基于扩展卡尔曼滤波的储能电池能量和功率状态联合估计方法

doi:10.19799/j.cnki.2095-4239.2022.0637

储能科学与技术 ›› 2023, Vol. 12 ›› Issue (3): 913-922.doi: 10.19799/j.cnki.2095-4239.2022.0637

基于扩展卡尔曼滤波的储能电池能量和功率状态联合估计方法

刘子豪¹(), 张雪松², 林达², 孙立清¹, 李正阳¹, 熊瑞¹()

^1.北京理工大学机械与车辆学院，北京 100081
^2.国网浙江省电力有限公司电力科学研究院，浙江杭州 310014

收稿日期:2022-10-31 修回日期:2022-11-17 出版日期:2023-03-05 发布日期:2023-04-14
通讯作者: 熊瑞 E-mail:lzh1921039462@163.com;rxiong@bit.edu.cn
作者简介:刘子豪（2000—），男，硕士研究生，研究方向为动力电池系统状态估计，E-mail： lzh1921039462@163.com；
基金资助:
国网浙江省电力有限公司科技项目 “基于数字孪生的储能电站数字化状态评估与决策支持技术研究”(5211DS21N006)

Joint energy and power state estimation method for energy storage battery based on extended Kalman filter

Zihao LIU¹(), Xuesong ZHANG², Da LIN², Liqing SUN¹, Zhengyang LI¹, Rui XIONG¹()

^1.School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
^2.Stae Grid Zhejiang Electric Power Co. , Ltd. Research Institute, Hangzhou 310014, Zhejiang, China

Received:2022-10-31 Revised:2022-11-17 Online:2023-03-05 Published:2023-04-14
Contact: Rui XIONG E-mail:lzh1921039462@163.com;rxiong@bit.edu.cn

摘要/Abstract

摘要：

电池储能是碳中和目标的有力抓手，准确估计其能量状态(state of energy，SOE)和峰值功率状态(state of power，SOP)是电池储能高效可靠运行的关键和基础。由于电池的电化学反应过程十分复杂，作为隐性状态量的SOE和SOP精确值难以获得。为此，本工作提出了一种基于模型SOE和SOP联合估计方法。应用Thevenin等效电路模型，采用递归最小二乘法建立了在线参数辨识算法，获得准确的模型参数。为解决恒定功率需求下的功率预测难题，提出了多步功率预测法，提高了SOP的预测精度，并结合扩展卡尔曼滤波算法，进一步提出了多状态联合估计方法。实验验证了算法的可行性，结果表明，在存在较大初始误差的情况下，所提出的方法电压、SOE最大预测误差均<2%，实现了准确的SOP预测。

关键词: 电池储能, Thevenin模型, 能量状态, 功率状态, 多步功率预测法

Abstract:

Battery energy storage is a powerful target for carbon neutrality. Accurate estimation of its state of energy (SOE) and state of power (SOP) is the key and foundation for the effective and reliable operation of battery energy storage. It is challenging to determine the precise values of SOE and SOP as recessive state quantities due to the intricacy of the electrochemical reaction process in batteries. Therefore, a model-based joint estimation method of SOE and SOP is suggested in this paper. Recursive least squares are utilized to create an online parameter identification technique using the Thevenin equivalent circuit model, and accurate model parameters are achieved. To address the prediction problem under constant power demand, a multi-step power prediction method is proposed to enhance the prediction accuracy of SOP. An additional joint estimation approach of SOE and SOP is suggested in conjunction with the expanded Kalman filter algorithm. The feasibility of the algorithm is verified by experiments. The findings demonstrate that, even in the presence of significant starting errors, the suggested method's maximum voltage and SOE prediction errors are both less than 2%, resulting in precise SOP prediction.

Key words: battery energy storage, Thevenin model, energy state, power state, multistep power prediction method

中图分类号:

TM 912

刘子豪, 张雪松, 林达, 孙立清, 李正阳, 熊瑞. 基于扩展卡尔曼滤波的储能电池能量和功率状态联合估计方法[J]. 储能科学与技术, 2023, 12(3): 913-922.

Zihao LIU, Xuesong ZHANG, Da LIN, Liqing SUN, Zhengyang LI, Rui XIONG. Joint energy and power state estimation method for energy storage battery based on extended Kalman filter[J]. Energy Storage Science and Technology, 2023, 12(3): 913-922.

图/表 13

图1

图2

表1

图3

图4

表2

动力电池单体约束条件(60 s)"

参数	最大值	最小值
$S O E (z m a x, z m i n)$	0.7	0.1
$U t (U t, m a x, U t, m i n) / V$	3.65	2.5
$I (I m a x, I m i n) / A$	600	-250
$P (P m a x, P m i n) / W$	2500	-1500

表2

图5

图6

表3

表4

图7

图8

图9

参考文献 23

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项目	储能电池参数
材料体系	磷酸铁锂
标称容量/Ah	50
标称电压/V	3.2
截止电压/V	2.5/3.65
工作温度/℃	-20/55

误差/s	最大值/W	绝对均值/W	均方根值/W
40	1.93	1.49	1.54
50	2.42	1.55	1.67
60	3.77	1.62	1.99

项目		误差
项目		绝对均值	最大值	均方根
25 ℃	端电压/mV	0.65	36.08	1.31
25 ℃	SOE/%	0.43	1.38	0.52
45 ℃	端电压/mV	0.37	17.67	0.70
45 ℃	SOE/%	0.68	1.63	0.92

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基于扩展卡尔曼滤波的储能电池能量和功率状态联合估计方法

Joint energy and power state estimation method for energy storage battery based on extended Kalman filter

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