State of Charge Estimation of Energy Storage Battery Pack under Typical Peak/Frequency Modulation Conditions

doi:10.11930/j.issn.1004-9649.202401003

Abstract

Abstract:

To address the issue of low estimation accuracy of the state of charge (SOC) for an energy storage battery pack under typical energy storage conditions of a power grid, this paper proposes a new SOC estimation model based on kernel principal component analysis (KPCA), pelican optimization algorithm (POA), and bidirectional gated recurrent unit (BiGRU). By designing the charge and discharge experiment of a battery pack under the condition of peak/frequency modulation, the paper extracts the fusion features of SOC change from the data as the model input. BiGRU networks are constructed under different working conditions, and POA is utilized to optimize its hyperparameters to improve the model's performance. The effectiveness of the model is further verified under mixed conditions. The results show that the proposed model has better SOC estimation performance and stronger robustness, which can improve the SOC estimation accuracy of energy storage battery packs under complex energy storage conditions.

Key words: energy storage battery pack, state of charge estimation, peak and frequency modulation, pelican optimization, bidirectional gated recurrent unit

Muyu ZHU, Hongzhong MA, Pengyu GUO, Wenjing XUAN. State of Charge Estimation of Energy Storage Battery Pack under Typical Peak/Frequency Modulation Conditions[J]. Electric Power, 2024, 57(6): 18-26.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202401003

https://www.electricpower.com.cn/EN/Y2024/V57/I6/18

Figures/Tables 14

Fig.1 BiGRU structure

Fig.2 Flow of SOC estimation

Fig.3 Lithium iron phosphate battery pack

Table 1 Battery pack parameters

型号	标称电压/V	标称容量/(A·h)
MCRSA08-LC	25.6	220

电池组尺寸/mm	工作温度/℃	质量/kg
555×430×154	–20~55	60

Fig.4 Experimental platform

Fig.5 Current instructions under different working conditions

Fig.6 Battery pack data monitoring

Fig.7 Contribution rates and eigenvalue distribution under different working conditions

Fig.8 Comparison of SOC estimation with different input features

Table 2 Error evaluation indexes of different input characteristics

工况	是否降维	E_RMS	E_MA	R²
调峰	是	0.008645	0.008527	0.9987
调峰	否	0.033260	0.022370	0.9406
调频	是	0.009983	0.010410	0.9930
调频	否	0.061020	0.047480	0.7753

Fig.9 Comparison of SOC estimation with different learning models

Table 3 Error evaluation indexes with different learning models

工况	模型	E_RMS	E_MA	R²
调峰	POA-BiGRU	0.008645	0.008527	0.9987
	KELM	0.024090	0.019680	0.9654
	BiLSTM	0.011050	0.012310	0.9892
	BiGRU	0.011360	0.012440	0.9875
调频	POA-BiGRU	0.009983	0.010410	0.9930
	KELM	0.037650	0.022560	0.9532
	BiLSTM	0.015780	0.011020	0.9785
	BiGRU	0.016620	0.012330	0.9771

Fig.10 SOC estimation effect under mixed conditions

Table 4 Error evaluation indexes under mixed conditions

模型	E_RMS	E_MA	R²
单模型	0.02872	0.025460	0.9755
双模型	0.01097	0.009862	0.9936

References 23

1	HIRSCH A, PARAG Y, GUERRERO J. Microgrids: a review of technologies, key drivers, and outstanding issues[J]. Renewable and Sustainable Energy Reviews, 2018, 90, 402- 411. DOI
2	岳家辉, 夏向阳, 蒋戴宇, 等. 基于电压数据片段混合模型的锂离子电池剩余寿命预测与健康状态估计[J]. 中国电力, 2023, 56 (7): 163- 174.
	YUE Jiahui, XIA Xiangyang, JIANG Daiyu, et al. Remaining useful life prediction and state of health estimation of lithium-ion batteries based on voltage data segment hybrid model[J]. Electric Power, 2023, 56 (7): 163- 174.
3	杨启帆, 马宏忠, 刘宝稳, 等. 锂离子电池气体故障特性分析及诊断方法[J]. 高电压技术, 2021, 47 (9): 3315- 3330.
	YANG Qifan, MA Hongzhong, LIU Baowen, et al. Gas fault characteristics analysis and diagnosis method of lithium-ion battery[J]. High Voltage Engineering, 2021, 47 (9): 3315- 3330.
4	XI Z M, WANG R, FU Y H, et al. Accurate and reliable state of charge estimation of lithium ion batteries using time-delayed recurrent neural networks through the identification of overexcited neurons[J]. Applied Energy, 2022, 305, 117962. DOI
5	曹钰, 姜彤, 刘炽, 等. 考虑频率特性及储能电池状态的电化学储能参与一次调频控制策略[J]. 储能科学与技术, 2023, 12 (10): 3120- 3130.
	CAO Yu, JIANG Tong, LIU Chi, et al. Electrochemical energy storage participation in primary frequency regulation control strategy considering frequency characteristics and energy storage battery state[J]. Energy Storage Science and Technology, 2023, 12 (10): 3120- 3130.
6	张力菠, 王格格. 电化学储能电池技术主题识别、演化及风险分析[J]. 储能科学与技术, 2023, 12 (8): 2680- 2692.
	ZHANG Libo, WANG Gege. Topic identification, evolution, and risk analysis of electrochemical energy storage battery technology[J]. Energy Storage Science and Technology, 2023, 12 (8): 2680- 2692.
7	黄晓凡, 李佳瑞, 秦玉文, 等. 基于多尺度CNN和残差LSTM网络的储能电站SOC估计[J/OL]. 电源学报: 1–12[2024-04-08]. http://kns.cnki.net/kcms/detail/12.1420.TM.20231023.1653.014.html.
	HUANG Xiaofan, LI Jiarui, QIN Yuwen, et al. A Multi-scale CNN and residual LSTM network method for estimating the state of charge in energy storage system[J/OL]. Journal of Power Supply: 1–12[2024-04-08]. http://kns.cnki.net/kcms/detail/12.1420.TM.20231023.1653.014.html.
8	CHEMALI E, KOLLMEYER P J, PREINDL M, et al. State-of-charge estimation of Li-ion batteries using deep neural networks: a machine learning approach[J]. Journal of Power Sources, 2018, 400, 242- 255. DOI
9	李晟延, 马鸿雁, 窦嘉铭, 等. 基于PBES-LS-SVM的锂离子电池组SOC预测[J]. 电源技术, 2022, 46 (11): 1279- 1283.
	LI Shenyan , MA Hongyan, DOU Jiaming, et al. SOC prediction of lithium-ion battery pack based on PBES-LS-SVM[J]. Chinese Journal of Power Sources, 2022, 46 (11): 1279- 1283.
10	于全庆, 王灿, 李建明, 等. 多拓扑结构锂电池组外短路特性分析及模型评价[J]. 机械工程学报, 2023, 59 (6): 159- 172.
	YU Quanqing, WANG Can, LI Jianming, et al. Model evaluation and analysis for multi-topology lithium battery pack external short circuit characteristics[J]. Journal of Mechanical Engineering, 2023, 59 (6): 159- 172.
11	SHEN J N, HE Y J, MA Z F, et al. Online state of charge estimation of lithium-ion batteries: a moving horizon estimation approach[J]. Chemical Engineering Science, 2016, 154, 42- 53. DOI
12	杨涛, 文贤馗, 谈竹奎, 等. 面向新型电力系统储能电站的锂电池荷电状态评估方法研究[J/OL]. 电测与仪表: 1–9[2024-04-08]. http://kns.cnki.net/kcms/detail/23.1202.th.20220714.0959.004.html.
	YANG Tao, WEN Xiankui, TAN Zhukui, et al. Research on SOC evaluation method of lithium battery for energy storage station in novel power system[J/OL]. Electrical Measurement & Instrumentation: 1–9[2024-04-08]. http://kns.cnki.net/kcms/detail/23.1202.th.20220714.0959.004.html.
13	张媛, 夏向阳, 岳家辉, 等. 基于电池簇放电电量的电池堆不一致性在线监测方法[J]. 中国电力, 2023, 56 (7): 207- 215, 227.
	ZHANG Yuan, XIA Xiangyang, YUE Jiahui, et al. Online monitoring method of battery stack inconsistency based on discharge quantity of battery clusters[J]. Electric Power, 2023, 56 (7): 207- 215, 227.
14	CHEN J X, ZHANG Y, WU J, et al. SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output[J]. Energy, 2023, 262, 125375. DOI
15	孙玉树, 李宏川, 王波, 等. 电池储能系统SOC神经网络融合估计方法[J]. 湖南大学学报(自然科学版), 2023, 50 (10): 31- 40.
	SUN Yushu, LI Hongchuan, WANG Bo, et al. SOC neural network fusion estimation method for battery energy storage system[J]. Journal of Hunan University (Natural Sciences), 2023, 50 (10): 31- 40.
16	张英达, 马鸿雁, 窦嘉铭, 等. 基于ISSA-KELM的锂离子电池组SOC预测[J/OL]. 电源学报: 1–13[2024-04-08]. http://kns.cnki.net/kcms/detail/12.1420.TM.20220223.1628.007.html.
	ZHANG Yingda, MA Hongyan, DOU Jiaming et al. SOC prediction of li-ion battery pack based on ISSA-KELM [J/OL]. Journal of Power Supply: 1–13[2024-04-08]. http://kns.cnki.net/kcms/detail/12.1420.TM.20220223.1628.007.html.
17	赵航, 张旭, 刘欢, 等. 考虑温度影响的可重构式储能系统电池荷电状态的估计[J/OL]. 现代电力: 1–8[2024-04-08].https://doi.org/10.19725/j.cnki.1007-2322.2022.0431.
	ZHAO Hang, ZHANG Xu, LIU Huan, et al. State estimation of the battery state of charge in reconfigurable energy storage systems considering temperature effects[J/OL]. Modern Electric Power: 1–8[2024-04-08].https://doi.org/10.19725/j.cnki.1007-2322.2022.0431.
18	张瑛, 康永哲, 段彬, 等. 基于数据-模型双驱动的电池组容量在线估计[J/OL]. 机械工程学报: 1–12[2024-04-08]. http://kns.cnki.net/kcms/detail/11.2187.TH.20230428.1431.020.html.
	ZHANG Ying, KANG Yongzhe, DUAN Bin, et al. Online estimation of battery pack capacity based on dual driven method of data and model [J/OL]. Journal of Mechanical Engineering, 1–12[2024-04-08]. http://kns.cnki.net/kcms/detail/11.2187.TH.20230428.1431.020.html.
19	曹现刚, 叶煜, 赵友军, 等. 基于KPCA-LSTM的旋转机械剩余使用寿命预测[J]. 振动与冲击, 2023, 42 (24): 81- 91.
	CAO Xiangang, YE Yu, ZHAO Youjun, et al. Remaining useful life prediction of rotating machinery based on KPCA-LSTM[J]. Journal of Vibration and Shock, 2023, 42 (24): 81- 91.
20	杨胡萍, 余阳, 汪超, 等. 基于VMD-CNN-BIGRU的电力系统短期负荷预测[J]. 中国电力, 2022, 55 (10): 71- 76.
	YANG Huping, YU Yang, WANG Chao, et al. Short-term load forecasting of power system based on VMD-CNN-BIGRU[J]. Electric Power, 2022, 55 (10): 71- 76.
21	TROJOVSKÝ P, DEHGHANI M. Pelican optimization algorithm: a novel nature-inspired algorithm for engineering applications[J]. Sensors, 2022, 22 (3): 855. DOI
22	倪晋兵, 肖仁军, 孙慧芳, 等. 基于POA-RVM模型的抽蓄机组故障诊断[J/OL]. 人民长江: 1–10[2024-04-08]. http://kns.cnki.net/kcms/detail/42.1202.TV.20231211.1401.004.html.
	NI Jinbing, XIAO Renjun, SUN Huifang, et al. Fault diagnosis of pumped storage unit based on POA-RVM model [J/OL]. Yangtze River, 1–10[2024-04-08]. http://kns.cnki.net/kcms/detail/42.1202.TV.20231211.1401.004.html.
23	谭必蓉, 杜建华, 叶祥虎, 等. 基于模型的锂离子电池SOC估计方法综述[J]. 储能科学与技术, 2023, 12 (6): 1995- 2010.
	TAN Birong, DU Jianhua, YE Xianghu, et al. Overview of SOC estimation methods for lithium-ion batteries based on model[J]. Energy Storage Science and Technology, 2023, 12 (6): 1995- 2010.