电化学(中英文) ›› 2022, Vol. 28 ›› Issue (12): 2219003. doi: 10.13208/j.electrochem.2219003
所属专题: “下一代二次电池”专题文章; iSAIEC 2023; “AI for Electrochemistry”专题文章
朱振威1, 邱景义1, 王莉2, 曹高萍1, 何向明2, 王京3, 张浩1,*()
收稿日期:
2022-08-31
修回日期:
2022-09-23
出版日期:
2022-12-28
发布日期:
2022-12-28
Zhen-Wei Zhu1, Jing-Yi Qiu1, Li Wang2, Gao-Ping Cao1, Xiang-Ming He2, Jing Wang3, Hao Zhang1,*()
Received:
2022-08-31
Revised:
2022-09-23
Published:
2022-12-28
Online:
2022-12-28
Contact:
*Tel: (86-10)66748524, E-mail: 摘要:
锂离子电池已成为解决现代社会储能问题的最佳解决方案之一。然而,电池材料和器件开发都是复杂的多变量问题,传统的依赖研究人员进行实验的试错法在电池性能提升方面遇到了瓶颈。人工智能(AI)具有强大的高速、海量数据处理能力,是上述突破研究瓶颈的最具潜力的技术。其中,机器学习 (ML) 算法在评估多维数据变量和集合之间的组合关联方面的独特优势有望帮助研究人员发现不同因素之间的相互作用规律并阐明材料合成和设备制造的机制。本综述总结了锂离子电池传统研究方法遇到的各种挑战,并详细介绍了人工智能在电池材料研究、电池器件设计与制造、材料与器件表征、电池循环寿命与安全性评估等方面的应用。最重要的是,我们介绍了AI和ML在电池研究中面临的挑战,并讨论了它们应用的缺点和前景。我们相信,未来实验科学家、数学建模专家和AI专家之间更紧密的合作将极大地促进AI和ML方法用以解决传统方法难以克服的电池和材料问题。
朱振威, 邱景义, 王莉, 曹高萍, 何向明, 王京, 张浩. 人工智能在锂离子电池研发中的应用[J]. 电化学(中英文), 2022, 28(12): 2219003.
Zhen-Wei Zhu, Jing-Yi Qiu, Li Wang, Gao-Ping Cao, Xiang-Ming He, Jing Wang, Hao Zhang. Application of Artificial Intelligence to Lithium-Ion Battery Research and Development[J]. Journal of Electrochemistry, 2022, 28(12): 2219003.
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