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

doi:10.19799/j.cnki.2095-4239.2022.0637

Abstract

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

CLC Number:

TM 912

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.

Figures/Tables 13

Fig. 1

Fig. 2

Table 1

Fig. 3

Fig. 4

Table 2

Power battery constraints (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

Table 2

Fig. 5

Fig. 6

Table 3

Table 4

Fig. 7

Fig. 8

Fig. 9

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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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