储能科学与技术 ›› 2022, Vol. 11 ›› Issue (2): 697-703.doi: 10.19799/j.cnki.2095-4239.2021.0450

• 储能测试与评价 • 上一篇    下一篇

锂离子电池建模现状综述

李建林1(), 肖珩2   

  1. 1.北方工业大学,北京 100192
    2.上海电力大学,上海 200090
  • 收稿日期:2021-08-30 修回日期:2021-10-13 出版日期:2022-02-05 发布日期:2022-02-08
  • 通讯作者: 李建林 E-mail:dkyljl@163.com

Review on modeling of lithium-ion battery

Jianlin LI1(), Heng XIAO2   

  1. 1.North China University of Technology, Beijing 100192, China
    2.Shanghai University of Electric Power, Shanghai 200090, China
  • Received:2021-08-30 Revised:2021-10-13 Online:2022-02-05 Published:2022-02-08
  • Contact: Jianlin LI E-mail:dkyljl@163.com

摘要:

简述了我国用于大规模储能的锂离子电池建模技术的最新研究进展。由于储能技术可以起到平抑波动、提高电能质量的作用,所以近年来电网对于储能的需求也逐年增大。大规模储能系统由锂电池组、双向逆变器和电池能量管理系统组成,在双向逆变器和电池能量管理系统有现成可用模型的前提下,建立精确、可靠的锂离子电池模型便成了实现大规模储能工程应用的重点。本文阐述了目前流行的电池建模方法:通过对电池电化学反应过程的模拟形成了电化学模型,虽然精度较高,但是模型复杂,使用时应当对其做适当简化,一般用于电池原理分析;通过对电池外特性不同程度的模拟形成了不同的等效电路模型,虽然不注重对原理的仿真,但是比较适合在工程实践中应用;通过对电池输入输出关系的研究形成了神经网络模型,但是其精度对于数据的数量和质量要求较高;最后总结指出为了更好地实现在电力系统中的应用,应当更加深入地研究锂离子电池反应原理并对其进行方程量化描述,提升模型在不同场景下的应用能力。

关键词: 储能, 锂离子电池, 建模

Abstract:

The latest research on lithium-ion battery modeling technology for large-scale energy storage in China is described briefly. Because energy storage technology can stabilize fluctuations and improve power quality, the energy storage demand in power grids has increased yearly. The large-scale energy storage system comprises a lithium battery pack, bidirectional inverter, and battery energy management system. Assuming the bidirectional inverter and battery energy management system have ready-made models, developing an accurate and reliable lithium-ion battery model has become the focus of applying large-scale energy storage engineering. This study describes current popular battery modeling methods. The electrochemical model is constructed by simulating the battery electrochemical reaction process. Although the accuracy is high, the model is complex; therefore, it should be properly simplified for use. It is typically used for battery principle analysis. Different equivalent circuit models are designed using different simulation degrees of the battery's external characteristics. Although we do not pay attention to simulating the principle, it is more suitable for application in engineering practice. The neural network model is constructed by studying the relationship between battery input and output, but its accuracy requires high quantity and quality data. Finally, to better realize the application in power systems, the reaction principle of lithium-ion batteries should be studied more deeply and described quantitatively to improve the application ability of the model in various scenarios.

Key words: energy storage, lithium-ion battery, modeling

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