锂离子电池建模现状综述

doi:10.19799/j.cnki.2095-4239.2021.0450

摘要/Abstract

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

中图分类号:

TK 02

李建林, 肖珩. 锂离子电池建模现状综述[J]. 储能科学与技术, 2022, 11(2): 697-703.

Jianlin LI, Heng XIAO. Review on modeling of lithium-ion battery[J]. Energy Storage Science and Technology, 2022, 11(2): 697-703.

图/表 5

图1

表1

常用锂电池模型比较"

基本电路模型	描述方程	参数	优点	缺点
Rint	$U = U O C - I R$	$U O C$ 为开路电压，R为欧姆内阻，U为端电压，I为负载电流	结构简单，参数容易计算	无法描述动态过程，电流过大时精度较差，对电池特性模拟情况差
Thevenin	$U = U O C - I R 0 - U C$ $I = U C R 1 + C d U C d t$	$R 1$ 和C是用来描述极化效应的电阻、电容	考虑了电池的极化效应，对电池特性有较好模拟，在实际工程应用中较多	电池老化、温度变化情况对模型精度有较大影响
PNGV	$U = U O C - U Q - U C - I R 0$ $U Q = I C Q$	$U Q$ 为等效电容	模型容易考虑温度影响，对电池各种工况适用性好，精确度较好	串联电容的累积误差会降低模型精确度，且仍不能较好反应极化现象
二阶RC	$U = U O C - I R 0 - U c 1 - U c 2$ $I = U c 1 R 1 + C 1 d U c 1 d t$	$R 2$ 和 $C 2$ 分别为浓差阻抗和浓差电容	计算量适中，模型精度高，更接近真实电池特性	结构、参数计算较为复杂
GNL	$U = U O C - U Q - U c 1 -$ $U c 2 - I L R 0$ $U = I S R S$	$R S$ 为自放电电阻	考虑自放电影响，仿真精度高	模型复杂，参数整定困难，计算复杂

表1

图2

表2

串并联方式比较"

成组方式

成组结构

描述方程

优点

缺点

先并后串

$I = I 11 + I 21 + … + I N 1$

$U 11 = U 21 = … = U N 1$

均衡电路和保护电路的成本大大降低

连接复杂，当某个电芯短路时将影响整个并联组

先串后并

$I = I 1 + I 2 + … + I N$

$U 11 + U 12 + … + U 1 M = U 21 + U 22 + … + U 2 M = … = U N 1 + U N 2 + … + U N M$

若某组串联出现问题，仅仅减小容量而不会使整个系统停止运行

容易出现电压过高^[32]

表2

图3

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