梯次利用锂离子电池等效模型参数在线辨识方法

doi:10.19799/j.cnki.2095-4239.2020.0235

储能科学与技术 ›› 2021, Vol. 10 ›› Issue (1): 342-348.doi: 10.19799/j.cnki.2095-4239.2020.0235

梯次利用锂离子电池等效模型参数在线辨识方法

杜帮华¹(), 张宇¹(), 吴铁洲¹, 何衍林¹, 李子龙²

^1.湖北工业大学太阳能高效利用及储能运行控制湖北省重点实验室，湖北武汉 430068
^2.国网新源湖北白莲河抽水蓄能有限公司，湖北黄冈 438600

收稿日期:2020-07-02 修回日期:2020-07-21 出版日期:2021-01-05 发布日期:2021-01-08
通讯作者: 张宇 E-mail:Dubh324@163.com;18971401533@163.com
作者简介:杜帮华（1995—)，男，硕士研究生，主要研究方向为电池管理系统，E-mail:Dubh324@163.com;
基金资助:
湖北省科技厅重大专项项目(2018AAA056)

An online identification method for equivalent model parameters of aging lithium-ion batteries

Banghua DU¹(), Yu ZHANG¹(), Tiezhou WU¹, Yanlin HE¹, Zilong LI²

^1.Hubei University of Technology, Hubei Provincial Key Laboratory of Solar Energy Efficient Utilization and Energy Storage Operation Control, Wuhan 430068, Hubei, China
^2.State Grid New Source Hubei Bailianhe Pumped Storage Co. Ltd. , Huanggang 438600, Hubei, China

Received:2020-07-02 Revised:2020-07-21 Online:2021-01-05 Published:2021-01-08
Contact: Yu ZHANG E-mail:Dubh324@163.com;18971401533@163.com

摘要/Abstract

摘要：

针对目前梯次利用锂离子电池受老化的影响，导致在采用传统带遗忘因子的最小二乘法进行等效模型在线参数辨识后，会出现模型参数过饱和、无法跟踪时变参数的问题。本工作提出一种可变遗忘因子的最小二乘法，通过不断地对遗忘因子进行矫正更新，从而更好地跟踪梯次利用电池老化特性。以锂电池一阶RC等效电路模型为对象，搭建测试平台进行充放电试验，并与传统带遗忘因子的最小二乘法的参数辨识结果进行对比。实验结果表明，该方法能够快速收敛及动态跟踪，同时模型端电压参数平均误差减小到25 mV以内，所提出的方法在DST和储能系统典型工况下运行时，其对应的参数辨识精度提高了38.33%，证明该方法具有较高的准确性。

关键词: 梯次利用, 锂离子电池, 退役电池, 等效电路模型, 参数辨识, 遗忘因子, 最小二乘法, SOC估算

Abstract:

The current challenges of modeling aging lithium-ion batteries include oversaturated model parameters and time-varying parameters, which cannot be evaluated with an online parameter identification of the model using the traditional least squares method with a fixed forgetting factor. This paper proposes a least squares method with a variable forgetting factor, which continuously updates the forgetting factor to better track the run-time utilization of battery aging characteristics. Using the first-order RC equivalent circuit model of the lithium battery as a model, a test platform was established for charge and discharge experiments, and the results were compared with the traditional least squares method with a fixed forgetting factor. The experimental results indicated that the proposed method can quickly converge and dynamically track battery aging. The average absolute error of the voltage parameters at the model terminal was found to be less than 25 mV. When the proposed method was run under a dynamic stress test with the typical working conditions of an energy storage system, the corresponding parameter identification accuracy was improved by 38.33%, indicating that the proposed method is highly accurate.

Key words: echelon use, Li-ion battery, decommissioned battery, equivalent circuit model, parameter identification, forgetting factor, least square method, SOC estimation

中图分类号:

TM 911

杜帮华, 张宇, 吴铁洲, 何衍林, 李子龙. 梯次利用锂离子电池等效模型参数在线辨识方法[J]. 储能科学与技术, 2021, 10(1): 342-348.

Banghua DU, Yu ZHANG, Tiezhou WU, Yanlin HE, Zilong LI. An online identification method for equivalent model parameters of aging lithium-ion batteries[J]. Energy Storage Science and Technology, 2021, 10(1): 342-348.

图/表 9

图1

图2

图3

图4

表1

可变遗忘因子最小二乘法在线辨识过程"

(1)参数初始化
$θ 0 = α 1, α 2, α 3 T P 0 = δ I 0$
(2)采集输入电流和输出电压，更新信息矩阵
$Φ k = U k, I 1, k, I 1, k - 1 T$
(3)预测输出电压
$U ? k = Φ k T θ ? k - 1$
(4)估计误差计算
$e k = U k - U ? k$
(5)可变遗忘因子计算
$μ k = m i n σ v σ ? q k ξ + σ ? e k - σ v, μ m a x$
(6)增益矩阵计算
$K k = P k - 1 Φ k T Φ k P k - 1 Φ k T + μ - 1$
(7)参数估计
$θ ? k = θ ? k - 1 + K k e k$
(8)更新方差矩阵
$P k = 1 μ I - K k Φ k P k - 1$
重复步骤(2)至(8)直至参数辨识结束

表1

表2

图5

图6

图7

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梯次利用锂离子电池等效模型参数在线辨识方法

An online identification method for equivalent model parameters of aging lithium-ion batteries

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