脉冲大倍率放电条件下磷酸铁锂电池荷电状态估计
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摘要
以磷酸铁锂电池为研究对象,针对电池在脉冲大倍率放电条件下,模型参数变化较大、荷电状态(SOC)难以准确估计的问题,以电池的二阶RC等效电路模型为基础,通过递推最小二乘算法动态辨识模型的参数,建立电池的时变参数模型。再通过时变参数模型建立电池的状态方程和观测方程,并应用二次方根容积卡尔曼算法实现电池的SOC估计。这种SOC估算方式能够适应模型的参数改变,且具有对初值误差的修正能力。经实验验证,在脉冲大倍率放电工况下,所建的时变参数模型可以准确模拟电池端电压的变化,所采用的SOC估算策略,在初值存在较大误差的条件下,依然能够准确估算出电池的SOC。
When the battery is used under the condition of high rate pulsed discharge,its parameters change largely and it is hard to estimate its state of charge(SOC) accurately.To solve the problems,taking LiFePO_4 battery as the research object,firstly,based on the second-order RC equivalent circuit model,recursive least square algorithm is adopted to identify the parameters dynamically,thus a time-varying parameter model is built.Secondly,the process equation and measurement equation of the battery are established.Finally,the square-root cubature Kalman filter algorithm is used to realize the SOC estimation of the battery.This SOC estimation algorithm can not only adapt to the parameter change of the model,but also correct the initial error.Experiments indicate that,under the condition of high rate pulsed discharge,the time-varying parameter model can simulate the variation of terminal voltage accurately,and the SOC estimation algorithm can ensure high accuracy even if the initial value has a large error.
When the battery is used under the condition of high rate pulsed discharge,its parameters change largely and it is hard to estimate its state of charge(SOC) accurately.To solve the problems,taking LiFePO_4 battery as the research object,firstly,based on the second-order RC equivalent circuit model,recursive least square algorithm is adopted to identify the parameters dynamically,thus a time-varying parameter model is built.Secondly,the process equation and measurement equation of the battery are established.Finally,the square-root cubature Kalman filter algorithm is used to realize the SOC estimation of the battery.This SOC estimation algorithm can not only adapt to the parameter change of the model,but also correct the initial error.Experiments indicate that,under the condition of high rate pulsed discharge,the time-varying parameter model can simulate the variation of terminal voltage accurately,and the SOC estimation algorithm can ensure high accuracy even if the initial value has a large error.
引文
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[2]连湛伟,石欣,克潇,等.电动汽车充换电站动力电池全寿命周期在线检测管理系统[J].电力系统保护与控制,2014,42(12):137-142.Lian Zhanwei,Shi Xin,Ke Xiao,et al.The whole life cycle on-line detection and management system of power battery in the electric vehicle charging and exchanging station[J].Power System Protection and Control,2014,42(12):137-142.
[3]朱小平,张涛.基于自适应理论的锂离子电池SOC估计[J].电气技术,2013,14(7):47-50.Zhu Xiaoping,Zhang Tao.New method of SOCestimation for lithium-ion batteries based on selfadaptive system[J].Electrical Engineering,2013,14(7):47-50.
[4]张云云,张其彬.动力锂电池大倍率放电时内外部温度场的研究[J].电源技术,2017,41(5):699-701.Zhang Yunyun,Zhang Qibin.Research of internal and external temperature field of lithium power battery discharged at high rate[J].Chinese Journal of Power Sources,2017,41(5):699-701.
[5]罗红斌,邓林旺,冯天宇,等.LiFePO4锂离子动力电池内阻与放电倍率关系研究[J].储能科学与技术,2017,6(4):799-805.Luo Hongbin,Deng Linwang,Feng Tianyu,et al.The relationship between internal resistance and discharge rate of LiFePO4 batteries[J].Energy Storage Science and Technology,2017,6(4):799-805.
[6]He Hongwen,Zhang Xiaowei,Xiong Rui,et al.Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles[J].Energy,2012(39):310-318.
[7]Guo Yifeng,Zhao Zeshuang,Huang Limin,et al.SOC estimation of lithium battery based on improved BP neural network[C]//8th International Conference on Applied Energy,Beijing,2017:4153-4158.
[8]潘海鸿,吕治强,李君子,等.基于灰色扩展卡尔曼滤波的锂离子电池荷电状态估算[J].电工技术学报,2017,32(21):1-8.Pan Haihong,LüZhiqiang,Li Junzi,et al.Estimation of lithium-ion battery state of charge based on grey prediction model-extended Kalman filter[J].Transactions of China Electrotechnical Society,201732(21):1-8.
[9]张金龙,佟微,李端凯,等.磷酸铁锂电池倍率容量特性建模及荷电状态估算[J].电工技术学报,2017,32(7):215-222.Zhang Jinlong,Tong Wei,Li Duankai,et al.Rate capacity modeling and state of charge estimation of LiFePO4 battery[J].Transactions of China Electrotechnical Society,2017,32(7):215-222.
[10]李晓宇,朱春波,魏国,等.基于分数阶联合卡尔曼滤波的磷酸铁锂电池简化阻抗谱模型参数在线估计[J].电工技术学报,2016,31(24):141-149.Li Xiaoyu,Zhu Chunbo,Wei Guo,et al.Online parameter estimation of a simplified impedance spectroscopy model based on the fractional joint kalman filter for LiFePO4 battery[J].Transactions of China Electrotechnical Society,2016,31(24):141-149.
[11]季迎旭,王明旺.动力电池建模与应用综述[J].电源技术,2016,40(3):740-742.Ji Yingxu,Wang Mingwang.Review in power battery modeling and application[J].Chinese Journal of Power Sources,2016,40(3):740-742.
[12]刘毅,谭国俊,何晓群.优化电池模型的自适应Sigma卡尔曼荷电状态估算[J].电工技术学报,2017,32(2):108-118.Liu Yi,Tan Guojun,He Xiaoqun.Optimized battery model based adaptive sigma Kalman filter for state of charge estimation[J].Transactions of China Electrotechnical Society,2017,32(2):108-118.
[13]陈息坤,孙冬.锂离子电池建模及其参数辨识方法研究[J].中国电机工程学报,2016,36(22):6254-6261.Chen Xikun,Sun Dong.Research on lithium-ion battery modeling and model parameter identification methods[J].Proceedings of the CSEE,2016,36(22):6254-6261.
[14]Ke Mingyang,Chiu Yusang,Wu Chiyao,et al.Battery modelling and SOC estimation of a Li Fe PO4battery[C]//2016 International Symposium on Computer Consumer and Control,Xi’an,2016:208-211.
[15]谢仕炜,胡志坚,吴方劼,等.基于递推最小二乘法的多端口外网静态等值参数辨识方法[J].电力系统保护与控制,2018,46(3):26-34.Xie Shiwei,Hu Zhijian,Wu Fangjie,et al.Static equivalent parameter identification method of multiport external network based on recursive least squares algorithm[J].Power System Protection and Control,2018,46(3):26-34.
[16]张金龙,佟微,漆汉宏,等.平方根采样点卡尔曼滤波在磷酸铁锂电池组荷电状态估算中的应用[J].中国电机工程学报,2016,36(22):6246-6253.Zhang Jinlong,Tong Wei,Qi Hanhong,et al.Application of square root sigma point Kalman filter to SOC estimation of LiFePO4 battery pack[J].Proceedings of the CSEE,2016,36(22):6246-6253.
[17]谷志锋,朱长青,邵天章,等.全状态EKF估计的最优反演鲁棒励磁控制设计[J].电力系统保护与控制,2013,41(19):118-125.Gu Zhifeng,Zhu Changqing,Shao Tianzhang,et al.Design of the optimum back-stepping nonlinear robust excitation control based on the all state parameters EKF estimate[J].Power System Protection and Control,2013,41(19):118-125.
[18]毕天姝,陈亮,薛安成,等.基于鲁棒容积卡尔曼滤波器的发电机动态状态估计[J].电工技术学报,2016,31(4):163-169.Bi Tianshu,Chen Liang,Xue Ancheng,et al.Dynamic state estimator for synchronous machines based on robust cubature Kalman filter[J].Transactions of China Electrotechnical Society,2016,31(4):163-169.
[19]徐树生,李娟,温利,等.强跟踪自适应CKF及其在动力定位中应用[J].电机与控制学报,2015,19(2):101-108.Xu Shusheng,Li Juan,Wen Li,et al.Strong tracking adaptive CKF and application for dynamic positioning[J]Electric Machines and Control,2015,19(2):101-108.
[20]Zhang Dongyang,Deng Zhihong,Wang Bo,et al.The application of square-root cubature Kalman filter in the SINS/CNS integrated navigation system[C]//2016 Chinese Guidance,Navigation and Control Conference,Nanjing,2016:2331-2335.