储能科学与技术 ›› 2022, Vol. 11 ›› Issue (2): 643-651.doi: 10.19799/j.cnki.2095-4239.2021.0437

• 储能测试与评价 • 上一篇    下一篇

一种可工程化检测磷酸铁锂电池问题的方法

赵尚玉(), 张震, 王宝源, 曾驱虎   

  1. 深圳市科陆电子科技股份有限公司,广东 深圳 518000
  • 收稿日期:2021-08-23 修回日期:2021-09-08 出版日期:2022-02-05 发布日期:2022-02-08
  • 通讯作者: 赵尚玉 E-mail:280747844@qq.com

An engineering method for detection of problems of lithium iron phosphate batteries

Shangyu ZHAO(), Zhen ZHANG, Baoyuan WANG, Quhu ZENG   

  1. Shenzhen Clou Co. Ltd. , Shenzhen 518000, Guangdong, China
  • Received:2021-08-23 Revised:2021-09-08 Online:2022-02-05 Published:2022-02-08
  • Contact: Shangyu ZHAO E-mail:280747844@qq.com

摘要:

磷酸铁锂电池广泛应用在大规模储能电站系统,为提高电池系统运行性能和保证电池系统安全性,迫切需要一套可工程化、在线、快速、精准定位不一致电芯的方法,便于系统快速诊断和维护。本工作提出了一种基于电池充放电特征,利用多种离群值检测的算法,实现问题分类。在此基础上计算电池偏差容量,以之作为优化系数,对上述各类问题进行优先处理级别排序,给工程运维带来了极大的便利。实践结果表明,本工作提出的方法具有辨识准确度高、调参简便、计算复杂度低的特性,十分适合在线工程,对储能电站的维护具有重要指导意义。

关键词: 储能电站在线监测, 磷酸铁锂电池, 高斯分布模型, Grubbs检验, 优化系数

Abstract:

Lithium iron phosphate batteries (LFP) are widely used in large-scale battery energy storage systems. To improve the operating performance and ensure the safety of the battery system, people urgently need an on-site fault diagnosis for inconsistent cells that is engineered to be fast and accurate to support the rapid diagnosis and maintenance of the system. In this study, an algorithm based on battery charge and discharge characteristics is proposed to realize the classification of inconsistent cells by multiple-outlier detection. On this basis, the battery deviation capacity is computed and used as the optimization coefficient to prioritize the difficulties, which brings great ease to engineering operation and maintenance. Practical results show that the proposed method has the characteristics of high identification accuracy, simple parameter adjustment, and low computational complexity, which makes it suitable for on-site engineering and has important guiding significance for the maintenance of energy storage power stations.

Key words: on-site monitoring of energy storage power stations, lithium iron phosphate battery, Gaussian distribution model, Grubbs test, optimization coefficient

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