyse the feature importance and predict the electrode mass loading of a solid state battery |
| topic | battery manufacturing feature importance analysis electrode mass loading prediction machine learning regression model |
| url | https://www.mdpi.com/2032-6653/15/2/72 |
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