State-of-charge estimation of energy storage batteries based on adaptive capacity considering discharge rate

doi:10.19799/j.cnki.2095-4239.2025.0080

Abstract

Abstract:

With the rapid development of renewable energy technologies, the application of energy storage batteries in power systems has become increasingly widespread. Accurately estimating the State of Charge (SOC) of batteries is critical to ensuring battery performance, extending lifespan, and ensuring safe operation. To improve the accuracy of SOC estimation for grid energy storage batteries under varying power demands, this paper proposes an SOC estimation method based on dynamic capacity correction. First, the error generation mechanism of traditional SOC estimation methods under complex operating conditions is analyzed, and a general improvement strategy is proposed. Secondly, the capacity variation characteristics of energy storage batteries at different discharge rates are analyzed, and a quantitative model to characterize the relationship between discharge rate and capacity is established, providing a theoretical foundation for precise SOC estimation. Next, a hybrid estimation algorithm, CLA-EKF, is proposed by integrating deep neural networks with the Extended Kalman Filter (EKF). This approach leverages the advantages of both methods in handling complex nonlinear relationships and resisting disturbances, and further develops an SOC estimation method with adaptive capacity correction based on discharge rate. The experimental results demonstrate that the proposed capacity-correction-based CLA-EKF method significantly improves the accuracy of SOC estimation under various variable power conditions. This method proves superior to conventional methods, and provides an effective solution for SOC estimation in grid energy storage systems with high practical application value.

Key words: Energy storage battery, SOC estimation, Discharge rate, Kalman filter method, Neural network

CLC Number:

TM 912

Li HE, Zhaoxing LENG, Zhuangxi TAN, Xueyuan LI, Xiaowen WU, Chaoyang CHEN. State-of-charge estimation of energy storage batteries based on adaptive capacity considering discharge rate[J]. Energy Storage Science and Technology, doi: 10.19799/j.cnki.2095-4239.2025.0080.

Figures/Tables 15

Fig. 1

Table 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Table 2

Fig. 6

Fig. 7

Figure. 8

Table 3

Fig. 9

Table 4

Fig. 10

Table 5

References 33

1	汤匀,岳芳,郭楷模,等.下一代电化学储能技术国际发展态势分析[J].储能科学与技术,2022,11(01):89-97.
	TANG Y, YUE F, GUO K M, et al. Analysis of international development trends of next-generation electrochemical energy storage technologies[J]. Energy Storage Science and Technology, 2022, 11(01): 89-97.
2	朱文韬,周杨,徐艺敏,等.电池储能技术在新能源发电系统中的应用与优化[J].储能科学与技术,2024,13(08):2737-2739.
	ZHU W T, ZHOU Y, XU Y M, et al. Application and optimization of battery energy storage technology in new energy power generation systems[J]. Energy Storage Science and Technology, 2024, 13(08): 2737-2739.
3	尹喜阳,王忠钰,刘乙召,等.面向配电网削峰填谷的5G基站储能调控方法[J].电网与清洁能源,2024,40(08):97-102.
	YIN X Y, WANG Z Y, LIU Y Z, et al. Peak-shaving and valley-filling energy storage control methods for 5G base stations oriented toward distribution networks[J]. Power System and Clean Energy, 2024, 40(08): 97-102.
4	廖世强,张新燕,刘莎莎,等. 储能电池一次调频无模型自适应控制策略 [J]. 储能科学与技术, 2022, 11 (10): 3221-3230.
	LIAO S Q, ZHANG X Y, LIU S S, et al. Model-free adaptive control strategy for primary frequency regulation of energy storage batteries[J]. Energy Storage Science and Technology, 2022, 11(10): 3221-3230.
5	梁继业,袁至,王维庆,等.基于电池储能系统的综合自适应一次调频策略[J/OL].电工技术学报,1-14[2024-10-29].
	LIANG J Y, YUAN Z, WANG W Q, et al. Integrated adaptive primary frequency regulation strategy based on battery energy storage systems[J/OL]. Transactions of China Electrotechnical Society, 1-14[2024-10-29].
61	李建林,郭兆东,曾伟,等.面向调频的锂电池储能建模及仿真分析[J].电力系统保护与控制,2022,50(13):33-42.
	LI J L, GUO Z D, ZENG W, et al. Modeling and simulation analysis of lithium battery energy storage for frequency regulation[J]. Power System Protection and Control, 2022, 50(13): 33-42.
7	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(06):1995-2010.
	TAN B R, DU J H, YE X H, et al. A review of model-based SOC estimation methods for lithium-ion batteries[J]. Energy Storage Science and Technology, 2023, 12(06): 1995-2010.
8	寇发荣,罗希,门浩,等.基于特征优选与改进极限学习机的锂电池SOC估计[J].储能科学与技术,2023,12(04):1234-1243.
	KOU F R, LUO X, MEN H, et al. SOC estimation of lithium-ion batteries based on feature selection and improved extreme learning machine[J]. Energy Storage Science and Technology, 2023, 12(04): 1234-1243.
9	黎冲,王成辉,王高,等.锂电池SOC估计的实现方法分析与性能对比[J].储能科学与技术,2022,11(10):3328-3344.
	LI C, WANG C H, WANG G, et al. Analysis and performance comparison of SOC estimation methods for lithium-ion batteries[J]. Energy Storage Science and Technology, 2022, 11(10): 3328-3344.
10	陈峥,杨博,赵志刚,等.考虑锂电池温度和老化的荷电状态估算[J].储能科学与技术,2024,13(08):2813-2822.
	CHEN Z, YANG B, ZHAO Z G, et al. SOC estimation considering lithium-ion battery temperature and aging[J]. Energy Storage Science and Technology, 2024, 13(08): 2813-2822.
11	付诗意,吕桃林,闵凡奇,等.电动汽车用锂离子电池SOC估算方法综述[J].储能科学与技术,2021,10(03):1127-1136.
	FU S Y, LÜ T L, MIN F Q, et al. Review of SOC estimation methods for lithium-ion batteries used in electric vehicles[J]. Energy Storage Science and Technology, 2021, 10(03): 1127-1136.
12	SUN F, HU X, ZOU Y,, et al. Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles[J]. Energy, 2011, 36(5): 3531-3540.
13	石怡康.锂电池电荷状态预测的关键技术研究[D].电子科技大学,2024.
	SHI Y K. Key technology research on state of charge prediction for lithium batteries[D]. University of Electronic Science and Technology of China, 2024.
14	陈雨墨.基于自适应扩展卡尔曼滤波的锂电池SOC估计[D].东北电力大学,2024.
	CHEN Y M. SOC estimation of lithium batteries based on adaptive extended Kalman filtering[D]. Northeast Electric Power University, 2024.
15	孙金磊,邹鑫,顾浩天,等.基于FFRLS-EKF联合算法的锂离子电池荷电状态估计方法[J].汽车工程,2022,44(04):505-513.
	SUN J L, ZOU X, GU H T, et al. SOC estimation method for lithium-ion batteries based on FFRLS-EKF hybrid algorithm[J]. Automotive Engineering, 2022, 44(04): 505-513.
16	BHANGU B S, BENTLEY P, STONE D A, et al. Nonlinear observers for predicting state-of-charge and state-of-health of lead-acid batteries for hybrid-electric vehicles[J]. IEEE transactions on vehicular technology, 2005, 54(3): 783-794.
17	XU K, HE T, YANG P, et al. A new online SOC estimation method using broad learning system and adaptive unscented Kalman filter algorithm[J]. Energy, 2024, 309: 132920.
18	CHEN J, 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.
19	TIAN Y, LAI R, LI X, et al. A combined method for state-of-charge estimation for lithium-ion batteries using a long short-term memory network and an adaptive cubature Kalman filter[J]. Applied Energy, 2020, 265: 114789.
20	ZHAO H, LIAO C, ZHANG C, et al. State-of-charge estimation of lithium-ion battery: Joint long short-term memory network and adaptive extended Kalman filter online estimation algorithm[J]. Journal of Power Sources, 2024, 604: 234451.
21	ZHENHAO S U, LI X J, QIN J, et al. SOC estimation method of power battery based on BP artificial neural network[J]. Energy Storage Science and Technology, 2019, 8(5): 868-874.
22	ALMAITA E, ALSHKOOR S, ABDELSALAM E, et al. State of charge estimation for a group of lithium-ion batteries using long short-term memory neural network[J]. Journal of Energy Storage, 2022, 52: 104761.
23	LIU D, WANG S, FAN Y, et al. An optimized multi-segment long short-term memory network strategy for power lithium-ion battery state of charge estimation adaptive wide temperatures[J]. Energy, 2024, 304: 132048.
24	CHEN Y, LI C, CHEN S, et al. A combined robust approach based on auto‐regressive long short‐term memory network and moving horizon estimation for state‐of‐charge estimation of lithium‐ion batteries[J]. International Journal of Energy Research, 2021, 45(9): 12838-12853.
25	YANG F, SONG X, XU F, et al. State-of-charge estimation of lithium-ion batteries via long short-term memory network[J]. IEEE Access, 2019, 7: 53792-53799.
26	宋洁,赵雪莹,朱玉婷,等.基于门控神经网络的储能电站荷电状态估计研究[J].电工电能新技术,2022,41(04):82-88.
	SONG J, ZHAO X Y, ZHU Y T, et al. Research on state of charge estimation of energy storage station based on gated neural network[J]. Advanced Technology of Electrical Engineering and Energy, 2022, 41(04): 82-88.
27	罗勇,祁朋伟,黄欢,等.基于容量修正的安时积分SOC估算方法研究[J].汽车工程,2020,42(05):681-687.
	LUO Y, QI P W, HUANG H, et al. Research on SOC estimation method based on ampere-hour integration with capacity correction[J]. Automotive Engineering, 2020, 42(05): 681-687.
28	韦莉,张世博,姚勇涛,等.基于动态容值修正的混合型超级电容器SOC估计[J].中国电机工程学报,2017,37(17):5188-5197+5239.
	WEI L, ZHANG S B, YAO Y T, et al. SOC estimation of hybrid supercapacitors based on dynamic capacity correction[J]. Proceedings of the CSEE, 2017, 37(17): 5188-5197+5239.
29	杨斌,樊立萍,高迎慧,等.高功率锂离子电池放电倍率对容量影响的研究[J].机械制造,2023,61(08):1-4+17.
	YANG B, FAN L P, GAO Y H, et al. Study on the effect of discharge rate on the capacity of high-power lithium-ion batteries[J]. Machinery Manufacture, 2023, 61(08): 1-4+17.
30	李悦,李天奇,秦建华,等.18650磷酸铁锂电池不同放电倍率下产热机理研究[J].电源技术,2021,45(08):1001-1004.
	LI Y, LI T Q, QIN J H, et al. Study on the heat generation mechanism of 18650 lithium iron phosphate battery under different discharge rates[J]. Chinese Journal of Power Sources, 2021, 45(08): 1001-1004.
31	王顺利,李小霞, 熊莉英等著.(2021).锂电池等效电路建模与荷电状态估计. 机械工业出版社.
	WANG S L, LI X X, XIONG L Y, et al. Lithium battery equivalent circuit modeling and state-of-charge estimation[M]. Beijing: China Machine Press, 2021.
32	LI J, CHENG Y, JIA M, et al. An electrochemical–thermal model based on dynamic responses for lithium iron phosphate battery[J]. Journal of Power Sources, 2014, 255: 130-143.
33	LAI Y, DU S, AI L, et al. Insight into heat generation of lithium ion batteries based on the electrochemical-thermal model at high discharge rates[J]. International Journal of Hydrogen Energy, 2015, 40(38): 13039-13049.

放电倍率	容量
0.5C	127.91
0.75C	127.25
1C	126.69
1.5C	125.49
2C	123.51

电池参数	数值
额定容量	125Ah
充电截止电压	3.65V
放电截止电压	2.5V

方法	RMSE	MAE	ME
EKF	2.9937	2.8248	4.5807
考虑容量修正的EKF	1.7728	1.5576	3.0820

方法	RMSE	MAE	ME
EKF	2.9937	2.8248	4.5807
CLA	0.7433	0.5523	3.9093
LSTM-EKF	1.1992	1.0373	3.8048
CLA-EKF	0.4498	0.3859	1.0796

方法	RMSE	MAE	ME
考虑容量修正的EKF法	1.7728	1.5576	3.0820
CLA-EKF算法	0.4498	0.3859	1.0796
本文方法	0.2769	0.1722	0.8908