基于FFRLS-EKF联合算法的锂离子电池荷电状态估计方法

doi:10.19562/j.chinasae.qcgc.2022.04.006

Abstract

Abstract:

In view of the problem of a bit too large error of battery SOC estimation due to the existing SOC estimation method based on the battery model with constant parameters ignores the effects of working conditions and battery model parameters， a SOC joint-estimation method for lithium-ion batteries combining the forgetting factor least squares algorithm （FFRLS） and the extended Kalman filter （EKF） algorithm is proposed in this paper. The method utilizes the FFRLS algorithm to online identify the parameters of battery equivalent circuit model and correct the battery model in real-time first. Then， the EKF algorithm and real-time corrected battery model are used to estimate battery SOC. The experimental results show that the SOC estimation method proposed can effectively reduce the SOC estimation error caused by the variation of battery model parameters. In the experiments of pulse discharging test， pulse charging test and dynamic stress test， the final maximum errors of SOC estimation are 1.01%， 0.87% and 1.59%， respectively.

Key words: SOC estimation, parameter identification, RLS, EKF

Jinlei Sun,Xin Zou,Haotian Gu,Kai Cui,Jinda Zhu. State of Charge Estimation for Lithium-ion Battery Based on FFRLS-EKF Joint Algorithm[J].Automotive Engineering, 2022, 44(4): 505-513.

Figures/Tables 11

电路频域方程	$U b s = R 0 I b s + U 1 s + U O C (s) (1 + τ 1 s) U 1 s = R 1 I b s ?????????????????$
连续传递函数	$U s I b s = R 0 + R 1 1 + τ 1 s$
脉冲响应不变法	$1 s + a ? → 1 z - e - a T s$
离散传递函数	$U (z) I b (z) = a 2 + a 3 z - 1 1 - a 1 z - 1$
差分方程	$U (k)$ = $a 1 U (k - 1)$ + $a 2 I b k + a 3 I b k - 1$
RLSCE	$y k = h (k) T θ ??????????????????????????????????????????? h (k) = U k - 1, I b k, I b k - 1 T θ = a 1, a 2, a 3 T ????????????????????????????????????????$
待辨识参数	$a 1 = e - T s τ 1 ?????????? a 2 = R 0 + 1 C 1 a 3 = - R 0 e - T s τ 1$
电路参数解析式	$R 0 = - a 3 a 1 ????????? C 1 = 1 a 2 - R 0 ?? R 1 = - T s C 1 l n ? a 1$

测试实验	$A E m a x$ /mV	$R E m a x$ /%	RMSE/mV
脉冲放电实验	88.628	2.77	1.631
脉冲充电实验	19.971	0.58	0.765
DST实验	106.367	3.23	2.429

测试实验	初始误差	全时间尺度 $A E m a x$	收敛后 $A E m a x$
脉冲放电	5.00%	6.00%	1.01%
脉冲充电	5.00%	4.99%	0.87%
DST	10.00%	11.00%	1.59%

References 18

1	郭向伟. 电动汽车电池荷电状态估计及均衡技术研究［D］. 广州：华南理工大学， 2016.
	GUO X W. Study on state of charge estimation and equalization technique of electric vehicle battery［D］. Guangzhou： South China University of Technology， 2016.
2	黄煜，朱立宗．电池SOC估算方法的研究现状［J］．时代汽车，2021（8）：99-100．
	HUANG Y， ZHU L Z. Research status of battery SOC estimation methods［J］. Auto Time， 2021（8）： 99-100.
3	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（5）：376-378．
	LIN C T， WANG J P， CHEN Q S. Methods for state of charge estimation of EV batteries and their application［J］. Battery Bimonthly， 2004（5）： 376-378.
4	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 W H， HAN X D， CHEN Q S， et al. A study on SOC estimation algorithm and battery management system for electric vehicle［J］. Automotive Engineering， 2007， 29（3）： 198-202.
5	OUYANG Q， MA R， WU Z X. Adaptive square-root unscented Kalman filter based state of charge estimation for lithium-ion batteries with model parameter online identification［J］. Energies， 2020， 13（18）.
6	刘艳莉，戴胜，程泽，等. 基于有限差分扩展卡尔曼滤波的锂离子电池SOC估计［J］．电工技术学报，2014，29（1）：221-228．
	LIU Y L， DAI S， CHENG Z， et al. Estimation of state of charge of Lithium-ion battery based on finite difference extended Kalman Filter［J］. Transactions of China Electrotechnical Society， 2014， 29（1）： 221-228.
7	刘艳. 锂离子电池SOC参数估算的研究［D］. 天津：天津大学， 2014.
	LIU Y. Research on the SOC parameter estimation of Li-ion battery［D］. Tianjin： Tianjin University， 2014.
8	王志福．基于BCRLS_AEKF的锂离子电池荷电状态估计及硬件在环验证［J］．北京理工大学学报，2020， 40（3）：275-281.
	WANG Z F. State of charge estimation and hardware-in-loop verification of Lithium-ion battery based on BCRLS-AEKF［J］. Transactions of Beijing Institute of Technology， 2020， 40（3）： 275-281.
9	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 B H， ZHANG Y， WU T Z， 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.
10	PEDRO F， PAUL P， PEDRO B. On-line parameter estimation of a lithium-ion battery/supercapacitor storage system using filtering sliding mode differentiators［J］. Journal of Energy Storage， 2020， 32： 101889.
11	FENG F， LU R G， WEI G， et al. Identification and analysis of model parameters used for LiFePO4 cells series battery pack at various ambient temperature［J］. IET Electrical Systems in Transportation， 2016， 6（2）： 50-55.
12	DANIJEL P， KRZNAR M， KOMLJENOVIC A， et al. Dual EKF-based state and parameter estimator for a LiFePO4 battery cell［J］. Journal of Power Electronics， 2017， 17（2）： 398-410.
13	陈媛，何怡刚，李忠. 电池变温度模型似然函数参数辨识及SOC估计［J］. 电子测量与仪器学报， 2019， 33（12）： 1-9.
	CHEN Y， HE Y G， LI Z. Battery variable temperature model parameter identification by likelihood estimation and SOC estimation［J］. Journal of Electronic Measurement and Instrumentation， 2019， 33（12）： 1-9.
14	BAIRWA B L， PAREEK K， HAMPANNAVAR S K．Investigation on lithium ion battery equivalent circuit models for dynamic load profiles［J］. Energy Storage， 2021， 3（2）．
15	陈息坤，孙冬．锂离子电池建模及其参数辨识方法研究［J］．中国电机工程学报， 2016， 36（22）： 6254-6261．
	CHEN X K， SUN D. Research on lithium-ion battery modeling and model parameter identification methods［J］. Proceedings of the CSEE， 2016， 36（22）： 6254-6261.
16	熊瑞，何洪文，丁银．HEV用锂离子电池动态模型参数辨识方法研究［J］. 电力电子技术， 2011， 45（4）：100-102.
	XIONG R， HE H W， DING Y. Study on identification approach of dynamic model parameters for lithium-ion batteries used in hybrid electric vehicle［J］. Power Electronics， 2011， 45（4）： 100-102.
17	费亚龙，谢长君，汤泽波，等．基于平方根无迹卡尔曼滤波的锂电池状态估计［J］. 中国电机工程学报， 2017， 37（15）： 4514-4520
	FEI Y L， XIE C J， TANG Z B， et al. State of charge estimation based on square root unscented Kalman filter algorithm for Li-ion batteries［J］. Proceeding of the CSEE， 2017， 37（15）： 4514-4520.
18	熊瑞．基于数据模型融合的电动车辆动力电池组状态估计研究［D］．北京：北京理工大学，2014．
	XIONG R. Estimation of battery pack state for electric vehicles using model-data fusion approach［D］. Beijing： Beijing Institute of Technology， 2014.

[1]	XU Cheng-Shan, JIANG Fa-Chao, SONG Sen-Nan, TIAN Guang-Yu. [J]. , 2017, 39(1): 9 -14 .
[2]	ZHANG Yi-Bing, 吕Jian-Jun , YUAN Guang-Hui. [J]. , 2016, 38(12): 1467 -1470 .
[3]	JIA Ji-De, WU Chun-Zhi, JIA Xiang-Yu, REN Gang, HAN Jia-Jia. [J]. , 2017, 39(1): 97 -101 .
[4]	RONG Bing, XIAO Pan, ZHOU Jian-Wen, LIU Yi-Lu. [J]. , 2017, 39(2): 214 -219 .
[5]	WANG Zhen, CUI Ya-Hui-.Liu-Kai, XU Lin. [J]. , 2017, 39(3): 304 -310 .
[6]	GUAN Xin, XU Liang, GUO Rui, WANG Bo, SONG Hai-Yan. [J]. , 2017, 39(3): 311 -316 .
[7]	JIN Geng-Ri, GUAN Xin, ZHAN Jun, GUO Rui. [J]. , 2017, 39(3): 364 -368 .
[8]	XIONG Xing-Wang, GAO Jun-Hua, ZHOU Tao, YU Jin-Tao. [J]. , 2017, 39(4): 381 -385 .
[9]	CAO Li-Feng, ZHOU Shou-Qin, XIE Xiao-Peng. [J]. , 2017, 39(4): 401 -406 .
[10]	Liu Zhiqiang & Pu Xian. An Experimental Study on Anti-lock Braking of Continuous Regenerative Braking System in Electric Vehicles[J]. , 2018, 40(7): 804 .

参数	数值
额定容量	2.2 A·h
标称电压	3.6 V
上限截止电压	4.2 V
下限截止电压	2.75 V
最大充电电流	1C（2.2 A）
最大放电电流	5C（11 A）

测试实验	EKF	AEKF	FFRLS-EKF
脉冲放电	1.77%	1.68%	0.36%
脉冲充电	2.15%	1.80%	0.69%
DST	1.60%	2.64%	0.23%

State of Charge Estimation for Lithium-ion Battery Based on FFRLS-EKF Joint Algorithm

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 11

References 18

Related Articles 10

Metrics

Comments

Recommended 10

[1]	Junfeng Wang,Jiqing Chen,Fengchong Lan,Qingshan Liu,Changjing Zeng. Global Sensitivity Analysis of Multi-scale Parameters of the Fuel Cell Model Based on Two Cost Functions [J]. Automotive Engineering, 2023, 45(3): 393-401.
[2]	Cheng Lin,Jiang Yi,Yu Tian. State Estimation and Parameter Identification of Shifting Actuator of Automatic Transmission of Electric Vehicles [J]. Automotive Engineering, 2022, 44(8): 1237-1250.
[3]	Yunfei Zha,Lü Xiaolong,Xinye Liu,Fangwu Ma. Fusion Estimation of Vehicle State Parameters Based on Dichotomy [J]. Automotive Engineering, 2022, 44(12): 1910-1918.
[4]	Xin Lai,Yunfei Li,Yuejiu Zheng,Jingjing Wang,Tao Zhou Long Sun. An Overall Estimation of State⁃of⁃Charge Based on SOC⁃OCV Optimization Curve and EKF for Lithium⁃ion Battery [J]. Automotive Engineering, 2021, 43(1): 19-26.
[5]	Ranjun, Zhou Wei, Wang Xu. An Improved Hierarchical Online Joint Estimation Method for Impedance Parameters and State of Charge of Traction BatteryHuang [J]. Automotive Engineering, 2020, 42(8): 1000-1007.
[6]	Jin Bowen, Qiao Huimin, Pan Tianhong, Chen Shan. Lithium Battery SOC Estimation Based on Internal Resistance Power Consumption [J]. Automotive Engineering, 2020, 42(8): 1008-1015.
[7]	Luo Yong, Qi Pengwei, Huang Huan, Wang Jianan, Wang Yi, Li Peiran. Study on Battery SOC Estimation by Ampere-hour Integral Method with Capacity Correction [J]. Automotive Engineering, 2020, 42(5): 681-687.
[8]	Zhu Bing, Zhang Peixing, Wang Zhen, Zhao Jian, Wu Jian & Feng Yao. Modeling and Control of Active Pneumatic Braking System for Tractor-Semitrailer Combination [J]. Automotive Engineering, 2019, 41(9): 1050-1055.
[9]	Gao Qi, Feng Jinzhi, Zheng Songlin, Lin Yang. A Study on Higher-order Fractional Derivative Dynamic Model of Rubber Bushing [J]. Automotive Engineering, 2019, 41(8): 872-879.
[10]	Luo Yong, Qi Pengwei, Kan Yingzhe, Li Peiran, Liu Li, Cui Huanyu. Parameter Identification of Lithium Battery Model Based on Simulated Annealing Algorithm [J]. , 2018, 40(12): 1418-1425.