Projects per year
Lithium-ion batteries are key energy storage technologies to promote the global clean energy process, particularly in power grids and electrified transportation. However, complex usage conditions and lack of precise measurement make it difficult for battery health estimation under field applications, especially for aging mode diagnosis. In a recent issue of Nature Communications, Dubarry et al. shed light on this issue by investigating the solution based on machine learning and battery digital twins. They achieved aging modes diagnosis of photovoltaics-connected batteries working for 2 years with more than 10,000 degradation paths under different seasons and cloud shading conditions.
Bibliographical noteFunding Information:
Y.C. and R.T. are supported by the “ SMART BATTERY” project, granted by Villum Foundation (222860), and X.H. is supported by the Natural Science Foundation of China (52111530194).
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- 2 Active
01/01/2022 → 31/12/2024
Project: PhD Project