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汽车安全与节能学报

JASE ›› 2019, Vol. 10 ›› Issue (1): 101-105.DOI: 10.3969/j.issn.1674-8484.2019.01.013

• 汽车节能与环保 • 上一篇    下一篇

基于大数据的电池健康状态（SoH）的估算及应用

肖 伟，钟卫东，舒小农，晏玖江，袁小溪   

  1. （清华四川能源互联网研究院，成都610213，中国）
  • 收稿日期:2018-10-12 出版日期:2019-03-31 发布日期:2019-04-01
  • 作者简介:第一作者 / First author ： 肖伟 (1981—)，男 ( 汉)，四川，高级工程师。E-mail: xiaowei@tsinghuasc.org。
  • 基金资助:

    国家电网公司科技项目资助（52020118000G）。

Battery state of health (SoH) estimation method and application based on big data

XIAO Wei, ZHONG Weidong, SHU Xiaonong, YAN Jiujiang, YUAN Xiaoxi   

  1. （Tsinghua Sichuan Energy Internet Research Institute, Chengdu 610213, China）
  • Received:2018-10-12 Online:2019-03-31 Published:2019-04-01

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摘要：

       为了突破因电池管理系统(BMS) 存储和计算能力不足导致的传统电池健康状态(SoH) 估算方法的局限，提出了基于互联网平台在线大数据的SoH 估算方法。研究了数据离散特性、电池单体一致性等因素对此估算方法的影响；结合某平台的在线大数据进行了此方法的集成应用，对单车以及分车辆品牌、分地域、分时域进行了多维度的电池SoH 衰减比较验证。结果表明：此方法能够有效估算单个动力电池系统的SoH 及其变化，并且能够与其他数据类型进行多维度整合，对动力电池进行大数据画像分析。

关键词: 动力电池 , 容量衰减 , 健康状态(SoH)  , 大数据

Abstract:

A traditional state of health (SoH) estimation method was proposed based on online big data online platform to break through the limitations of SoH estimation methods caused by insufficient battery management system (BMS) storage and computing power. The effects of data discrete characteristics and battery cell
consistency on the method were studied. The method was integrated into specific big-data platform, and the batteries’ SoH decrease were verified in muti-dimensions such as different vehicle brands, different locations and different time. The results show that this method can effectively estimate SoH of single power battery system and its variation, as well as integrate within other data in muti-dimensions and analyze the big data image of power batteries.

Key words: power battery , capacity decrease , state of health (SoH) ,  big data