Energy Storage Science and Technology ›› 2024, Vol. 13 ›› Issue (4): 1188-1196.doi: 10.19799/j.cnki.2095-4239.2023.0819

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Method for sorting the dynamic characteristics of lithium-ion battery consistency based on production line big data

Ge LI1(), Xiangdong KONG2, Yuedong SUN1, Fei CHEN1, Yuebo YUAN3, Xuebing HAN3(), Yuejiu ZHENG1   

  1. 1.College of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
    2.Sichuan Cell Technology Co. Ltd. , Yibin 644000, Sichuan, China
    3.School of Vehicle and Mobility, State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing 100084, China
  • Received:2023-11-15 Revised:2023-12-13 Online:2024-04-26 Published:2024-04-22
  • Contact: Xuebing HAN E-mail:leeger202@163.com;hanxuebing@mail.tsinghua.edu.cn

Abstract:

As lithium-ion battery production rapidly expands, manufacturers urgently require high-precision and high-efficiency sorting methods to improve the consistency, lifespan, safety, and energy density of battery packs. Traditional techniques that rely on capacity and internal resistance address static consistency postgrouping but fail to ensure dynamic consistency within the same group. Addressing this, our study focuses on the dynamic characteristics of the charge-discharge voltage curve to propose a next-generation sorting approach. We extract key dynamic features from the voltage curve during the battery capacity grading process, utilizing big data from the production line, and employ K-means clustering for battery sorting. Furthermore, we assess battery performance consistency by analyzing metrics from the recharging stage postcapacity grading, devising an evaluation method based on the standard deviation of these metrics. Our proposed sorting method demonstrates a 15.65% improvement in the overall performance consistency of batteries compared to conventional approaches.

Key words: lithium-ion battery, battery consistency, battery sorting, clustering algorithm

CLC Number: