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| 1 | Designing electrodes and electrolytes for batteries by leveraging deep learning显示文摘High-performance batteries are poised for electrification of vehicles and therefore mitigate greenhouse gas emissions,which,in turn,promote a sustainable future.However,the design of optimized batteries is challenging due to the nonlinear governing physics and electrochemistry.Recent advancements have demonstrated the potential of deep learning techniques in efficiently designing batteries,particularly in optimizing electrodes and electrolytes.This review provides comprehensive concepts and principles of deep learning and its application in solving battery-related electrochemical problems,which bridges the gap between artificial intelligence and electrochemistry.We also examine the potential challenges and opportunities associated with different deep learning approaches,tailoring them to specific battery requirements.Ultimately,we aim to inspire future advancements in both fundamental scientific understanding and practical engineering in the field of battery technology.Furthermore,we highlight the potential challenges and opportunities for different deep learning methods according to the specific battery demand to inspire future advancement in fundamental science and practical engineering. | Chenxi Sui Ziyang Jiang Genesis Higueros David Carlson Po-Chun Hsu | 2024 | Nano Research Energy2024，3，2： | 0 |
| 2 | 锂离子电池制造工艺仿真技术进展显示文摘锂离子电池的综合性能不仅取决于材料和结构的创新,还与制造工艺及相关设备技术的进步息息相关。目前电池制造厂商针对不同体系的电池工艺开发多采用穷举法进行实验试错,在工艺仿真技术方面还存在较大的发展空间。面向电池高质量制造发展和数智化升级的行业发展趋势,本文结合宏观电池制造设备和微观电池电极结构两个角度,对电池制造工艺仿真研究现状进行了系统总结,分析了各工序工艺仿真技术机理研究、结构发展及应用前景,并进一步指出当前研究的不足及未来的发展趋势,旨在为优化锂离子电池的制造流程和提高其综合性能提供理论参考。 | 陈飞 孔祥栋 孙跃东 韩雪冰 卢兰光 郑岳久 欧阳明高 | 2023 | 汽车工程2023，45，9： | 0 |
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