Modelingand State of Charge Robust Estimation for Lithium-ion Batteries
Chen Xikun1,Sun Dong1,2,Chen Xiaohu1
1.School of Mechatronics Engineering and Automation,Shanghai University Shanghai 200072 China; 2.Zhengzhou University of Light Industry Zhengzhou 450002 China
Abstract:In lithium-ion battery power management system,the dynamic modeling and the estimation of state of charge (SOC) are the key techniques.The battery working states are affected by external environment factors and load changes.So the variable forgetting factorleast squares method is used to identify the model parameters based on the second-order RC equivalent circuit model.For the uncertainty noise problem in actual applications,the SOC robust estimation method is proposed based on the discrete-time H-infinity filter.The experiments are carried to compare the suggested method with the commonly used extended Kalman filter.The results show that the performance of the second-order RC model can be improved by the variable forgetting factor least squares method; the robust estimation method can be used to compute battery SOC accurately with 3% error,and the proposed algorithm has better robustness than the extended Kalman filter.
[1] Rahimi-Eichi H,Ojha U,Baronti F,et al.Battery management system:an overview of its application in the smart grid and electric vehicles[J].IEEE Industrial Electronics Magazine,2013,7(2):4-16. [2] Rezvanizaniani S M,Liu Z C,Chen Y,et al.Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle (EV) safety and mobility[J].Journal of Power Sources,2014,256:110-124. [3] Seaman A,Dao T S,Mcphee J.A survey of mathematics-based equivalent circuit and electrochemical battery models for hybrid and electric vehicle simulation[J].Journal of Power Sources,2014,256:410-423. [4] Hu Y,Yurkovich S,Guezennec Y,et al.A technique for dynamic battery model identication in automotive applications using linear parameter varying structures[J].Control Engineering Practice,2009,17(10):1190-1201. [5] Sun Fengchun,Xiong Rui,He Hongwen,et al.Model-based dynamic multi-parameter method for peak power estimation of lithium-ion batteries[J].Applied Energy,2012,96:378-386. [6] Freedom CAR battery test manual,Revision 3[R].United States Idaho National Engineering and Environmental Laboratory,USA,2003:D1-D21. [7] 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. [8] 雷肖,陈清泉,刘开培,等.电动车蓄电池荷电状态估计的神经网络方法[J].电工技术学报,2007,22(8):155-160. Lei Xiao,Chen Qingquan,Liu Kaipei,et al.Battery state of charge estimation based on neural-network for electric vehicles[J].Transactions of China Electrotechnical Society,2007,22(8):155-160. [9] Plett G L.Extended Kalman ltering for battery management systems of LiPB-based HEV battery packs part 2:modeling and identication[J].Journal of Power Sources,2004,134(2):262-276. [10]许爽,孙冬,柳钦煌.一种锂电池组无损均衡管理系统设计[J].电子器件,2014,37(4):799-802. Xu Shuang,Sun Dong,Liu Qinhuang.A novel scheme of non-dissipative equalization management system for lithium battery pack[J].Chinese Journal of Electron Devices,2014,37(4):799-802. [11]高明煜,何志伟,徐杰.基于采样点卡尔曼滤波的动力电池 SOC 估计[J].电工技术学报,2011,26(11):161-167. Gao Mingyu,He Zhiwei,Xu Jie.Sigma point Kalman filter based SOC estimation for power supply battery[J].Transactions of China Electrotechnical Society,2011,26(11):161-167. [12]Hu X S,Li S B,Peng H.A comparative study of equivalent circuit models for Li-ion batteries[J].Journal of Power Sources,2012,198:359-367. [13]Li Yong,Wang Lifang,Liao Chenglin,et al.Recursive modeling and online identification of lithium-ion batteries for electric vehicle applications[J].Science China Technological Sciences,2014,57(2):403-413. [14]Yuan Shifei,Wu Hongjie,Yin Chengliang.State of charge estimation using the extended Kalman filter for battery management systems based on the ARX battery model[J].Energies,2013,6(1):444-470. [15]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 Dong,Chen Xikun.Charge state estimation of Li-ion batteries based on discrete-time sliding mode observers[J].Proceedings of the CSEE,2015,35(1):185-191. [16]Yan Jingyu,Xu Guoqing,Qian Huihuan,et al.Robust state of charge estimation for hybrid electric vehicles:framework and algorithms[J].Energies,2010,3(10):1654-1672. [17]Simon D.Optimal State Estimation:Kalman,H∞,and Nonlinear Approaches[M].USA:John Wiley & Sons,2006:333-388.