Non-destructive degradation pattern decoupling for early battery trajectory prediction via physics-informed learning
Energy Environ. Sci., 2025, 18,1544-1559DOI: 10.1039/D4EE03839H, PaperShengyu Tao, Mengtian Zhang, Zixi Zhao, Haoyang Li, Ruifei Ma, Yunhong Che, Xin Sun, Lin Su, Chongbo Sun, Xiangyu Chen, Heng Chang, Shiji Zhou, Zepeng Li, Hanyang Lin, Yaojun Liu, Wenjun Yu, Zhongling Xu, Han Hao, Scott Moura, Xuan Zhang, Yang Li, Xiaosong Hu, Guangmin ZhouThe paper proposes a physics-informed model to predict battery lifetime trajectories by computing thermodynamic and kinetic parameters, saving costly data that has not been established for sustainable manufacturing, reuse, and recycling.The content of this RSS Feed (c) The Royal Society of Chemistry
Energy Environ. Sci., 2025, 18,1544-1559
DOI: 10.1039/D4EE03839H, Paper
DOI: 10.1039/D4EE03839H, Paper
Shengyu Tao, Mengtian Zhang, Zixi Zhao, Haoyang Li, Ruifei Ma, Yunhong Che, Xin Sun, Lin Su, Chongbo Sun, Xiangyu Chen, Heng Chang, Shiji Zhou, Zepeng Li, Hanyang Lin, Yaojun Liu, Wenjun Yu, Zhongling Xu, Han Hao, Scott Moura, Xuan Zhang, Yang Li, Xiaosong Hu, Guangmin Zhou
The paper proposes a physics-informed model to predict battery lifetime trajectories by computing thermodynamic and kinetic parameters, saving costly data that has not been established for sustainable manufacturing, reuse, and recycling.
The content of this RSS Feed (c) The Royal Society of Chemistry
The paper proposes a physics-informed model to predict battery lifetime trajectories by computing thermodynamic and kinetic parameters, saving costly data that has not been established for sustainable manufacturing, reuse, and recycling.
The content of this RSS Feed (c) The Royal Society of Chemistry
