Non-destructive degradation pattern decoupling for early battery trajectory prediction <i>via</i> physics-informed learning
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
Abstract
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.
Topics & Concepts
Decoupling (probability)TrajectoryBattery (electricity)Degradation (telecommunications)Artificial intelligenceComputer sciencePhysicsEngineeringControl engineeringElectrical engineeringQuantum mechanicsPower (physics)Advanced Battery Technologies ResearchAdvancements in Battery MaterialsAdvanced battery technologies research