基于自适应扩展卡尔曼滤波法的储能电池荷电状态估计研究
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摘要
荷电状态估计是储能电池管理的一项重要指标。目前工程上广泛使用的安时积分法虽然简单,但是存在诸多局限性。为了提高电量估算的精度和速度,同时考虑实际应用需求,针对储能电池开展了基于自适应扩展卡尔曼滤波(AEKF)法的荷电状态估计研究,以二阶Thevenin等效电路模型为基础,列写状态空间表达式,建立滤波器模型并根据实际情况对算法进行适当改进。仿真实验通过对比扩展卡尔曼滤波(EKF)法和AEKF方法,证实了AEKF方法的优越性。
The state of charge(SOC) estimation plays an important role in the management of energy storage batteries.The Amperehour integral method is most commonly used in engineering, which is simple but has many limitations.In order to improve accuracy and speed of SOC estimation, the adaptive extended Kalman filter(AEKF) method is studied, considering the actual application requirements.On the basis of second-order Thevenin equivalent circuit model, the state-space equations is given and the filter model is built with some improvement(s) according to the actual conditions.In simulation experiments, the AEKF method is compared to the extended Kalman filter(EKF), which shows the superiority of the AEKF method.
The state of charge(SOC) estimation plays an important role in the management of energy storage batteries.The Amperehour integral method is most commonly used in engineering, which is simple but has many limitations.In order to improve accuracy and speed of SOC estimation, the adaptive extended Kalman filter(AEKF) method is studied, considering the actual application requirements.On the basis of second-order Thevenin equivalent circuit model, the state-space equations is given and the filter model is built with some improvement(s) according to the actual conditions.In simulation experiments, the AEKF method is compared to the extended Kalman filter(EKF), which shows the superiority of the AEKF method.
引文
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[3] SAUER D U,BOPP G,JOSSEN A,et al.State of chargewhat do we really speak about[C]. Copenhagen:The 21st International Telecommunications Energy Conference.1999:6-9.
[4] XU D, HUO X, BAO X, et al.Improved EKF for SOC of the storage battery[C]. Takamatsu:Mechatronics and Automation(ICMA), 2013 IEEE International Conference 2013:1497-1501.
[5]张金龙,夏超英.基于dSPACE的铅酸蓄电池SOC估计[J].电力电子技术,2011,45(5):81-83.ZHANG Jinlong, XIA Chaoying. State of charge estimation of VRLA battery based on dspace[J]. Power Electronics,2011,45(5):81-83.
[6]范兴明,曾求勇,张鑫.基于改进安时积分法的电动汽车电池SOC估计与仿真研究[J].电气应用,2015(8):111-115.FAN Xingming, ZENG Qiuyong, ZHANG Xin. SOC estimation and simulation of electric vehicle battery based on improved ampere-time integral method[J]. Electrotechnical Application,2015(8):111-115.
[7] WEIGERT T, TIAN Q, LIAN K. State-of-charge prediction of batteries and battery-supercapacitor hybrids using artificial neural networks[J]. Journal of Power Sources, 2011, 196(8):4061-4066.
[8] MALKHANDI S. Fuzzy logic-based learning system andestimation of state of charge of lead acid battery[J].Engineering Applications of Artificial Intelligence, 2006,19(5):479-485.
[9] HU C, YOUN B D, CHUNG J. A multiscale framework with extended Kalman filter for lithiumion battery SOC and capacity estimation[J]. Applied Energy, 2012, 92:694-704.
[10] ZHANG J, XIA C. State of charge estimation of valve regulated lead acid battery based on multi-state unscented Kalman filter[J]. International Journal of Electrical Power&Energy Systems, 2011, 33(3):472-476.
[11]李江,王义伟,魏超,等.卡尔曼滤波理论在电力系统中的应用综述[J].电力系统保护与控制,2014(6):135-144.LI Jiang, WANG Yiwei, WEI Chao, et al. A survey on the application of Kalman filtering method in power system[J]. Power System Protection and Control, 2014(6):135-144.
[12]陈建强,洪彬倬,文波.基于扩展卡尔曼滤波算法的电网动态状态估计[J].广东电力,2017, 30(10):92-98.CHEN Jianqiang, HONG Binzhuo, WEN Bo. Dynamic state estimation in power system based on the extended Kalman filter[J]. Guangdong Electric Power, 2017, 30(10):92-98.
[13]李朋,赵克壮.基于优化模型的配电系统状态估计方法[J].智慧电力,2017,45(11):47-51.LI Peng, ZHAO Kezhuang. State estimation method for distribution system based on optimization model[J]. Smart Power, 2017,45(11):47-51.
[14]王振浩,刘宇男,张明江,等.基于双向DC/AC变换器的混合储能系统动态控制策略[J].电力系统保护与控制,2017,45(3):26-32.WANG Zhenhao, LIU Yunan, ZHANG Mingjiang.Dynamic control strategy for hybrid energy storage system based on bi-directional DC/AC converters[J]. Power System Protection and Control,2017,45(3):26-32.
[15] XIONG R, HE H, SUN F, 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.
[16] JUNPING W, JINGANG G, LEI D. An adaptive Kalman filtering based state of charge combined estimator for electric vehicle battery pack[J]. Energy Conversion and Management, 2009,50(12):3182-3186.
[17]贾玉健,解大,顾羽洁,et al.电动汽车电池等效电路模型的分类和特点[J].电力与能源,2011(6):516-521.JIA Yujian, XIE Da, GU Yujie. Classification and characteristics of equivalent circuit models for EV's battery[J].Power and Energy, 2011(6):516-521.
[18]王大磊,王康丽,程时杰,等.液态金属电池储能特性建模及荷电状态估计[J].中国电机工程学报,2017, 37(8):2253-2260.WANG Dalei, WANG Kangli, CHENG Shijie, et al.Modeling of energy storage properties and SOC estimation for liquid metal batteries[J]. Proceedings of the CSEE,2017, 37(8):2253-2260.
[19] UNKEHAEUSER T, SMALLWOOD D. Freedom CAR battery test manual for powerassist hybrid electric vehicles[R].U.S.Department of Energy,2003.