基于在线参数辨识和AEKF的锂电池SOC估计
|
摘要
SOC的准确估计对提高电池的动态性能和能量利用效率至关重要,估计过程中,模型参数不准确以及系统噪声的不确定性都会对结果产生较大影响。为减小模型参数辨识和系统噪声对SOC估计精度的影响,本文采用二阶RC等效电路模型,结合自适应扩展卡尔曼滤波算法(AEKF)进行锂电池的SOC估计。用带有遗忘因子的最小二乘法对模型参数进行在线辨识,以减小由参数辨识引起的估计误差,AEKF可以对系统和过程噪声进行修正,从而减小噪声对SOC估计的影响。最后分别用EKF和AEKF进行SOC估计并比较其误差,结果表明,AEKF联合最小二乘法参数在线辨识具有更高的精度和更好的适应性。
The accurate estimation of SOC is very important for improving the dynamic performance and energy utilization efficiency of batteries. In the estimation process, the inaccuracy of model parameters and the uncertainty of system noise will greatly affect the results. In order to reduce the influence of model parameter identification and system noise on the SOC estimation accuracy, this paper adopts the second-order RC equivalent circuit model combined with the adaptive extended kalman filter algorithm(AEKF) to estimate the SOC of lithium batteries. In order to reduce the estimation error caused by parameter identification, the least square method with forgetting factoris used to identify the model parameters online. AEKF can correct the system and process noise, so as to reduce the impact of noise on SOC estimation. At last, EKF and AEKF are used for SOC estimation respectively and their errors are compared. The results show that joint AEKF and least square parameter online identification has higher accuracy and better adaptability.
The accurate estimation of SOC is very important for improving the dynamic performance and energy utilization efficiency of batteries. In the estimation process, the inaccuracy of model parameters and the uncertainty of system noise will greatly affect the results. In order to reduce the influence of model parameter identification and system noise on the SOC estimation accuracy, this paper adopts the second-order RC equivalent circuit model combined with the adaptive extended kalman filter algorithm(AEKF) to estimate the SOC of lithium batteries. In order to reduce the estimation error caused by parameter identification, the least square method with forgetting factoris used to identify the model parameters online. AEKF can correct the system and process noise, so as to reduce the impact of noise on SOC estimation. At last, EKF and AEKF are used for SOC estimation respectively and their errors are compared. The results show that joint AEKF and least square parameter online identification has higher accuracy and better adaptability.
引文
[1]ZHOU Y,LI X.Overview of lithium-ion battery SOC estimation[C]//Proceeding of the 2015 IEEE International Conference on Information and Automation,Lijiang,China,2015.
[2]BOUCAR Diouf,RAMCHANDRA Pode.Potential of lithium-ion batteries in renewable energy[J].Renewable Energy,2015(76):375-380.
[3]RAMADAN H S,BECHERIF M,CLAUDE F.Extended kalman filter for accurate state of charge estimation of lithium-based batteries:Acomparative analysis[J].International Journal of Hydrogen Energy,2017(42):29033-29046.
[4]FLEISCHER C,WAAG W,HEYN H M,et al.On-line adaptive battery impedance parameter and state estimation considering physical principles in reduced order equivalent circuit battery models:Part 1.Requirements,critical review of methods and modeling[J].Journal of Power Sources,2014,260(15):276-291.
[5]ZHANG Fei,LIU Guangjun,FANG Lijin,et al.Estimation of battery state of charge with,observer:applied to a robot for inspecting power transmission lines[J].IEEE Transactions on Industrial Electronics,2012,59(2):1086-1095.
[6]DANG X,YAN L,JIANG H,et al.Open-circuit voltage-based state of charge estimation of lithium-ion power battery by combining controlled auto-regressive and moving average modeling with feedforward-feedback compensation method[J].International Journal of Electrical Power&Energy Systems,2017(90):27-36.
[7]SBARUFATTI C,CORBETTA M,GIGLIO M,et al.Adaptive prognosis of lithium-ion batteries based on the combination of particle filters and radial basis function neural networks[J].Journal of Power Sources,2017,344(3):128-140.
[8]PLETT G L.Extended kalman filtering for battery management systems of LiPB based HEV battery packs:Part 1.Background[J].Journal of Power Sources,2004,134:252-261.
[9]WANG Qianqian,WANG Jiao,ZHAO Pengju,et al.Correlation between the model accuracy and model-based SOC estimation[J].Electrochimica Acta,2017(228):146-159.
[10]JOHNSON V H,SACK T.Temperature-dependent battery models for high-power lithium-ion batteries[C]//17th Annual Electric Vehicle Symposium,Montreal,Canada,2000.
[11]HAN H,XU H,YUAN Z,et al.State of Charge estimation of Liion battery in EVs based on second-order sliding mode observer[C]//Transportation Electrification Asia-Pacific.IEEE,2014:1-5.
[12]杨阳,汤桃峰,秦大同,等.电动汽车锂电池PNGV等效电路模型与SOC估算方法[J].系统仿真学报,2012,24(4):202-206.YANG Yang,TANG Taofeng,QIN Datong,et al.PNGV equivalent circuit model and SOC estimation algorithm of lithium batteries for electric vehicle[J].Journal of System Simulation,2012,24(4):202-206.
[13]DUONG V,BASTAWROUS H A,LIM K,et al.Online state of charge and model parameters estimation of the LiFePO4 battery in electric vehicles using multiple adaptive forgetting factors recursive leastsquares[J].Journal of Power Sources,2015,296(11):215-224.
[14]DONG Xile,ZHANG Caiping,JIANG Jiuchun.Evaluation of SOCestimation method based on EKF/AEKF under noise interference[J].Energy Procedia,2018(152):520-525.
[15]WANG J,GUO J,LEI D.An adaptive Kalman filtering based state of charge combined estimator for electric vehicle battery pack[J].Energy Conversion&Management,2009(50):3182-3186.
[2]BOUCAR Diouf,RAMCHANDRA Pode.Potential of lithium-ion batteries in renewable energy[J].Renewable Energy,2015(76):375-380.
[3]RAMADAN H S,BECHERIF M,CLAUDE F.Extended kalman filter for accurate state of charge estimation of lithium-based batteries:Acomparative analysis[J].International Journal of Hydrogen Energy,2017(42):29033-29046.
[4]FLEISCHER C,WAAG W,HEYN H M,et al.On-line adaptive battery impedance parameter and state estimation considering physical principles in reduced order equivalent circuit battery models:Part 1.Requirements,critical review of methods and modeling[J].Journal of Power Sources,2014,260(15):276-291.
[5]ZHANG Fei,LIU Guangjun,FANG Lijin,et al.Estimation of battery state of charge with,observer:applied to a robot for inspecting power transmission lines[J].IEEE Transactions on Industrial Electronics,2012,59(2):1086-1095.
[6]DANG X,YAN L,JIANG H,et al.Open-circuit voltage-based state of charge estimation of lithium-ion power battery by combining controlled auto-regressive and moving average modeling with feedforward-feedback compensation method[J].International Journal of Electrical Power&Energy Systems,2017(90):27-36.
[7]SBARUFATTI C,CORBETTA M,GIGLIO M,et al.Adaptive prognosis of lithium-ion batteries based on the combination of particle filters and radial basis function neural networks[J].Journal of Power Sources,2017,344(3):128-140.
[8]PLETT G L.Extended kalman filtering for battery management systems of LiPB based HEV battery packs:Part 1.Background[J].Journal of Power Sources,2004,134:252-261.
[9]WANG Qianqian,WANG Jiao,ZHAO Pengju,et al.Correlation between the model accuracy and model-based SOC estimation[J].Electrochimica Acta,2017(228):146-159.
[10]JOHNSON V H,SACK T.Temperature-dependent battery models for high-power lithium-ion batteries[C]//17th Annual Electric Vehicle Symposium,Montreal,Canada,2000.
[11]HAN H,XU H,YUAN Z,et al.State of Charge estimation of Liion battery in EVs based on second-order sliding mode observer[C]//Transportation Electrification Asia-Pacific.IEEE,2014:1-5.
[12]杨阳,汤桃峰,秦大同,等.电动汽车锂电池PNGV等效电路模型与SOC估算方法[J].系统仿真学报,2012,24(4):202-206.YANG Yang,TANG Taofeng,QIN Datong,et al.PNGV equivalent circuit model and SOC estimation algorithm of lithium batteries for electric vehicle[J].Journal of System Simulation,2012,24(4):202-206.
[13]DUONG V,BASTAWROUS H A,LIM K,et al.Online state of charge and model parameters estimation of the LiFePO4 battery in electric vehicles using multiple adaptive forgetting factors recursive leastsquares[J].Journal of Power Sources,2015,296(11):215-224.
[14]DONG Xile,ZHANG Caiping,JIANG Jiuchun.Evaluation of SOCestimation method based on EKF/AEKF under noise interference[J].Energy Procedia,2018(152):520-525.
[15]WANG J,GUO J,LEI D.An adaptive Kalman filtering based state of charge combined estimator for electric vehicle battery pack[J].Energy Conversion&Management,2009(50):3182-3186.