双自适应衰减卡尔曼滤波锂电池荷电状态估计
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摘要
针对卡尔曼滤波法在锂离子电池荷电状态(SOC)估计时存在误差较大、收敛较慢等问题,提出了一种双自适应衰减扩展卡尔曼滤波荷电状态估计(DAFEKF)算法。该算法首先设计了针对动力电池的荷电状态估计观测器,利用测得的电流和电压值分别作为观测器的输入和观测值,结合双自适应衰减扩展卡尔曼滤波估计出观测器中的电池荷电状态,在卡尔曼滤波算法的基础上加入时变衰减因子来减弱过去数据对当前滤波值的影响,并自适应地调整卡尔曼算法中过程噪声和测量噪声协方差。利用DAFEKF算法估计出的SOC结果与扩展卡尔曼滤波(EKF)和自适应扩展卡尔曼滤波(AEKF)算法进行了比较,结果表明,DAFEKF方法具有较好的准确性、鲁棒性和收敛性,使SOC估计误差控制在2%以内。
A dual adaptive fading extended Kalman filter(DAFEKF)algorithm is proposed for the problem of low accuracy and convergent speed of state-of-charge(SOC)estimation.The algorithm designs an observer of state-of-charge for the power battery,and the measured current and voltage are taken as input and observation values of the observer,respectively.Then the state of charge of a battery is estimated by the DAFEKF.The DAFEKF bases on the Kalman algorithm,and adds the time-varying fading factor to reduce the influence of past data on current filtering values and to adaptively adjust the covariances of the process noise and measurement noise.SOC results of a lithium battery obtained using the proposed DAFEKF are compared with those obtained using the extended Kalman filter(EKF)and the adaptive extended Kalman filter(AEKF),and the comparison shows that the DAFEKF method provides better accuracy,robustness and convergence,and the SOC error of the proposed method is less than 2%.
A dual adaptive fading extended Kalman filter(DAFEKF)algorithm is proposed for the problem of low accuracy and convergent speed of state-of-charge(SOC)estimation.The algorithm designs an observer of state-of-charge for the power battery,and the measured current and voltage are taken as input and observation values of the observer,respectively.Then the state of charge of a battery is estimated by the DAFEKF.The DAFEKF bases on the Kalman algorithm,and adds the time-varying fading factor to reduce the influence of past data on current filtering values and to adaptively adjust the covariances of the process noise and measurement noise.SOC results of a lithium battery obtained using the proposed DAFEKF are compared with those obtained using the extended Kalman filter(EKF)and the adaptive extended Kalman filter(AEKF),and the comparison shows that the DAFEKF method provides better accuracy,robustness and convergence,and the SOC error of the proposed method is less than 2%.
引文
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[11]熊瑞,孙逢春,何洪文.自适应卡尔曼滤波器在车用锂离子动力电池SOC估计上的应用[J].高技术通讯,2012,22(2):198-204.XIONG Rui,SUN Fengchun,HE Hongwen.State-ofcharge estimation of lithium-ion batteries in electric vehicles based on an adaptive extended Kalman filter[J].Chinese High Technology Letters,2012,22(2):198-204.
[12]XIONG Rui,HE Hongwen,SUN Fengchun,et al.Evaluation on state of charge estimation of batteries with adaptive extended Kalman filter by experiment approach[J].IEEE Transactions on Vehicular Technology,2013,62(1):108-117.
[13]YAO L W,AZIZ J A,IDRIS N R,et al.Online battery modeling for state-of-charge estimation using extended Kalman filter with Busse’s adaptive rule[C]∥Proceedings of 41st Annual Conference of the IEEE Industrial Electronics Society.Piscataway,NJ,USA:IEEE,2015:4742-4747.
[14]戴海峰,魏学哲,孙泽昌.基于等效电路的内阻自适应锂离子电池模型[J].同济大学学报(自然科学版),2010,38(1):98-102.DAI Haifeng,WEI Xuezhe,SUN Zechang.An inner resistance adaptive model based on equivalent circuit of lithium-ion batteries[J].Journal of Tongji University(Natural Science),2010,38(1):98-102.
[15]RAHIMI-EICHI H,CHOW M Y.Adaptive parameter identification and state-of-charge estimation of lithium-ion batteries[C]∥Proceedings of Conference on IEEE Industrial Electronics Society.Piscataway,NJ,USA:IEEE,2012:4012-4017.
[16]PALEOLOGU C,BENESTY J,CIOCHIN S.A robust variable forgetting factor recursive least-squares algorithm for system identification[J].IEEE Signal Processing Letters,2008,15:597-600.
[17]RIJANTO E,ROZAQI L,NUGROHO A,et al.RLS with optimum multiple adaptive forgetting factors for SOC and SOH estimation of li-ion battery[C]∥Proceedings of the 2017 5th International Conference on Instrumentation,Control,and Automation.Piscataway,NJ,USA:IEEE,2017:73-77.
[18]KAZEMI M,AREFI M M.A fast iterative recursive least squares algorithm for Wiener model identification of highly nonlinear systems[J].ISA Transactions,2017,67:382-388.
[2]王霄,徐俊,曹秉刚,等.小波降噪卡尔曼滤波锂电池荷电状态估计[J].西安交通大学学报,2017,51(10):71-76.WANG Xiao,XU Jun,CAO Binggang,et al.A Kalman filter SOC estimation method for lithium-ion batteries based on discrete wavelet transform denoising[J].Journal of Xi’an Jiaotong University,2017,51(10):71-76.
[3] NG K S,MOO C S,CHEN Yiping,et al.Enhanced coulomb counting method for estimating state-ofcharge and state-of-health of lithium-ion batteries[J].Applied Energy,2009,86(9):1506-1511.
[4] LEE S,KIM J,LEE J,et al.State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge[J].Journal of Power Sources,2008,185(2):1367-1373.
[5] HU Xiaosong,SUN Fengchun,ZOU Yuan.Estimation of state of charge of a lithium-ion battery pack for electric vehicles using an adaptive Luenberger observer[J].Energies,2010,3(9):1586-1603.
[6] XU Jun,CAO Binggang,CAO Junyi,et al.A comparison study of the model based SOC estimation methods for lithium-ion batteries[C]∥Proceedings of 20139th IEEE Vehicle Power and Propulsion Conference.Piscataway,NJ,USA:IEEE,2013:1-3.
[7] XU Jun,MI C C,CAO Binggang,et al.The state of charge estimation of lithium-ion batteries based on a proportional-integral observer[J].IEEE Transactions on Vehicular Technology,2014,63(4):1614-1621.
[8]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 Youqun,ZHOU Xiaofeng,LIU Yingjie.SOC estimation for li-ion battery based on extended Kalman particle filter[J].China Mechanical Engineering,2015,26(3):394-397.
[9] HE Hongwen,XIONG Rui,ZHANG Xiaowei,et al.State-of-charge estimation of the lithium-ion battery using an adaptive extended Kalman filter based on an improved Thevenin model[J].IEEE Transactions on Vehicular Technology,2011,60(4):1461-1469.
[10]SUN Fengchun,HU Xiaosong,ZHOU Yuan,et al.Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles[J].Energy,2011,36(5):3531-3540.
[11]熊瑞,孙逢春,何洪文.自适应卡尔曼滤波器在车用锂离子动力电池SOC估计上的应用[J].高技术通讯,2012,22(2):198-204.XIONG Rui,SUN Fengchun,HE Hongwen.State-ofcharge estimation of lithium-ion batteries in electric vehicles based on an adaptive extended Kalman filter[J].Chinese High Technology Letters,2012,22(2):198-204.
[12]XIONG Rui,HE Hongwen,SUN Fengchun,et al.Evaluation on state of charge estimation of batteries with adaptive extended Kalman filter by experiment approach[J].IEEE Transactions on Vehicular Technology,2013,62(1):108-117.
[13]YAO L W,AZIZ J A,IDRIS N R,et al.Online battery modeling for state-of-charge estimation using extended Kalman filter with Busse’s adaptive rule[C]∥Proceedings of 41st Annual Conference of the IEEE Industrial Electronics Society.Piscataway,NJ,USA:IEEE,2015:4742-4747.
[14]戴海峰,魏学哲,孙泽昌.基于等效电路的内阻自适应锂离子电池模型[J].同济大学学报(自然科学版),2010,38(1):98-102.DAI Haifeng,WEI Xuezhe,SUN Zechang.An inner resistance adaptive model based on equivalent circuit of lithium-ion batteries[J].Journal of Tongji University(Natural Science),2010,38(1):98-102.
[15]RAHIMI-EICHI H,CHOW M Y.Adaptive parameter identification and state-of-charge estimation of lithium-ion batteries[C]∥Proceedings of Conference on IEEE Industrial Electronics Society.Piscataway,NJ,USA:IEEE,2012:4012-4017.
[16]PALEOLOGU C,BENESTY J,CIOCHIN S.A robust variable forgetting factor recursive least-squares algorithm for system identification[J].IEEE Signal Processing Letters,2008,15:597-600.
[17]RIJANTO E,ROZAQI L,NUGROHO A,et al.RLS with optimum multiple adaptive forgetting factors for SOC and SOH estimation of li-ion battery[C]∥Proceedings of the 2017 5th International Conference on Instrumentation,Control,and Automation.Piscataway,NJ,USA:IEEE,2017:73-77.
[18]KAZEMI M,AREFI M M.A fast iterative recursive least squares algorithm for Wiener model identification of highly nonlinear systems[J].ISA Transactions,2017,67:382-388.