Citation: Xiangyue Li, Dexin Zhu, Kunmin Pan, Xiaoye Zhou, Jiaming Zhu, Yingxue Wang, Yongpeng Ren, Hong-Hui Wu. Identifying key determinants of discharge capacity in ternary cathode materials of lithium-ion batteries[J]. Chinese Chemical Letters, ;2025, 36(5): 109870. doi: 10.1016/j.cclet.2024.109870 shu

Identifying key determinants of discharge capacity in ternary cathode materials of lithium-ion batteries

Xiangyue Li ^{a, 1} ,
Dexin Zhu ^{b, 1} ,
Kunmin Pan ^{a, *,} ,
Xiaoye Zhou ^c ,
Jiaming Zhu ^{d, e} ,
Yingxue Wang ^{f, *,} ,
Yongpeng Ren ^a ,
Hong-Hui Wu ^{b, d, *,}

a.
School of Materials Science and Engineering, Henan University of Science and Technology, Luoyang 471003, China
b.
Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Carbon Neutrality, University of Science and Technology Beijing, Beijing 100083, China
c.
Department of Materials Science and Engineering, Shenzhen MSU-BIT University, Shenzhen 518172, China
d.
Institute of Materials Intelligent Technology, Liaoning Academy of Materials, Shenyang 110004, China
e.
School of Civil Engineering, Shandong University, Ji'nan 250061, China
f.
National Engineering Laboratory for Risk Perception and Prevention, Beijing 100041, China

pankunming2008@haust.edu.cn

wangyingxue@cetc.com.cn

wuhonghui@ustb.edu.cn

Received Date: 15 March 2024
Accepted Date: 8 April 2024
Available Online: 9 April 2024

Figures(4)

Although lithium-ion batteries (LIBs) currently dominate a wide spectrum of energy storage applications, they face challenges such as fast cycle life decay and poor stability that hinder their further application. To address these limitations, element doping has emerged as a prevalent strategy to enhance the discharge capacity and extend the durability of Li-Ni-Co-Mn (LNCM) ternary compounds. This study utilized a machine learning-driven feature screening method to effectively pinpoint four key features crucially impacting the initial discharge capacity (IC) of Li-Ni-Co-Mn (LNCM) ternary cathode materials. These features were also proved highly predictive for the 50^th cycle discharge capacity (EC). Additionally, the application of SHAP value analysis yielded an in-depth understanding of the interplay between these features and discharge performance. This insight offers valuable direction for future advancements in the development of LNCM cathode materials, effectively promoting this field toward greater efficiency and sustainability.
- LNCM ternary cathode material,
- Discharge capacity,
- Feature engineering,
- Machine learning,
- SHAP analysis
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