摘要
能源储存系统是电动汽车、电子设备等高新技术的重要基础。近年来基于机器学习的电池设计能够快速连结材料微观结构-材料微观性能-电池宏观性能的复杂关系,成为了热点研究。本文从能源电池的微观材料设计和宏观状态预测两方面系统性地综述了电池设计中机器学习的应用现状和前景,概括综述了机器学习电池设计的研究数据来源、算法的优缺点及其在电池领域的应用场景以及近年来的相关创新性工作及其展望,以期为机器学习在能源储存系统的宏微观设计提供了参考。
The energy storage systems are an important basis for electric vehicles and electronic devices.The existing battery design based on machine learning is able to quickly connect the complex relationship among material microstructure,material properties,and battery macroscopic properties.This review represented the applications and prospects of machine learning in micro-material design and state estimation of batteries.The data sources of machine learning battery design,advantages and disadvantages of algorithms and their application scenarios in the battery field,related innovative work in recent years and their prospects were discussed.This review can provide a reference for machine learning in the macro-/micro-design of energy storage systems.
作者
李金金
蔡俊飞
韩彦强
汪志龙
陈安
叶思敏
LI Jinjin;CAI Junfei;HAN Yanqiang;WANG Zhilong;CHEN An;YE Simin(Shanghai Jiao Tong University and Electrical Engineering,Key Laboratory of Film and Microfabrication,Ministry of Education,Shanghai 200240,China)
出处
《硅酸盐学报》
EI
CAS
CSCD
北大核心
2023年第2期438-451,共14页
Journal of The Chinese Ceramic Society
基金
国家科技部重点研发计划(2021YFC2100100)
国家自然科学基金(21901157)
上海市自然科学基金(21JC1403400)。
关键词
机器学习
数据挖掘
电池材料
电池状态
machine learning
data mining
battery materials
battery state
