SOC Estimation of Lithium Battery Based on Monte Carlo and SH-AUKF Algorithm

doi:10.11985/2022.03.008

Abstract

Abstract:

Aiming at the problem of low estimation accuracy of SOC of lithium battery, a new adaptive filtering algorithm, SH-AUKF algorithm is proposed by combining Sage-Husa adaptive algorithm with AUKF method. SH-AUKF algorithm can update and modify system noise continuously. UKF, AUKF and SH-AUKF algorithms are used to estimate SOC under DST conditions. The results show that SH-AUKF algorithm has the lowest estimation error and the highest estimation accuracy. Compared with UKF, the estimation accuracy of SH-AUKF algorithm is improved by 45.4%. Compared with AUKF, the estimation accuracy of SH-AUKF algorithm is improved by 14.3%. In order to further reduce the influence of accidental and sudden noise interference on SOC estimation, Monte Carlo sampling method is added in the estimation process. The results show that the error range of SH-AUKF algorithm combined with Monte Carlo method is only ±1×10^-3, which effectively improves the estimation accuracy.

Key words: Lithium-ion battery, state of charge, Sage-Husa, Monte Carlo

CLC Number:

TM911

WU Chunling, CHENG Yanqing, XU Xianfeng, MENG Jinhao, XIE Meimei. SOC Estimation of Lithium Battery Based on Monte Carlo and SH-AUKF Algorithm[J]. Journal of Electrical Engineering, 2022, 17(3): 66-75.

Figures/Tables 17

References 19

[1]	林成涛, 王军平, 陈全世. 电动汽车SOC估计方法原理与应用[J]. 电池, 2004, 34(5):376-378.
	LIN Chengtao, WANG Junping, CHEN Quanshi. Methods for state of charge estimation of EV batteries and their application[J]. Battery Bimonthly, 2004, 34(5):376-378.
[2]	谈发明, 赵俊杰, 王琪. 动力电池SOC估计的一种新型鲁棒UKF算法[J]. 汽车工程, 2019, 41(8):944-952.
	TAN Faming, ZHAO Junjie, WANG Qi. A novel robust UKF algorithm for SOC estimation of traction battery[J]. Automotive Engineering, 2019, 41(8):944-952.
[3]	黄文华, 韩晓东, 陈全世, 等. 电动汽车SOC估计算法与电池管理系统的研究[J]. 汽车工程, 2007, 29(3):198-202.
	HUANG Wenhua, HAN Xiaodong, CHEN Quanshi, et al. A study on SOC estimation algorithm and battery management system for electric vehicle[J]. Automotive Engineering, 2007, 29(3):198-202.
[4]	王海群, 王水满, 张怡. 基于SSRCKF算法估计锂离子电池的SOC[J]. 电池, 2019, 49(4):301-304.
	WANG Haiqun, WANG Shuiman, ZHANG Yi. Estimation of SOC in Li-ion battery based on SSRCKF algorithm[J]. Battery Bimonthly, 2019, 49(4):301-304.
[5]	李伟, 刘伟嵬, 邓业林. 基于扩展卡尔曼滤波的锂电池SOC估计[J]. 中国机械工程, 2020, 31(3):321-327.
	LI Wei, LIU Weiwei, DENG Yelin. SOC estimation for lithium-ion batteries baesd on EKF[J]. China Mechanical Engineering, 2020, 31(3):321-327.
[6]	颜湘武, 邓浩然, 郭琪, 等. 基于自适应无迹卡尔曼滤波的动力电池健康状态检测及梯次利用研究[J]. 电工技术学报, 2019, 34(18):3937-3948.
	YAN Xiangwu, DENG Haoran, GUO Qi, et al. Study on the state of health detection of power batteries based on adaptive unscented Kalman filters and the battery echelon utilization[J]. Transactions of China Electrotechnical Society, 2019, 34(18):3937-3948.
[7]	安治国, 田茂飞, 赵琳, 等. 基于自适应无迹卡尔曼滤波的锂电池SOC估计[J]. 储能科学与技术, 2019, 8(5):856-861.
	AN Zhiguo, TIAN Maofei, ZHAO Lin, et al. SOC estimation of lithium battery based on adaptive untracked Kalman filter[J]. Energy Storage Science and Technology, 2019, 8(5):856-861.
[8]	黄凯, 郭永芳, 李志刚. 动力锂离子电池荷电状态估计综述[J]. 电源技术, 2018, 42(9):1398-1401.
	HUANG Kai, GUO Yongfang, LI Zhigang. Review of state of charge estimation methods for power lithium-ion battery[J]. Chinese Journal of Power Sources, 2018, 42(9):1398-1401.
[9]	梁嘉宁, 谭霁宬, 孙天夫, 等. 基于容积卡尔曼滤波算法估计动力锂电池荷电状态[J]. 集成技术, 2018, 7(6):31-38.
	LIANG Jianing, TAN Jicheng, SUN Tianfu, et al. Power lithium battery state of charge estimation cubature Kalman filtering[J]. Journal of Integration Technology, 2018, 7(6):31-38.
[10]	李争, 张丽平. 基于无迹卡尔曼滤波的锂离子电池SOC估计[J]. 电池, 2018, 48(5):313-317.
	LI Zheng, ZHANG Liping. SOC estimation of Li-ion battery based on unscented Kalman filter[J]. Battery Bimonthly, 2018, 48(5):313-317.
[11]	刘胜永, 于跃, 罗文广, 等. 基于自适应无迹卡尔曼滤波的锂电池SOC估计[J]. 控制工程, 2017, 24(8):1611-1616.
	LIU Shengyong, YU Yue, LUO Wenguang, et al. Estimation of state of charge for lithium battery based on adaptive unscented Kalman filter[J]. Control Engineering of China, 2017, 24(8):1611-1616.
[12]	赵云飞, 徐俊, 王霄, 等. 双自适应衰减卡尔曼滤波锂电池荷电状态估计[J]. 西安交通大学学报, 2018, 52(12):99-105.
	ZHAO Yunfei, XU Jun, WANG Xiao, et al. An estimation method for state of charge of lithium-ion batteries using dual adaptive fading extendde Kalman filter[J]. Journal of Xi’an Jiaotong University, 2018, 52(12):99-105.
[13]	朱丽群, 张建秋. 一种联合锂电池健康和荷电状态的新模型[J]. 中国电机工程学报, 2018, 38(12):3613-3620.
	ZHU Liqun, ZHANG Jianqiu. A new model of jointed states of charge and health for lithium batteries[J]. Proceedings of the CSEE, 2018, 38(12):3613-3620.
[14]	熊瑞, 何洪文, 许永莉, 等. 电动汽车用动力锂电池组建模和参数辨识方法[J]. 吉林大学学报, 2012, 42(4):809-815.
	XIONG Rui, HE Hongwen, XU Yongli, et al. Modeling and parameter identification approach for power battery pack used in electric vehicle[J]. Journal of Jilin University, 2012, 42(4):809-815.
[15]	王霄, 徐俊, 曹秉刚, 等. 小波降噪卡尔曼滤波锂电池荷电状态估计[J]. 西安交通大学学报, 2017, 51(10):71-76.
	WANG Xiao, XU Jun, CAO Binggang, et al. A Kalman filter SOC estimation method for lithium-ion batteries based on discrete wavelet transform denoising[J]. Journal of Xi’an Jiaotong University, 2017, 51(10):71-76.
[16]	TANIM T R, RAHN C D, WANG C Y. State of charge estimation of a lithium ion cell based on a temperature dependent and electrolyte enhanced single particle model[J]. Energy, 2015, 80:731-739. doi: 10.1016/j.energy.2014.12.031
[17]	RAGHAVAN A, KIESEL P, SOMMER L W, et al. Embedded fiber-optic sensing for accurate internal monitoring of cell state in advanced battery management systems part 1:Cell embedding method and performance[J]. J. Power Sources, 2017, 341:466-473. doi: 10.1016/j.jpowsour.2016.11.104
[18]	SMITH K A, WANG C Y. Power and thermal characterization of a lithium-ion battery pack for hybrid-electric vehicles[J]. J. Power Sources, 2006, 160:662-673. doi: 10.1016/j.jpowsour.2006.01.038
[19]	ANDERSON R, ZANE R, PLETT G, et al. Life balancing—A better way to balance large batteries[R]. SAE Tech. Paper, 2017-01-1210,2017.

参数	数值
容量/(mA·h)	2 000
直径/mm	18.33±0.07
长度/mm	64.85±0.15
额定电压/V	3.7
充电截止电压/V	4.2
放电截止电压/V	2.75

参数	数值
欧姆内阻R₀/Ω	0.102
极化内阻R_p/Ω	0.266
极化电容C_p/F	21.125
充电内阻R_c/Ω	0.023
放电内阻R_d/Ω	0.026

算法	最大误差值	MAE	RMSE
UKF	0.007 375	0.003 062	0.002 828
AUKF	0.006 432	0.002 780	0.002 779
SH-AUKF	0.004 311	0.000 927	0.000 050

算法	最大误差值	MAE	RMSE
UKF	0.004 666	0.002 972	0.002 711
AUKF	0.003 443	0.002 587	0.002 477
SH-AUKF	0.000 771	0.000 174	0.000 025

方案	误差区间
方案一	±3×10^-3
方案二	±1×10^-3