Lithium Battery SOC Estimation Based on Multi Innovation Least Square and Multi Innovation Extended Kalman Filter Algorithm

doi:10.12141/j.issn.1000-565X.220636

Abstract

Abstract:

The existing State of Charge (SOC) estimation methods assume constant parameters for the battery model and do not consider the dynamic changes in these parameters, resulting in imprecise SOC estimates. In view of this limitation, the paper introduced an algorithm that combines online identification of battery model parameters with SOC estimation. Based on the second-order RC equivalent circuit model, it used Multi Innovation Least Squares (MILS) algorithm to identify the parameters in the lithium-ion battery model online, so as to modify the battery model in real time. At the same time, based on the modified battery model, it estimated the battery state of charge through Multi Innovation Extended Kalman Filter (MIEKF) algorithm. MILS algorithm can solve the problem of initial error accumulation in the process of online parameter identification, and can realize online accurate identification of model parameters. MIEK algorithm combines multi-innovation theory and Kalman filter theory, adds forgetting factor to weaken historical data and correct weight, solves the problem of data oversaturation, and has high accuracy and convergence. The experimental results show that, when identifying the parameters of the battery model, the Root Mean Square Error (RMSE) of the MILS algorithm is 1.4 mV, the RMSE of the RLS algorithm is 1.9 mV, and the estimation accuracy is improved by 26.3%. For the SOC estimation after parameter identification, the RMSE estimated by the MIEKF algorithm is 0.003 7, while the RMSE estimated by the EKF and AEKF algorithms are 0.007 3 and 0.005 2, respectively. The MIEKF algorithm improves the estimation accuracy by 49.31% compared to the EKF algorithm and by 28.84% compared to the AEKF algorithm. Moreover, in the case of an incorrect initial SOC value, the proposed algorithm can converge to the true value after about 30 seconds of battery operation. The algorithm proposed in the paper is an effective estimation method with high accuracy and good robustness.

Key words: SOC estimation, multi-innovation, parameter identification, extended Kalman filter

CLC Number:

TM912

WU Chunling, FU Juncheng, XU Xianfeng, et al. Lithium Battery SOC Estimation Based on Multi Innovation Least Square and Multi Innovation Extended Kalman Filter Algorithm[J]. Journal of South China University of Technology(Natural Science Edition), 2024, 52(2): 74-83.

Figures/Tables 17

Fig.1

Table 1

Main parameters of experimental battery"

电池参数	数值
标称容量/（A·h）	35
标称电压/V	3.7
充电截止电压/V	4.2
放电截止电压/V	3.0
电池内阻/m $Ω$	≤10
质量/g	1 080±10

Table 1

Table 2

Fitting results of different polynomials"

阶数	RSS/10^-5	$R 2$
4	40.25	0.999 5
5	37.41	0.999 6
6	19.93	0.999 8
7	9.65	0.999 9

Table 2

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Table 3

εmax and RRMSE of terminal voltage for two algorithms"

算法	$ε m a x$ /mV	$R R M S E$ /mV
RLS	69.20	1.9
MILS	39.32	1.4

Table 3

Fig.10

Fig.11

Table 4

εmax and RRMSE for SOC estimation using three algorithms"

算法	$ε m a x$	$R R M S E$
EKF	0.013 5	0.007 3
AEKF	0.008 6	0.005 2
MIEKF	0.007 4	0.003 7

Table 4

Fig.12

Table 5

Maximum absolute error of initial value of different SOC"

错误SOC初始值	全时间尺度的 $ε m a x$	收敛后的 $ε m a x$
0.9	0.050 5	0.007 4
0.8	0.153 9	0.007 4
0.6	0.286 3	0.007 4

Table 5

References 21

1	王榘，熊瑞，穆浩．温度和老化意识融合驱动的电动车辆锂离子动力电池电量和容量协同估计［J］．电工技术学报，2020，35（23）：4980-4987.
	WANG Ju， XIONG Rui， MU Hao ．Co-estimation of lithium-ion battery state-of-charge and capacity through the temperature and aging awareness model for electric vehicles［J］．Transactions of China Electrotechnical Society，2020，35（23）：4980-4987.
2	HE W， WILLIARD N， CHEN C C，et al ．State of charge estimation for electric vehicle batteries using unscented Kalman filtering［J］．Microelectronics Reliability，2013，53（6）：840-847.
3	BIAN C， YANG S， MIAO Q ．Cross-domain state-of-charge estimation of Li-ion batteries based on deep transfer neural network with multiscale distribution adaptation［J］．IEEE Transactions on Transportation Electrification，2021，7（3）：1260-1270.
4	ZHAO Y F， XU J， WANG X，et al ．The adaptive fading extended Kalman filter SOC estimation method for lithium-ion batteries［J］．Energy Procedia，2018，145：357-362.
5	WEI Z B， ZHAO J Y， ZOU C F，et al ．Comparative study of methods for integrated model identification and state of charge estimation of lithium-ion battery［J］．Journal of Power Sources，2018，402：189-197.
6	WANG L M， LU D， LIU Q，et al ．State of charge estimation for LiFePO₄ battery via dual extended Kalman filter and charging voltage curve［J］．Electrochimica Acta，2019，296：1009-1017.
7	罗琴琴，苏建徽，林志光，等．基于递推最小二乘法的虚拟同步发电机参数辨识方法［J］．电力系统自动化，2019，43（1）：215-221.
	LUO Qinqin， SU Jianhui， LIN Zhiguang，et al ．Parameter identification method for virtual synchronous generators based on recursive least square algorithm［J］．Automation of Electric Power Systems，2019，43（1）：215-221.
8	杜帮华，张宇，吴铁洲，等．梯次利用锂离子电池等效模型参数在线辨识方法［J］．储能科学与技术，2021，10（1）：342-348.
	DU Banghua， ZHANG Yu， WU Tiezhou，et al ．An online identification method for equivalent model parameters of aging lithium-ion batteries［J］．Energy Storage Science and Technology，2021，10（1）：342-348.
9	卫志农，原康康，成乐祥，等．基于多新息最小二乘算法的锂电池参数辨识［J］．电力系统自动化，2019，43（15）：139-145.
	WEI Zhinong， YUAN Kangkang， CHENG Lexiang，et al ．Parameter identification of lithium-ion battery based on multi-innovation least squares algorithm［J］．Automation of Electric Power Systems，2019，43（15）：139-145.
10	ZHAO B G， ZHANG X K， LIANG C L ．A novel parameter identification algorithm for 3-DOF ship maneuvering modelling using nonlinear multi-innovation［J］．Journal of Marine Science and Engineering，2022，10（5）：581.
11	魏金科，安小宇．多新息模型辨识PMSM双曲正切滑模定位控制［J］．机械设计与制造，2022（1）：164-167.
	WEI Jin-ke， AN Xiao-yu ．Hyperbolic-tangent sliding mode position control of multi-innovation model identification permanent magnet synchronous motor［J］．Machinery Design & Manufacture，2022（1）：164-167.
12	吴定会，张建宇，沈艳霞，等．基于多新息近似最小一乘算法PMSM参数辨识［J］．系统仿真学报，2018，30（3）：1001-1007.
	WU Dinghui， ZHANG Jianyu， SHEN Yanxia，et al ．Parameter identification for PMSM based on multi-innovation approximate least absolute deviation identification algorithm［J］．Journal of System Simulation， 2018，30（3）：1001-1007
13	谢朔，陈德山，初秀民，等．改进多新息卡尔曼滤波法辨识船舶响应模型［J］．哈尔滨工程大学学报，2018，39（2）：282-289.
	XIE Shuo， CHEN Deshan， CHU Xiumin，et al ．Identification of ship response model based on improved
	multi-innovation extended Kalman filter［J］．Journal of Harbin Engineering University，2018，39（2）：282-289.
14	GUO R H， SHEN W X ．A review of equivalent circuit model based online state of power estimation for lithium-ion batteries in electric vehicles［J］．Vehicles，2022，4（1）：1-29.
15	SU J， LIN M S， WANG S L，et al ．An equivalent circuit model analysis for the lithium-ion battery pack in pure electric vehicles［J］．Measurement & Control，2019，52（3/4）：193-201.
16	MENG J H， STROE D I， RICCO M，et al ．A novel multiple correction approach for fast open circuit voltage prediction of lithium-ion battery［J］．IEEE Transactions on Energy Conversion，2019，34（2）：1115-1123.
17	REN B Y， XIE C X， SUN X D，et al ．Parameter identification of a lithium-ion battery based on the improved recursive least square algorithm［J］．IET Power Electronics，2020，13（12）：2531-2537.
18	黄敬尧，李凌峰，张扬，等．基于FF-MILS和UKF算法的锂电池SOC估算［J］．电源技术，2021，45（6）：711-715，735.
	HUANG Jingyao， LI Lingfeng， ZHANG Yang，et al ．Estimation of state of charge for lithium-ion battery based on multi-innovation recursive least square algorithm and unscented Kalman filter［J］．Chinese Journal of Power Sources，2021，45（6）：711-715，735.
19	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（4）：505-513.
	SUN Jinlei， ZOU Xin， GU Haotian，et al ．State of charge estimation for lithium-ion battery based on FFRLS-EKF joint algorithm［J］．Automotive Engineering，2022，44（4）：505-513.
20	巫春玲，胡雯博，孟锦豪，等．基于最大相关熵扩展卡尔曼滤波算法的锂离子电池荷电状态估计［J］．电工技术学报，2021，36（24）：5165-5175.
	WU Chunling， HU Wenbo， MENG Jinhao，et al ．State of charge estimation of lithium-ion batteries based on maximum correlation-entropy criterion extended Kalman filtering algorithm［J］．Transactions of China Electrotechnical Society，2021，36（24）：5165-5175.
21	雷克兵，陈自强．基于改进多新息扩展卡尔曼滤波的电池SOC估计［J］．浙江大学学报（工学版）， 2021，55（10）：1978-1985，2001.
	LEI Ke-bing， CHEN Zi-qiang ．Estimation of state of charge of battery based on improved multi-innovation extended Kalman filter［J］．Journal of Zhejiang University （Engineering Science），2021，55（10）：1978-1985，2001.

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