Energy state estimation of lithium-ion batteries based on sage-husa EKF algorithm

doi:10.19799/j.cnki.2095-4239.2022.0277

Abstract

Abstract:

It is necessary to accurately estimate the state of energy (SOE) of a battery to make full use of the energy stored in the battery and prevent over-discharge and over-charge. Generally, SOE is defined as the ratio of the remaining energy to the standard rated energy. The current SOE estimation algorithms have not fully considered the influence of temperature, operating conditions, etc., leading to low accuracy of the estimation results. This study proposes an adaptive extended Kalman filter (S-H EKF) based on Sage-Husa to accurately estimate the energy state of a battery. The algorithm's energy state estimation approach for lithium-ion batteries is compared with the traditional SOE estimation algorithm based on the EKF. First, the influence of temperature on the battery's energy characteristics was examined, and the standard energy of the battery at various temperatures was obtained. Then, a second-order RC equivalent circuit model considering the variation of parameter values with temperature and SOE value is established. Combined with an experiment of mixed pulse power characteristics, the least squares approach is employed to identify the model parameters, and the model accuracy is verified. With a good simulation of the battery's terminal voltage having high accuracy, the verification findings reveal that the model can be compared better. Finally, by employing S-H EKF and EKF to estimate the SOE under dynamic conditions and intermittent high-rate charging conditions, the comparison results demonstrate that: taking the mean absolute error as the comparison standard, the estimation accuracy of S-H EKF is 20.72% higher than that of EKF, and the maximum SOE estimation is the absolute error is less than 3%, which is more appropriate for energy estimation of lithium-ion batteries.

Key words: lithium ion battery, energy state, equivalent circuit model, extended Kalman filter, adaptive extended Kalman filter

CLC Number:

TM 912

Xiaohan LI, Lei SUN, Yong MA, Dongliang GUO, Peng XIAO, Jianjun LIU, Peng WU, Zhihang ZHANG, Xuebing HAN. Energy state estimation of lithium-ion batteries based on sage-husa EKF algorithm[J]. Energy Storage Science and Technology, 2022, 11(11): 3603-3612.

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References 20

1	朱伟杰, 史尤杰, 雷博. 锂离子电池储能系统BMS的功能安全分析与设计[J]. 储能科学与技术, 2020, 9(1): 271-278.
	ZHU W J, SHI Y J, LEI B. Functional safety analysis and design of BMS for lithium-ion battery energy storage system[J]. Energy Storage Science and Technology, 2020, 9(1): 271-278.
2	卢兰光, 李建秋, 华剑锋, 等. 电动汽车锂离子电池管理系统的关键技术[J]. 科技导报, 2016, 34(6): 39-51.
	LU L G, LI J Q, HUA J F, et al. A review on the key issues of the lithium-ion battery management[J]. Science ＆ Technology Review, 2016, 34(6): 39-51.
3	梁新成, 张勉, 黄国钧. 基于BMS的锂离子电池建模方法综述[J]. 储能科学与技术, 2020, 9(6): 1933-1939.
	LIANG X C, ZHANG M, HUANG G J. Review on lithium-ion battery modeling methods based on BMS[J]. Energy Storage Science and Technology, 2020, 9(6): 1933-1939.
4	谭泽富, 孙荣利, 杨芮, 等. 电池管理系统发展综述[J]. 重庆理工大学学报(自然科学), 2019, 33(9): 40-45.
	TAN Z F, SUN R L, YANG R, et al. Overview of battery management system[J]. Journal of Chongqing University of Technology (Natural Science), 2019, 33(9): 40-45.
5	胡晓松, 唐小林. 电动车辆锂离子动力电池建模方法综述[J]. 机械工程学报, 2017, 53(16): 20-31.
	HU X S, TANG X L. Review of modeling techniques for lithium-ion traction batteries in electric vehicles[J]. Journal of Mechanical Engineering, 2017, 53(16): 20-31.
6	王海峰. 纯电动汽车锂动力电池能量状态估算算法研究[D]. 长春: 吉林大学, 2012.
	WANG H F. Study on state of energy estimation algorithm for lithium power battery used on pure electric vehicle[D]. Changchun: Jilin University, 2012.
7	范汝新, 张宵洋, 张振福, 等. 锂电池能量状态与功率状态的联合估计[J]. 电源技术, 2021, 45(10): 1252-1255, 1259.
	FAN R X, ZHANG X Y, ZHANG Z F, et al. Joint estimation of state of energy and state of power of lithium battery[J]. Chinese Journal of Power Sources, 2021, 45(10): 1252-1255, 1259.
8	尹乐乐, 靳成杰, 康健强, 等. 基于DP模型的锂离子电池能量状态估算[J]. 电源技术, 2019, 43(10): 1619-1622.
	YIN L L, JIN C J, KANG J Q, et al. Estimation of state of energy of lithium-ion batteries based on DP model[J]. Chinese Journal of Power Sources, 2019, 43(10): 1619-1622.
9	LIU X T, WU J, ZHANG C B, et al. A method for state of energy estimation of lithium-ion batteries at dynamic currents and temperatures[J]. Journal of Power Sources, 2014, 270: 151-157.
10	刘光明, 欧阳明高, 卢兰光, 等. 基于电池能量状态估计和车辆能耗预测的电动汽车续驶里程估计方法研究[J]. 汽车工程, 2014, 36(11): 1302-1309, 1301.
	LIU G M, OUYANG M G, LU L G, et al. Driving range estimation for electric vehicles based on battery energy state estimation and vehicle energy consumption prediction[J]. Automotive Engineering, 2014, 36(11): 1302-1309, 1301.
11	何美光, 葛泉波, 赵嘉懿. 一种Sage-Husa和可观测度的滤波算法研究[J]. 控制工程, 2021, 28(1): 120-126.
	HE M G, GE Q B, ZHAO J Y. Research on a filtering algorithm based on sage-husa and observable degree[J]. Control Engineering of China, 2021, 28(1): 120-126.
12	封居强, 伍龙, 黄凯峰, 等. 基于FFRLS和AEKF的锂离子电池SOC在线估计研究[J]. 储能科学与技术, 2021, 10(1): 242-249.
	FENG J Q, WU L, HUANG K F, et al. Online SOC estimation of a lithium-ion battery based on FFRLS and AEKF[J]. Energy Storage Science and Technology, 2021, 10(1): 242-249.
13	孙爱芬, 赤娜. 基于改进的高斯过程回归的SOC估计算法[J]. 储能科学与技术, 2022, 11(1): 253-257.
	SUN A F, CHI N. SOC estimation algorithm based on improved Gaussian process regression[J]. Energy Storage Science and Technology, 2022, 11(1): 253-257.
14	郭阳东, 李玉芳, 张文浩, 等. 典型工况下动力电池温度特性研究[J]. 电源技术, 2018, 42(8): 1143-1147.
	GUO Y D, LI Y F, ZHANG W H, et al. Research on temperature performance of power battery under typical condition[J]. Chinese Journal of Power Sources, 2018, 42(8): 1143-1147.
15	李礼夫, 龚定旺, 韦毅, 等. 磷酸铁锂电池恒流充电过程中的温度特性分析[J]. 电源技术, 2017, 41(12): 1706-1708, 1732.
	LI L F, GONG D W, WEI Y, et al. Lithium iron phosphate battery temperature characteristic analysis during constant current charging[J]. Chinese Journal of Power Sources, 2017, 41(12): 1706-1708, 1732.
16	SHEN P, OUYANG M G, HAN X B, et al. Error analysis of the model-based state-of-charge observer for lithium-ion batteries[J]. IEEE Transactions on Vehicular Technology, 2018, 67(9): 8055-8064.
17	SHEN P, OUYANG M G, LU L G, et al. The co-estimation of state of charge, state of health, and state of function for lithium-ion batteries in electric vehicles[J]. IEEE Transactions on Vehicular Technology, 2018, 67(1): 92-103.
18	WANG Y J, TIAN J Q, SUN Z D, et al. A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems[J]. Renewable and Sustainable Energy Reviews, 2020, 131: doi: 10.1016/j.rser.2020.110015.
19	叶丽华, 王海钰, 施烨璠, 等. IPSO-EKF融合算法的SOC估算研究[J]. 重庆理工大学学报(自然科学), 2021, 35(12): 18-27.
	YE L H, WANG H Y, SHI Y F, et al. Research on SOC prediction based on fusion algorithm of IPSO-EKF[J]. Journal of Chongqing University of Technology (Natural Science), 2021, 35(12): 18-27.
20	SAGE A P, HUSA G W. Adaptive filtering with unknown prior statistics[C]//Joint Automatic Control Conference, 1969 (7): 760-769.

温度/℃	45	25	10	0	-10	-20
能量/Wh	41.64	39.42	37.28	31.20	23.89	17.94
容量/Ah	13.03	12.31	11.20	10.11	7.67	6.46

温度/℃	误差均值/mV	误差均方根值/mV
45	3.9	17.6
25	5.3	16.22
-20	26.3	51.8

温度/℃	算法	绝对误差均值/%	误差最大绝对值/%	误差均方根值/%
25	EKF	2.07	2.92	2.21
25	S-H EKF	1.41	1.93	1.44
45	EKF	0.75	2.45	0.83
45	S-H EKF	0.67	0.84	0.68
-20	EKF	1.72	2.38	1.78
-20	S-H EKF	1.50	1.84	1.54
45 ℃间歇大倍率充电	EKF	1.09	2.08	1.21
45 ℃间歇大倍率充电	S-H EKF	0.70	0.83	0.71

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