Energy Storage Science and Technology ›› 2023, Vol. 12 ›› Issue (9): 2937-2945.doi: 10.19799/j.cnki.2095-4239.2023.0332

• Energy Storage Test: Methods and Evaluation • Previous Articles     Next Articles

Consistency evaluation method of battery pack in energy storage power station based on running data

Xin GAO1(), Ruogu WANG1, Wenjing GAO2, Zejun DENG1, Ruiqi LIANG3, Kun YANG3   

  1. 1.Shanxi Electric Power Research Institute of State Electricity Network, Xi'an 710054, Shaanxi, China
    2.China University of Mining and Technology, Xuzhou 221116, Jiangsu, China
    3.Department of Electrical Engineering, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
  • Received:2023-05-09 Revised:2023-06-20 Online:2023-09-05 Published:2023-09-16
  • Contact: Xin GAO E-mail:18209183315@163.com

Abstract:

This study takes a large-capacity power station of lithium iron phosphate battery energy storage as the research object, based on the daily operation data of battery packs in the engineering scene of energy storage systems. First, the key parameters characterizing the voltage and temperature consistency of Li-ion batteries were analyzed according to the operating data of the battery. Second, the evaluation features that can effectively reflect the battery pack consistency were extracted. Finally, based on such characteristics, the consistency analysis of the energy storage power station was divided into two levels, and the consistency analysis algorithm was proposed for large-scale battery packs in the station. Furthermore, a screening algorithm was proposed for abnormal cells in battery packs based on density-based spatial clustering of applications with noise (DBSCAN) clustering. The results showed that the proposed algorithm could efficiently obtain the key electrical characteristics related to the battery pack consistency in the operation data of the energy storage power station. Moreover, it could accurately judge the battery pack consistency in the energy storage system and locate the single battery that may fail. This study is helpful in judging the consistent state of large-scale battery packs in engineering scenarios. It can also timely and accurately screen out abnormal single batteries to ensure the battery packs' safety in energy storage power stations.

Key words: energy storage power station, lithium-ion batteries, DBSCAN clustering algorithm, consistency evaluation

CLC Number: