化工学报 ›› 2018, Vol. 69 ›› Issue (1): 309-316.DOI: 10.11949/j.issn.0438-1157.20171097

• 综述与专论 • 上一篇    下一篇

基于模型的锂离子电池SOC及SOH估计方法研究进展

沈佳妮1,2, 贺益君1,2, 马紫峰1,2   

  1. 1 上海交通大学化工系, 上海 200240;
    2 上海电化学能源器件工程技术中心, 上海 200240
  • 收稿日期:2017-08-14 修回日期:2017-10-13 出版日期:2018-01-05 发布日期:2018-01-05
  • 通讯作者: 马紫峰
  • 基金资助:

    国家重点基础研究发展计划项目(2014CB239703);国家重点研发计划项目(2016YFB0901505);国家自然科学基金项目(21576163,21336003)。

Progress of model based SOC and SOH estimation methods for lithium-ion battery

SHEN Jiani1,2, HE Yijun1,2, MA Zifeng1,2   

  1. 1 Department of Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;
    2 Shanghai Electrochemical Energy Devices Research Center, Shanghai 200240, China
  • Received:2017-08-14 Revised:2017-10-13 Online:2018-01-05 Published:2018-01-05
  • Contact: 10.11949/j.issn.0438-1157.20171097
  • Supported by:

    supported by the National Basic Research Program of China (2014CB239703), the National Key Research and Development Program of China (2016YFB0901505) and the National Natural Science Foundation of China (21576163, 21336003).

摘要:

电池管理系统是保证锂离子电池高效、安全运行的重要手段。在电池管理系统功能中,电池状态估计,特别是荷电状态(state of charge,SOC)估计和健康状态(state of health,SOH)估计至关重要。SOC/SOH不仅与全生命周期内电池安全运行直接相关,也是其他功能有效实现的必要前提。本文围绕模型类电池状态估计方法,综述了国内外在锂离子电池模型构建、SOC及SOH估计方法方面的研究进展;指出了模型类状态估计方法存在的难点和局限,提出了今后研究重点。

关键词: 锂离子电池, 电池管理系统, 电池模型, 荷电状态估计, 健康状态估计

Abstract:

Battery management system (BMS) is an important way to ensure the efficient, reliable and safety operation of lithium-ion battery system. Among the BMS functions, estimation of battery states, especially the state of charge (SOC) and state of health (SOH), is critical to the overall performance. They are not only directly related to battery safety operation during the whole life, but also sever as the urgent prerequisite for implementing other facilities. We reviewed the domestic and foreign development of model-based battery state estimation methods, including battery model and estimation methods of SOC and SOH, in the hope of providing some inspirations to the research in this field.

Key words: lithium-ion battery, battery management system, battery model, state of charge estimation, state of health estimation

中图分类号: