A Machine-Learning-Based Approach to Analyse the Feature Importance and Predict the Electrode Mass Loading of a Solid-State Battery
- DOI
- 10.3390/wevj15020072
- Published
- 02
- Container
- World Electric Vehicle Journal
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3390/wevj15020072,
title = {A Machine-Learning-Based Approach to Analyse the Feature Importance and Predict the Electrode Mass Loading of a Solid-State Battery},
author = {Wenming Dai and Yong Xiang and Wenyi Zhou and Qiao Peng},
year = {2024},
journal = {World Electric Vehicle Journal},
doi = {10.3390/wevj15020072},
url = {https://doi.org/10.3390/wevj15020072}
}RIS
TY - JOUR TI - A Machine-Learning-Based Approach to Analyse the Feature Importance and Predict the Electrode Mass Loading of a Solid-State Battery AU - Wenming Dai AU - Yong Xiang AU - Wenyi Zhou AU - Qiao Peng PY - 2024 JO - World Electric Vehicle Journal DO - 10.3390/wevj15020072 UR - https://doi.org/10.3390/wevj15020072 ER -
APA
Dai, W., Xiang, Y., Zhou, W., & Peng, Q. (2024). 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. https://doi.org/10.3390/wevj15020072
Source records
- doaj · retrieved 2026-09-26T03:41:08.766Z