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.
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by Ruohan Guo,F... om arxiv.org 04-22-2024
https://arxiv.org/pdf/2404.12769.pdfDiepere vragen