Capacity estimation of retired lithium-ion batteries using random charging segments from massive real-world data
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影响因子：7.9
DOI码：10.1016/j.xcrp.2025.102444
教研室：P. Zhou, J. Liang, Y. Liu, J. Wu, Q. Song, & X. Li
发表刊物：Cell Reports Physical Science
摘要：The repurposing of retired lithium-ion batteries from electric vehicles is a critical strategy for reducing carbon emissions. Capacity estimation plays a key role in facilitating this process. However, achieving an accurate estimation remains challenging due to the inherent variability of batteries and the complexity of real-world conditions. Existing methods often rely on small datasets and feature extraction, which limits their generalizability and leads to high testing costs. To overcome these limitations, we compile the two largest known datasets of retired lithium-ion batteries and develop a tailored neural network model capable of directly capturing both long-term and short-term temporal patterns. By utilizing randomly segmented charging curves as input, the proposed model reduces the cost of charging-discharging tests in practical applications. The experimental results demonstrate that the model achieves satisfactory performance in capacity estimation, supporting the optimization of resource use and reduction of environmental pollution.
论文类型：期刊论文
学科门类：工学
文献类型：J
页面范围：102444
是否译文：否
发表时间：2025-02-11
收录刊物：SCI
发布期刊链接：https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(25)00043-8