AI for Science时代下的电池平台化智能研发  

Intelligent R&D of battery design automation in the era of artificial intelligence

作　　者：谢莹莹 邓斌 张与之 王晓旭 张林峰 XIE Yingying;DENG Bin;ZHANG Yuzhi;WANG Xiaoxu;ZHANG Linfeng(Beijing DP Technology Co.,Ltd,Beijing 100080,China;AI for Science Institute,Beijing 100080,China)

机构地区：[1]北京深势科技有限公司,北京100080 [2]北京科学智能研究院,北京100080

出　　处：《储能科学与技术》2024年第9期3182-3197,共16页Energy Storage Science and Technology

摘　　要：在AI for Science时代,电池设计自动化智能研发(battery design automation,BDA)平台通过整合先进的人工智能技术,为电池研发领域带来了革命性进展。BDA平台覆盖了文献调研、实验设计、合成制备、表征测试和分析优化这五个电池研发的关键环节,利用机器学习、多尺度建模、预训练模型等先进算法,结合软件工程开发用户交互友好的工具,加速从理论设计到实验验证的整个电池研发周期。通过自动化的实验设计、合成制备、表征测试和性能优化,BDA平台不仅提升了研发效率,还提高了电池设计的精确度和可靠性,推动了电池技术向更高能量密度、更长循环寿命和更低成本的方向发展。In the era of artificial intelligence(AI)in science,the battery design automation(BDA)intelligent R&D platform has revolutionized battery R&D by integrating advanced AI technologies.The BDA platform covers five key aspects of battery R&D:Read,Design,Make,Test,and Analysis.It uses advanced algorithms,such as machine learning,multi-scale modeling,and pre-training models,combined with software engineering to develop userfriendly tools for accelerating the complete battery R&D cycle from theoretical design to experimental validation.Through synthesis and preparation,characterization testing,performance optimization,and automated experimental design,the BDA platform enhances R&D efficiency and improves the accuracy and reliability of battery design,which results in battery technology with higher energy density,longer cycle life,and lower costs.

关 键 词：AI for Science 电池 智能研发 机器学习 BDA 多尺度 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]