Kavli Affiliate: Feng Wang
| First 5 Authors: Ruohan Guo, Feng Wang, Cungang Hu, Weixiang Shen,
| Summary:
Retired batteries (RBs) for second-life applications offer promising economic
and environmental benefits. However, accurate and efficient sorting of RBs with
discrepant characteristics persists as a pressing challenge. In this study, we
introduce a data driven based electrode aging assessment approach to address
this concern. To this end, a number of 15 feature points are extracted from
battery open circuit voltage (OCV) curves to capture their characteristics at
different levels of aging, and a convolutional neural network with an optimized
structure and minimized input size is established to relocate the relative
positions of these OCV feature points. Next, a rapid estimation algorithm is
proposed to identify the three electrode aging parameters (EAPs) which best
reconstruct the 15 OCV feature points over the entire usable capacity range.
Utilizing the three EAPs as sorting indices, we employ an adaptive affinity
propagation algorithm to cluster RBs without the need for pre-determining the
clustering number. Unlike conventional sorting methods based solely on battery
capacity, the proposed method provides profound insights into electrode aging
behaviors, minimizes the need for constant-current charging data, and supports
module/pack-level tests for the simultaneous processing of high volumes of RBs.
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