The content presents a novel approach for sorting retired lithium-ion batteries (RBs) based on their electrode aging characteristics. The key highlights are:
Three electrode aging parameters (EAPs) - loss of lithium inventory (LLI), loss of active material (LAM) of positive electrode (PE), and LAM of negative electrode (NE) - are introduced to capture the root causes of battery capacity decay and open circuit voltage (OCV) distortion.
A convolutional neural network (CNN) is developed to relocate the relative positions of 15 OCV feature points, which are then used to rapidly estimate the three EAPs through a hybrid optimization algorithm.
An adaptive affinity propagation (adAP) clustering algorithm is employed to sort RBs using the estimated EAPs as indices, without the need for pre-determining the clustering number.
The proposed approach provides profound insights into electrode aging behaviors, minimizes the need for constant-current charging data, and supports module/pack-level tests for high-volume RB sorting, showing great potential for industrial applications.
To Another Language
from source content
arxiv.org
Thông tin chi tiết chính được chắt lọc từ
by Ruohan Guo,F... lúc arxiv.org 04-22-2024
https://arxiv.org/pdf/2404.12769.pdfYêu cầu sâu hơn