Bio-inspired vascularized electrodes for high-performance fast-charging batteries designed by deep learning

TL;DR

This paper introduces bio-inspired vascularized electrodes optimized by deep learning to enhance lithium-ion battery fast-charging performance, achieving significant capacity improvements and providing a new methodology for battery design.

Contribution

It presents a novel bio-inspired vascularized electrode design optimized via neural networks, enabling faster charging and improved capacity in lithium-ion batteries.

Findings

66% increase in charging capacity at 3.2C rate

Optimized vascular structures reduce IR drop and tortuosity

Deep learning accelerates structural optimization process

Abstract

Slow ionic transport and high voltage drop (IR drop) of homogeneous porous electrodes are the critical causes of severe performance degradation of lithium-ion (Li-ion) batteries under high charging rates. Herein, we demonstrate that a bio-inspired vascularized porous electrode can simultaneously solve these two problems by introducing low tortuous channels and graded porosity. To optimize the vasculature structural parameters, we employ artificial neural networks (ANNs) 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 of charging capacity than the traditional homogeneous cell under 3.2C current density. This research provides an innovative methodology to solve the fast-charging problem in batteries and broaden the applicability of deep learning algorithm to different scientific or engineering areas.