电池(电)
桥接(联网)
自动化
过程(计算)
工程类
计算机科学
系统工程
软件
转化式学习
软件设计
储能
工作(物理)
工程设计过程
在制品
大数据
作者
Zekai Liu,Genming Lai,Yunxing Zuo,Xiaohe Song,Qinghua Liu,Fan Zhang,Ziyi Wang,Ji Qi,Jiaxin Zheng,Jiadong Gong,Bo Xu,Chuying Ouyang
出处
期刊:National science open
[EDP Sciences]
日期:2025-11-01
卷期号:4 (6): 20250062-20250062
被引量:3
摘要
This review presents Battery Design Automation (BDA) as a transformative AI-driven paradigm for the next-generation lithium-ion battery research and development. Addressing the intricacy of the problems and challenges in developing lithium-ion batteries with better performance, which are cross-scale, long-process, and multi-factor, BDA integrates multi-scale simulations and artificial intelligence into a unified platform. It ranges from atomic-scale material screening to system-level performance prediction. By bridging the gap between scientific innovation and industrial applications, BDA facilitates the development of lithium-ion battery, enhancing its efficiency, safety, and energy density. The paper outlines BDA’s architecture, core technologies, current progress, and future challenges, highlighting its potential to revolutionize the battery design process and strengthen the pivotal role of lithium-ion battery in energy storage technology.
科研通智能强力驱动
Strongly Powered by AbleSci AI