Bio‐Inspired Computational Design of Vascularized Electrodes for High‐Performance Fast‐Charging Batteries Optimized by Deep Learning

Slow ionic transport and high voltage drop (IR drop) of homogeneous porous electrodes are the critical causes of severe performance degradation of lithium‐ion batteries at high charging rates. Herein, it is numerically demonstrated that a bio‐inspired vascularized porous electrode can simultaneously...

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Bibliographic Details
Published in:Advanced energy materials 2022-02, Vol.12 (6), p.n/a
Main Authors: Sui, Chenxi, Li, Yao‐Yu, Li, Xiuqiang, Higueros, Genesis, Wang, Keyu, Xie, Wanrong, Hsu, Po‐Chun
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
bio‐inspired vasculature
Charging
Deep learning
Design criteria
fast‐charging batteries
Lithium-ion batteries
Machine learning
Optimization
Parameter sensitivity
Parameter uncertainty
Performance degradation
Sensitivity analysis
Voltage drop
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Summary:Slow ionic transport and high voltage drop (IR drop) of homogeneous porous electrodes are the critical causes of severe performance degradation of lithium‐ion batteries at high charging rates. Herein, it is numerically demonstrated that a bio‐inspired vascularized porous electrode can simultaneously solve these two problems by introducing low tortuous channels and graded porosity, which can be verified by porous electrode theory. To optimize the vasculature structural parameters, artificial neural networks are employed to accelerate the computation of possible structures with high accuracy. Furthermore, an inverse‐design searching library is compiled to find the optimal vascular structures under different industrial fabrication and design criteria. The prototype delivers a customizable package containing optimal geometric parameters and their uncertainty and sensitivity analysis. Finally, the full‐vascularized cell shows a 66% improvement in charging capacity compared to the traditional homogeneous cell under 3.2 C current density in a numerical simulation. This computational research provides an innovative methodology to solve the fast‐charging problem in batteries and broaden the applicability of deep learning algorithms to different scientific or engineering areas. Nature is always full of profound wisdom to solve complicated engineering problems. Inspired by vascular structures, this work uses a deep neural network to computationally design vascularized porous electrodes with low tortuosity and graded porosity for fast charging batteries. The vascularized full‐cell battery demonstrates a 66% improvement in charging capacity under a 3.2 C charging rate.
ISSN:1614-6832
1614-6840
DOI:10.1002/aenm.202103044