基于改进无迹卡尔曼滤波的锂电池SOC在线估计

doi:10.3969/j.issn.1000-1158.2019.01.07

Abstract
Figure/Table
References (24)
Related Citation (15)

Download: PDF (1858 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract An improved unscented Kalman filter (IUKF) approach was proposed to enhance the accuracy and robustness of state of charge (SOC) online estimation, aimed at improving the drawbacks of unscented Kalman filter (UKF) which needs an accurate model and a priori noise statistics. Firstly, Li-ion battery modeling and offline parameters identification were realized. Secondly, sensitivity analysis experiment of the cell’s electrical model was designed to verify which model parameter has the most important influence on the SOC estimation accuracy, and provide the appropriate parameter for the model adaptive algorithm. Thirdly, IUKF approach composed of model adaptive algorithm and noise adaptive algorithm was introduced. Finally, this method was verified through physical experiment. The experimental results revealed that the proposed approach’s estimation error is less than 1.79% with acceptable robustness.

Key words： metrology state of charge estimation Li-ion battery unscented kalman filter model adaptive noise adaptive

Received: 17 July 2017 Published: 07 January 2019

PACS:

TB971

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	CHEN Ze-wang
	YANG Li-wen
	ZHAO Xiao-bing
	WANG You-ren

Cite this article:

CHEN Ze-wang,YANG Li-wen,ZHAO Xiao-bing, et al. Online Estimation of SOC for Li-ion Battery Based on An Improved Unscented Kalman Filters Approach[J]. Acta Metrologica Sinica, 2019, 40(1): 40-48.

URL:

http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2019.01.07 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2019/V40/I1/40

［1］魏克新, 陈峭岩. 基于自适应无迹卡尔曼滤波算法的锂离子动力电池状态估计［J］. 中国电机工程学报, 2014,34(3): 445-452.
Wei K X, Chen Q Y. States Estimation of Li-ion Power Batteries Based on Adaptive Unscented Kalman Filters［J］. Proceedings of the CSEE, 2014, 34(3): 445-452.
［2］Waag Wladislaw, Fleischer Christian, Uwe Dirk. Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles［J］. Journal of Power Sources, 2014, 258(23): 321-339.
［3］于海芳, 逯仁贵, 朱春波, 等. 基于安时法的镍氢电池SOC估计误差校正［J］. 电工技术学报, 2012, 27(6): 12-18.
Yu H F, Lu R G, Zhu C , et al.   State of Charge Estimation Calibration for Ni-MH Battery Based on Ampere-Hour Method［J］. Transactions of China Electrotechnical Society, 2012, 27(6): 12-18.
［4］王凤国, 张忠相, 欧阳松, 等. 基于 Kalman 算法的软测量技术在电池容量检测中的应用［J］. 计量学报, 2014, 35(2): 165-168.
Wang F , Zhang Z , Ouyang S, et al. Application Soft-sensing Technique Based on Kalman Algorithm for Battery Capacity Detection［J］. Acta Metrologica Sinica, 2014, 35(2): 165-168.
［5］廉春原, 李蓓, 蔡纪鹤. 基于固定电阻放电的蓄电池SOC在线估算方法［J］. 电源技术, 2015, 39(11): 2395-2399.
Lian C Y, Li B, Cai J H. New on-line estimation method of battery SOC based on discharge through fixed resistor［J］.   Chinese Journal of Power Sources, 2015, 39(11): 2395-2399.
［6］姜久春, 时玮, 张言茹, 等. 磷酸铁锂动力电池阻抗谱参数分析［J］. 北京理工大学学报, 2014, 34(5): 470-474.
Jiang J C, Shi W, Zhang Y R, et al. Impedance Spectra Investigation and Parameter Analysis of Traction LiFePO4 Battery［J］.   Transactions of Beijing Institute of Technology, 2014, 34(5): 470-474.
［7］刘征宇, 杨俊斌, 张庆, 等. 基于QPSO-BP神经网络的锂电池SOC预测［J］. 电子测量与仪器学报, 2013, 27(3): 224-228.
Liu Z Y, Yang J B, Zhang Q, et al. Estimation for SOC of lithium battery based on QPSO-BP neural network［J］. Journal of Electronic Measurement and Instrument, 2013, 27(3): 224-228.
［8］彭志远, 杨亚联, 秦大同, 等. 一种估计混合动力用Ni-MH电池单体SOC的方法［J］. 重庆大学学报, 2011, 34(5): 19-25.
Peng Z Y, Yang Y L, Qin D T, et al. A method for calculating SOC of single Ni-MH battery used in hybrid electric vehicles［J］.   JOURNAL OF CHONGQING UNIVERSITY, 2011, 34(5): 19-25.
［9］Burgos C, Sáez D, Orchard ME, et al. Fuzzy modelling for the state-of-charge estimation of lead-acid batteries［J］. Journal of Power Sources, 2015, 274:355-366.
［10］Hu J N, Hu J J, Lin H B, et al. State-of-charge estimation for battery management system using optimized support vector machine for regression［J］. Journal of Power Sources, 2014, 269(3): 682-693.
［11］袁学庆, 张阳, 赵林,等. 基于EKF的锂电池SOC估算与试验研究［J］. 电源技术, 2015, 39(12):2587-2589.
Yuan X Q, Zhang Y, Zhao L, et al. Li-ion batterySOC estimation and test research based on EKF［J］. Chinese Journal of Power Sources, 2015,39(12):2587-2589.
［12］吴铁洲, 罗蒙, 张琪,等. 基于迭代扩展卡尔曼滤波算法的电池SOC估算［J］. 电力电子技术, 2016, 50(5):95-98.
Wu T Z, Luo M, Zhang Q, et al. Battery SOC Estimation Based on Iterative Extended Kalman Filter Algorithm［J］. Power Electronics, 2016, 50(5):95-98.
［13］张金龙, 魏艳君, 李向丽, 等. 基于模型参数在线辨识的蓄电池SOC估算［J］. 电工技术学报, 2014, 29(S1): 23-28.
Zhang J L, Wei Y J, Li X L, et al. Battery SOC Estimation Based on Online Parameter Identification［J］. Transactions of China Electrotechnical Society, 2014, 29(S1): 23-28.
［14］程泽, 吕继考, 刘继光, 等. 等效滞回模型在锂离子电池SOC估计中的应用［J］. 湖南大学学报(自然科学版), 2015, 42(4): 63-70.
Cheng Z, Lv J K, Liu J G, et al. Application of Equivalent Hysteresis Model in Estimaion of State of Charge of Lithium-ion Battery［J］. Journal of Hunan University(Natural Sciences), 2015, 42(4): 63-70.
［15］赵又群, 周晓凤, 刘英杰. 基于扩展卡尔曼粒子滤波算法的锂电池SOC估计［J］. 中国机械工程, 2015, 26(3): 394-397.
Zhao Y Q, Zhou X F, Liu Y J.   SOC Estimation for Li-Ion Battery Based on Extended Kalman Particle Filter［J］.   China Mechanical Engineering, 2015, 26(3): 39.
［16］孙冬, 陈息坤. 基于离散滑模观测器的锂电池荷电状态估计［J］. 中国电机工程学报, 2015, 35(1):185-191.
Sun D, Chen X K. Charge State Estimation of Li-ion Batteries Based on Discrete-time Sliding Mode Observers［J］.   Proceedings of the CSEE, 2015, 35(1):185-191.
［17］何朕, 王广雄. 基于广义系统观测器的电池荷电状态估计［J］. 电机与控制学报, 2016, 20(1):94-98.
He Z, Wang G X.   Descriptor system observer-based state-of-charge estimation for batteries［J］. Electric Machines and Control,2016, 20(1):94-98.
［18］张利, 刘帅帅, 刘征宇,等. 锂离子电池自适应参数辨识与SoC估算研究［J］. 电子测量与仪器学报, 2016, 30(1):45-51.
Zhang L,Liu S S,Liu Z Y, et al. Research on adaptive parameter identification and SoC estimation for Lithium-ion battery［J］. Journal of Electronic Measurement and Instrumentation, 2016, 30(1):45-51.
［19］Sun F, Xiong R. A novel dual-scale cell state-of-charge estimation approach for series-connected battery pack used in electric vehicles［J］. Journal of Power Sources, 2015, 274: 582-594.
［20］赵天意, 彭喜元, 彭宇,等. 改进卡尔曼滤波的融合型锂离子电池SOC估计方法［J］. 仪器仪表学报, 2016, 37(7):1441-1448.
Zhao T Y, Peng X Y, Peng Y, et al. Lithium-ion battery SOC estimation method with fusion improved Kalman filter algorithm［J］.   Chinese Journal of Scientific Instrument, 2016, 37(7):1441-1448.
［21］刘和平, 许巧巧, 胡银全, 等. 自适应卡尔曼滤波法磷酸铁锂动力电池剩余容量估计［J］. 重庆大学学报, 2014, 37(1): 68-74.
Liu H P, Xu Q Q, Hu Y Q, et al. State of charge estimation of lithium iron phosphate batteries based on adaptive Kalman filters［J］.   JOURNAL OF CHONGQING UNIVERSITY, 2014, 37(1): 68-74.
［22］杨海学, 张继业, 张晗. 基于改进Sage-Husa的自适应无迹卡尔曼滤波的锂离子电池SOC估计［J］. 电工电能新技术, 2016, 35(1):30-35.
Yang H X, Zhang J Y, Zhang H. States of charge estimation of lithium-ion battery based on improved Sage-Husa adaptive unscented Ka［J］.   Advanced Technology of Electrical Engineering and Energy, 2016, 35(1):30-35.
［23］顾亚雄, 张友近. 基于双卡尔曼滤波的锂电池SOC估算［J］. 电源技术, 2016, 40(5):986-989.
Gu Y X, Zhang Y J. Estimation of state of charge of lithium-ion battery based on dual extend Kalman filter［J］. Chinese Journal of Power Sources, 2016, 40(5):986-989.
［24］Environmental Idaho National Engineering and Laboratory. FreedomCAR Battery Test Manual For Power-Assist Hybrid Electric Vehicles［Z］. 2003.