Estimation of lithium-ion battery state of charge based on UGOA-BP

doi:10.19799/j.cnki.2095-4239.2021.0352

Abstract

Abstract:

Accurate estimation of battery state of charge (SOC) is an important guarantee for the safe operation of energy storage devices. This work proposes a joint algorithm based on an improved grasshopper optimization algorithm and a backpropagation neural network (UGOA-BP). The algorithm introduces a uniform distribution function based on the standard grasshopper optimization algorithm, updates the control parameters, and builds a new system of random adjustment. The location update increases the population diversity and makes up for the limitations of the weak global search capability of the locust optimization algorithm. Historical data of energy storage equipment from a new energy company was used. The complete and partial discharge process data set of battery SOC from 100% to 0% and 52% to 49%, respectively, were selected. The proposed model, the grasshopper optimization algorithm BP neural network (GOA-BP), and the traditional BP neural network model were compared, tested, and analyzed from two dimensions. The simulation results show that the absolute errors of the UGOA-BP predicted values are all in the range of [-0.050, 0.050], with -0.046 maximum absolute error and 0.001 average mean square error. The average mean square errors of the GOA-BP model and the BP neural network are 0.009 and 0.067, respectively. Thus, the model prediction accuracy is better than other methods, with reasonable accuracy and engineering application value.

Key words: lithium-ion battery, state of charge, grasshopper optimisation, BP neural network

CLC Number:

TM 912

Shuai WANG, Hongyan MA, Jiaming DOU, Yingda ZHANG, Shengyan LI, Lujin HU. Estimation of lithium-ion battery state of charge based on UGOA-BP[J]. Energy Storage Science and Technology, 2022, 11(1): 258-264.

Figures/Tables 8

Fig. 1

Fig. 2

Table 1

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Table 2

References 19

1	张天任, 鄢丽娜. 大力发展智慧型储能电站[N]. 中国煤炭报, 2021-04-15(4).
	ZHANG T R, YAN L N. Vigorous development of intelligent energy storage plants[N]. China Coal News, 2021-04-15(4).
2	季迎旭, 杜海江, 孙航. 蓄电池SOC估算方法综述[J]. 电测与仪表, 2014, 51(4): 18-22.
	JI Y X, DU H J, SUN H. A survey of state of charge estimation methods[J]. Electrical Measurement & Instrumentation, 2014, 51(4): 18-22.
3	苏振浩, 李晓杰, 秦晋, 等. 基于BP人工神经网络的动力电池SOC估算方法[J]. 储能科学与技术, 2019, 8(5): 868-873.
	SU Z H, 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): 868-873.
4	LEE J, NAM O, CHO B H. Li-ion battery SOC estimation method based on the reduced order extended Kalman filtering[J]. Journal of Power Sources, 2007, 174(1): 9-15.
5	ZHANG S Z, GUO X, ZHANG X W. An improved adaptive unscented kalman filtering for state of charge online estimation of lithium-ion battery[J]. Journal of Energy Storage, 2020, 32: 101980.
6	CHEN Z, FU Y, MI C C. State of charge estimation of lithium-ion batteries in electric drive vehicles using extended Kalman filtering[J]. IEEE Transactions on Vehicular Technology, 2012, 62(3): 1020-1030.
7	蒋聪, 王顺利, 李小霞, 等. 基于改进EKF算法变温度下的动力锂电池SOC估算[J]. 储能科学与技术, 2020, 9(1): 145-151.
	JIANG C, WANG S L, LI X X, et al. Estimation method of SOC for power lithium battery based on improved EKF algorithm adaptive to various temperature[J]. Energy Storage Science and Technology, 2020, 9(1): 145-151.
8	张远进, 吴华伟, 叶从进. 基于AUKF-BP神经网络的锂电池SOC估算[J]. 储能科学与技术, 2021, 10(1): 237-241.
	ZHANG Y J, WU H W, YE C J. Estimation of the SOC of a battery based on the AUKF-BP algorithm[J]. Energy Storage Science and Technology, 2021, 10(1): 237-241.
9	俞美鑫, 施卫, 蒋龙, 等. 基于GA-BP神经网络的动力锂电池SOC估算[J]. 电子技术应用, 2020, 46(1): 104-107,112.
	YU M X, SHI W, JIANG L, et al. SOC estimation of power lithium battery based on GA-BP neural network[J]. Application of Electronic Technique, 2020, 46(1): 104-107, 112.
10	于仲安, 褚彪, 葛庭宇. 基于HPSO-BP神经网络融合的锂电池SOC预估研究[J]. 汽车技术, 2019(6): 20-24.
	YU Z, CHU B, GE T Y. Estimation for SOC of Li-ion battery based on HPSO-BP neural network fusion[J]. Automobile Technology, 2019(6): 20-24.
11	朱元富, 贺文武, 李建兴, 等. 基于Bi-LSTM/Bi-GRU循环神经网络的锂电池SOC估计[J]. 储能科学与技术, 2021, 10(3): 1163-1176.
	ZHU Y F, HE W W, LI J X, et al. SOC estimation for Li-ion batteries based on Bi-LSTM and Bi-GRU[J]. Energy Storage Science and Technology, 2021, 10(3): 1163-1176.
12	成文晶, 潘庭龙. 基于分布估计算法LSSVM的锂电池SOC预测[J]. 储能科学与技术, 2020, 9(6): 1948-1953.
	CHENG W J, PAN T L. Prediction for SOC of lithium-ion batteries by estimating the distribution algorithm with LSSVM[J]. Energy Storage Science and Technology, 2020, 9(6): 1948-1953.
13	SAREMI S, MIRJALILI S, LEWIS A. Grasshopper optimisation algorithm: Theory and application[J]. Advances in Engineering Software, 2017, 15: 30-47.
14	ARORA S, ANAND P. Chaotic grasshopper optimization algorithm for global optimization[J]. Neural Computing and Applications, 2019, 31(8): 4385-4405.
15	EWEES A A, ELAZIZ M A, HOUSSEIN E H. Improved grasshopper optimization algorithm using opposition-based learning[J]. Expert Systems with Applications, 2018, 112: 156-172.
16	何正风. MATLAB R2015b神经网络技术[M]. 北京: 清华大学出版社, 2016.
	HE Z F. MATLAB R2015b neural network technology[M]. Beijing: Tsinghua University Press, 2016.
17	吴忠强, 申丹丹, 尚梦瑶, 等. 基于改进蝗虫优化算法的光伏电池模型参数辨识[J]. 计量学报, 2020, 41(12): 90-97.
	WU Z Q, SHEN D D, SHANG M Y, et al. Parameter identification of photovoltaic cell model based on improved grasshopper optimization algorithm[J]. Acta Metrologica Sinica, 2020, 41(12): 90-97.
18	何庆, 林杰, 徐航. 混合柯西变异和均匀分布的蝗虫优化算法[J]. 控制与决策, 2021, 36(7): 1558-1568.
	HE Q, LIN J, XU H. Hybrid Cauchy mutation and uniform distribution of grasshopper optimization algorithm[J]. Control and Decision, 2021, 36(7): 1558-1568.
19	KENNEDY J, EBERHART R. Particle swarm optimization[C]// Proceedings of ICNN'95-international conference on neural networks. IEEE, 1995, 4: 1942-1948.

项目	参数	项目	参数
标称容量	120 A·h	额定充电功率	200 W
标称电压	3.2 V	额定充电能量	400 W·h
电池内阻	<0.3 mΩ	额定放电功率	190 W
电池重量	≤2.86 kg	额定放电能量	380 W·h
工作电压	2.5～3.65 V 2.0～3.65 V	放电工作温度	-20～55 ℃
充电工作温度	0～50 ℃	贮存温度	-30～45 ℃

模型	UGOA-BP	GOA-BP	BP
e_MSE	0.001	0.009	0.067
e_MAE	0.018	0.040	0.115
e_MAPE/%	5.54	29.81	54.61

[1]	Shunmin YI, Linbo XIE, Li PENG. Remaining useful life prediction of lithium-ion batteries based on VF-DW-DFN [J]. Energy Storage Science and Technology, 2022, 11(7): 2305-2315.
[2]	Qingwei ZHU, Xiaoli YU, Qichao WU, Yidan XU, Fenfang CHEN, Rui HUANG. Semi-empirical degradation model of lithium-ion battery with high energy density [J]. Energy Storage Science and Technology, 2022, 11(7): 2324-2331.
[3]	Yuzuo WANG, Jin WANG, Yinli LU, Dianbo RUAN. Study on the effects of pore structure on lithium-storage performances for soft carbon [J]. Energy Storage Science and Technology, 2022, 11(7): 2023-2029.
[4]	Wei KONG, Jingtao JIN, Xipo LU, Yang SUN. Study on cooling performance of lithium ion batteries with symmetrical serpentine channel [J]. Energy Storage Science and Technology, 2022, 11(7): 2258-2265.
[5]	Tian WU, Mincheng LIN, Hao HAI, Haiyu SUN, Zhaoyin WEN, Fuyuan MA. Development of high-power Ni-MH battery system for primary frequency modulation [J]. Energy Storage Science and Technology, 2022, 11(7): 2213-2221.
[6]	YAN Qiaoyi, WU Feng, CHEN Renjie, LI Li. Recovery and resource recycling of graphite anode materials for spent lithium-ion batteries [J]. Energy Storage Science and Technology, 2022, 11(6): 1760-1771.
[7]	WANG Yuzuo, DENG Miao, WANG Jin, YANG Bin, LU Yinli, JIN Ge, RUAN Dianbo. Study on the effects of carbonization temperature on lithium-storage kinetics for soft carbon [J]. Energy Storage Science and Technology, 2022, 11(6): 1715-1724.
[8]	YU Chunhui, HE Ziying, ZHANG Chenxi, LIN Xianqing, XIAO Zhexi, WEI Fei. The analyses and suppressing strategies of silicon anode with the electrolyte [J]. Energy Storage Science and Technology, 2022, 11(6): 1749-1759.
[9]	WANG Can, MA Pan, ZHU Guoliang, WEI Shuimiao, YANG Zhilu, ZHANG Zhiyu. Effect of lithium acrylic-coated nature graphite on its electrochemical properties [J]. Energy Storage Science and Technology, 2022, 11(6): 1706-1714.
[10]	LIU Hangxin, CHEN Xiantao, SUN Qiang, ZHAO Chenxi. Cycle performance characteristics of soft pack lithium-ion batteries under vacuum environment [J]. Energy Storage Science and Technology, 2022, 11(6): 1806-1815.
[11]	Guangyu CHENG, Xinwei LIU, Yueni MEI, Honghui GU, Cheng YANG, Ke WANG. Capacity fading analysis of lithium-ion battery after high temperature storage [J]. Energy Storage Science and Technology, 2022, 11(5): 1339-1349.
[12]	Yanwen DAI, Aiqing YU. Combined CNN-LSTM and GRU based health feature parameters for lithium-ion batteries SOH estimation [J]. Energy Storage Science and Technology, 2022, 11(5): 1641-1649.
[13]	Chunjing LIN, Danhua LI, Haoran WEN, Tianyi MA, Hong CHANG, Peixiang CHANG, Haiqiang LI, Shiqiang LIU. Research on swelling force characteristics of power battery during charging [J]. Energy Storage Science and Technology, 2022, 11(5): 1627-1633.
[14]	Qiaomin KE, Jian GUO, Yiwei WANG, Wenjiong CAO, Man CHEN, Fangming JIANG. The effect of liquid-cooled thermal management on thermal runaway of power battery [J]. Energy Storage Science and Technology, 2022, 11(5): 1634-1640.
[15]	Feng TIAN, Zhijiang CHENG, Handi YANG, Tianxiang YANG. Fault-tolerant control strategy for modular multi-level hybrid converter battery energy storage system [J]. Energy Storage Science and Technology, 2022, 11(5): 1583-1591.