基于变参数模型的锂电池荷电状态观测方法（英文）
|
摘要
锂电池荷电状态(SOC)观测技术作为电池管理系统(BMS)的关键技术,在维持电池系统设备安全高效运作、延长电池组整体生命周期等方面均起着不可或缺的作用.本文以改善锂电池荷电状态的观测结果为目的,对锂离子电池荷电状态的观测方法进行了研究,基于二阶变参数锂电池模型,设计了一种有效的改善SOC观测精度的方法.首先,根据SOC的定义,建立了安时积分估计(AH),通过引入二阶变参数锂电池模型建立扩展卡尔曼滤波估计器(EKF),然后结合Takagi-Sugeno模糊模型原理,设计Takagi-Sugeno和EKF联合估计器(TS–EKF).最后,在Simulink仿真平台上验证了SOC观测方法的准确性和实用性.结果表明,本文所设计的Takagi-Sugeno和EKF联合估计器可以改善SOC观测精度.
The observation technology of the battery state-of-charge(SOC) plays an indispensable role in maintaining the safety and high efficiency of the battery manage system(BMS) and prolonging the battery's life period etc.. In this paper, the observation method of the Li-ion battery's SOC is carried on aiming at improving the SOC observation results,an accurate and efficient SOC observation method is designed for Li-ion battery based on a 2 nd-order resistor-capacitor(RC) model with variable parameters. Firstly, the Amper-hour(AH) integral estimator is built according to the definition of SOC, and then the extended Kalman filter(EKF) estimator is established by introducing the EKF principle; Then, combined with the Takagi-Sugeno(TS) fuzzy principle, the Takagi-Sugeno and extended Kalman filter union estimator(TS–EKF)union estimator is eventually designed. Finally, the accuracy and the practicability of the SOC observation method with the core technologies are verified based on the simulation platform of Simulink. The results show that TS–EKF union estimator can improve the observation accuracy of SOC.
The observation technology of the battery state-of-charge(SOC) plays an indispensable role in maintaining the safety and high efficiency of the battery manage system(BMS) and prolonging the battery's life period etc.. In this paper, the observation method of the Li-ion battery's SOC is carried on aiming at improving the SOC observation results,an accurate and efficient SOC observation method is designed for Li-ion battery based on a 2 nd-order resistor-capacitor(RC) model with variable parameters. Firstly, the Amper-hour(AH) integral estimator is built according to the definition of SOC, and then the extended Kalman filter(EKF) estimator is established by introducing the EKF principle; Then, combined with the Takagi-Sugeno(TS) fuzzy principle, the Takagi-Sugeno and extended Kalman filter union estimator(TS–EKF)union estimator is eventually designed. Finally, the accuracy and the practicability of the SOC observation method with the core technologies are verified based on the simulation platform of Simulink. The results show that TS–EKF union estimator can improve the observation accuracy of SOC.
引文
[1]BALL R J,KURIAN R,EVANS R,et al.Failure mechanisms in valve regulated lead/acid batteries for cyclic applications.Journal of Power Sources,2002,109(1):189-202.
[2]GUO Y,TANG S,MENG G,et al.Failure modes of valve-regulated lead-acid batteries for electric bicycle applications in deep discharge.Journal of Power Sources,2009,191(1):127-133.
[3]DUBARRY M,SVOBODA V,HWU R,et al.Capacity loss in rechargeable lithium cells during cycle life testing:The importance of determining state-of-charge.Journal of Power Sources,2007,174(2):1121-1125.
[4]EINHORNE M,CONTE F V,KRAL C,et al.A method for online capacity estimation of lithium ion battery cells using the state of charge and the transferred charge.IEEE Transactions on Industry Applications,2012,48(2):736-741.
[5]CUADRAS A,KANOUN O.SoC Li-ion battery monitoring with impedance spectroscopy.International Multi-conference on Systems.Djerba,Tunisia:IEEE,2009:1-5.
[6]NG K S,MOO C S,CHEN Y P,et al.Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithiumion batteries.Applied Energy,2009,86(9):1506-1511.
[7]WAAG W,SAUER D U.Adaptive estimation of the electromotive force of the lithium-ion battery after current interruption for an accurate state-of-charge and capacity determination.Applied Energy,2013,111:416-427.
[8]YANG N,ZHANG X,LI G.State of charge estimation for pulse discharge of a LiFePO4 battery by a revised AH counting.Electrochimica Acta,2015,151:63-71.
[9]ALVAREZ ANTON J C,GARCIA NIETO P J,BLANCO VIEJOC,et al.Support vector machines used to estimate the battery state of charge.IEEE Transactions on Power Electronics,2013,28(12):5919-5926.
[10]DAI H,GUO P,WEI X,et al.ANFIS(adaptive neuro-fuzzy inference system)based online SOC(state of charge)correction considering cell divergence for the EV(electric vehicle)traction batteries.Energy,2015,80:350-360.
[11]DU J,LIU Z,WANG Y.State of charge estimation for Li-ion battery based on model from extreme learning machine.Control Engineering Practice,2014,26(1):11-19.
[12]SHENG H,XIAO J.Electric vehicle state of charge estimation:Nonlinear correlation and fuzzy support vector machine.Journal of Power Sources,2015,281:131-137.
[13]ZHOU F,WANG L,LIN H,et al.High accuracy state-of-charge online estimation of EV/HEV lithium batteries based on adaptive wavelet neural network.Proceedings of 2013 ECCE Asia Downunder(ECCE Asia).Melbourne,VIC,Australia:IEEE,2013:513-517.
[14]DAI H,WEI X,SUN Z,et al.Online cell SOC estimation of Li-ion battery packs using a dual time-scale Kalman filtering for EV applications.Applied Energy,2012,95:227-237.
[15]PLETT G L.Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs,Part 3:State and parameter estimation.Journal of Power sources,2004,134(2):277-292.
[16]PLETT G L.Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs,Part 2:Simultaneous state and parameter estimation.Journal of Power Sources,2006,161(2):1369-1384.
[17]SUN F,XIONG R,HE H.A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique.Applied Energy,2016,162:1399-1409.
[18]LIU X,CHEN Z,ZHANG C,et al.A novel temperature-compensated model for power Li-ion batteries with dual-particle-filter state of charge estimation.Applied Energy,2014,123:263-272.
[19]FARAGHER,RAMSEY.Understanding the basis of the Kalman filter via a simple and intuitive derivation.IEEE Signal Processing Magazine,2012,29(5):128-132.
[20]LIU H,ZHOU T.Robust state estimation for uncertain linear systems with deterministic input signals.Control Theory and Technology,2014,12(4):383-392.
[21]GAO Xianzhong,HOU Zhongxi,WANG Bo.Quaternion-based Kalman filter and its performance analysis in integrated navigation.Control Theory and Applications,2013,30(2):171-177.(高显忠,侯中喜,王波.四元数卡尔曼滤波组合导航算法性能分析.控制理论与应用,2013,30(2):171-177.)
[22]TAKAGI T,SUGENO M.Fuzzy identification of systems and its applications to modeling and control.Readings in Fuzzy Sets for Intelligent Systems,1993,15(1):387-403.
[23]SHANG Yunlong,ZHANG chenghui,CHUI Naxin.State of charge estimation for lithium-ion batteries based on extended Kalman filter optimized by fuzzy neural network.Control Theory and Technology,2016,33(2):212-220.(商云龙,张承慧,崔纳新.基于模糊神经网络优化扩展卡尔曼滤波的锂离子电池荷电状态估计.控制理论与技术,2016,33(2):212-220.)
[24]DU H,ZHANG N.Application of evolving takagi-sugeno fuzzy model to nonlinear system identification.Applied Soft Computing,2008,8(1):676-686.
[25]NELLES O.Nonlinear System Identification:From Classical Approaches to Neural Networks and Fuzzy Models.New York,USA:Springer Science&Business Media,2013:299-341.
[26]PARK M J,KWON O M.Stability and stabilization of discrete-time T-S fuzzy systems with time-varying delay via Cauchy-Schwartzbased summation inequality.IEEE Transactions on Fuzzy Systems,2017,25(1):128-140.
[27]PRECUP R E,FILIP H I,RADAC M B,et al.Online identification of evolving takagi-sugeno-kang fuzzy models for crane systems.Applied Soft Computing,2014,24(C):1155-1163.
[2]GUO Y,TANG S,MENG G,et al.Failure modes of valve-regulated lead-acid batteries for electric bicycle applications in deep discharge.Journal of Power Sources,2009,191(1):127-133.
[3]DUBARRY M,SVOBODA V,HWU R,et al.Capacity loss in rechargeable lithium cells during cycle life testing:The importance of determining state-of-charge.Journal of Power Sources,2007,174(2):1121-1125.
[4]EINHORNE M,CONTE F V,KRAL C,et al.A method for online capacity estimation of lithium ion battery cells using the state of charge and the transferred charge.IEEE Transactions on Industry Applications,2012,48(2):736-741.
[5]CUADRAS A,KANOUN O.SoC Li-ion battery monitoring with impedance spectroscopy.International Multi-conference on Systems.Djerba,Tunisia:IEEE,2009:1-5.
[6]NG K S,MOO C S,CHEN Y P,et al.Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithiumion batteries.Applied Energy,2009,86(9):1506-1511.
[7]WAAG W,SAUER D U.Adaptive estimation of the electromotive force of the lithium-ion battery after current interruption for an accurate state-of-charge and capacity determination.Applied Energy,2013,111:416-427.
[8]YANG N,ZHANG X,LI G.State of charge estimation for pulse discharge of a LiFePO4 battery by a revised AH counting.Electrochimica Acta,2015,151:63-71.
[9]ALVAREZ ANTON J C,GARCIA NIETO P J,BLANCO VIEJOC,et al.Support vector machines used to estimate the battery state of charge.IEEE Transactions on Power Electronics,2013,28(12):5919-5926.
[10]DAI H,GUO P,WEI X,et al.ANFIS(adaptive neuro-fuzzy inference system)based online SOC(state of charge)correction considering cell divergence for the EV(electric vehicle)traction batteries.Energy,2015,80:350-360.
[11]DU J,LIU Z,WANG Y.State of charge estimation for Li-ion battery based on model from extreme learning machine.Control Engineering Practice,2014,26(1):11-19.
[12]SHENG H,XIAO J.Electric vehicle state of charge estimation:Nonlinear correlation and fuzzy support vector machine.Journal of Power Sources,2015,281:131-137.
[13]ZHOU F,WANG L,LIN H,et al.High accuracy state-of-charge online estimation of EV/HEV lithium batteries based on adaptive wavelet neural network.Proceedings of 2013 ECCE Asia Downunder(ECCE Asia).Melbourne,VIC,Australia:IEEE,2013:513-517.
[14]DAI H,WEI X,SUN Z,et al.Online cell SOC estimation of Li-ion battery packs using a dual time-scale Kalman filtering for EV applications.Applied Energy,2012,95:227-237.
[15]PLETT G L.Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs,Part 3:State and parameter estimation.Journal of Power sources,2004,134(2):277-292.
[16]PLETT G L.Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs,Part 2:Simultaneous state and parameter estimation.Journal of Power Sources,2006,161(2):1369-1384.
[17]SUN F,XIONG R,HE H.A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique.Applied Energy,2016,162:1399-1409.
[18]LIU X,CHEN Z,ZHANG C,et al.A novel temperature-compensated model for power Li-ion batteries with dual-particle-filter state of charge estimation.Applied Energy,2014,123:263-272.
[19]FARAGHER,RAMSEY.Understanding the basis of the Kalman filter via a simple and intuitive derivation.IEEE Signal Processing Magazine,2012,29(5):128-132.
[20]LIU H,ZHOU T.Robust state estimation for uncertain linear systems with deterministic input signals.Control Theory and Technology,2014,12(4):383-392.
[21]GAO Xianzhong,HOU Zhongxi,WANG Bo.Quaternion-based Kalman filter and its performance analysis in integrated navigation.Control Theory and Applications,2013,30(2):171-177.(高显忠,侯中喜,王波.四元数卡尔曼滤波组合导航算法性能分析.控制理论与应用,2013,30(2):171-177.)
[22]TAKAGI T,SUGENO M.Fuzzy identification of systems and its applications to modeling and control.Readings in Fuzzy Sets for Intelligent Systems,1993,15(1):387-403.
[23]SHANG Yunlong,ZHANG chenghui,CHUI Naxin.State of charge estimation for lithium-ion batteries based on extended Kalman filter optimized by fuzzy neural network.Control Theory and Technology,2016,33(2):212-220.(商云龙,张承慧,崔纳新.基于模糊神经网络优化扩展卡尔曼滤波的锂离子电池荷电状态估计.控制理论与技术,2016,33(2):212-220.)
[24]DU H,ZHANG N.Application of evolving takagi-sugeno fuzzy model to nonlinear system identification.Applied Soft Computing,2008,8(1):676-686.
[25]NELLES O.Nonlinear System Identification:From Classical Approaches to Neural Networks and Fuzzy Models.New York,USA:Springer Science&Business Media,2013:299-341.
[26]PARK M J,KWON O M.Stability and stabilization of discrete-time T-S fuzzy systems with time-varying delay via Cauchy-Schwartzbased summation inequality.IEEE Transactions on Fuzzy Systems,2017,25(1):128-140.
[27]PRECUP R E,FILIP H I,RADAC M B,et al.Online identification of evolving takagi-sugeno-kang fuzzy models for crane systems.Applied Soft Computing,2014,24(C):1155-1163.