Multi-feature weighted battery pack consistency evaluation based on massive real-world data
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影响因子：8.9
DOI码：10.1016/j.est.2025.115919
教研室：Z. Gao, P. Chang, Y. Peng, & J. Wu
发表刊物：Journal of Energy Storage
关键字：Lithium-ion battery; Battery consistency; Multi-feature weighting; Principal component analysis; Deep learning
摘要：The widespread application of electric vehicles and energy storage systems has led to an increasing use of battery packs, and the problem of inconsistency among battery cells has become prominent. This issue stems from differences in manufacturing processes and usage conditions, and it severely affects the performance, safety, and service life of battery packs. Most existing studies are based on limited laboratory data and are unable to comprehensively analyze battery consistency, often neglecting the correlation of characteristics. This study proposes a consistency evaluation scheme based on information fusion, which comprehensively and accurately evaluates the consistency of battery packs in actual operation by integrating multiple factors, providing an effective guide for management optimization. Firstly, multi-dimensional consistency characteristics such as voltage, internal resistance, capacity, and temperature are comprehensively extracted, and a consistency score weighted by multiple characteristics is obtained through principal component analysis. Then, the score samples are optimized based on the Box-Cox transformation, and the consistency level is divided according to the normal distribution law. Finally, a mask-conformer deep learning model is constructed based on the characteristics of battery data to predict the consistency state. Experiments show that the proposed evaluation method can accurately distinguish the consistency state of batteries, and the mask-conformer model has excellent performance. It can directly predict from charging data without complex feature calculations, reducing the dependence on a large amount of operating data.
论文类型：期刊论文
学科门类：工学
文献类型：J
卷号：115
页面范围：115919
是否译文：否
发表时间：2025-02-25
收录刊物：SCI
发布期刊链接：https://www.sciencedirect.com/science/article/pii/S2352152X25006322