基于扩展卡尔曼粒子滤波算法的锂电池SOC估计

中国机械工程 ›› 2015, Vol. 26 ›› Issue (3): 394-397.

基于扩展卡尔曼粒子滤波算法的锂电池SOC估计

赵又群;周晓凤;刘英杰

南京航空航天大学,南京,210016

出版日期:2015-02-10 发布日期:2015-02-09
基金资助:
国家高技术研究发展计划（863计划）资助项目(2011AA11A210,2011AA11A220)

SOC Estimation for Li-Ion Battery Based on Extended Kalman Particle Filter

Zhao Youqun;Zhou Xiaofeng;Liu Yingjie

College of Energy and Power Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing,210016

Online:2015-02-10 Published:2015-02-09
Supported by:
National High-tech R&D Program of China (863 Program)（No. 2011AA11A210,2011AA11A220）

摘要/Abstract

摘要：

锂电池荷电状态用来描述电池剩余电量的多少，进而反映电动汽车的续驶里程，是电池管理系统中的核心参数。电池循环次数、瞬间大电流以及温度等因素都会使电池特性发生变化,使用扩展卡尔曼滤波算法对电池荷电状态进行估计，会有较大的误差甚至导致算法不收敛。为了有效地抑制发散以及噪声的影响,基于锂电池混合噪声模型，应用扩展卡尔曼粒子滤波算法对锂电池荷电状态和电流漂移噪声进行同步估计。最后根据充放电试验数据进行仿真分析，结果证明了该算法的优越性。

关键词: 锂电池, 荷电状态, 混合噪声模型, 扩展卡尔曼粒子滤波

Abstract:

As the key parameter for power battery management, the SOC of Li-ion battery described the residual capacity, and indicated the remainder driving range of electric vehicles. The cycles, instantaneous high current, abnormal temperatures and other factors would change cell characteristics, which might introduce larger errors even divergence over time if the extended Kalman filter algorithm were applied to the SOC estimation. To suppress the divergence and noise, this paper proposed a method based on EKPF algorithm to realize accurate SOC and the current drift estimation on the Li-ion battery mixed noise model. Finally, the superiority of this method was validated by simulation results.

Key words: Li-ion battery, state-of-charge(SOC), mixed noise model, extended Kalman particle filter(EKPF)

中图分类号:

U463

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.

参考文献

[1]郭炳焜,徐徽,王先友,等.锂离子电池[M].长沙:中南大学出版社,2002.
[2]Cai C H,Du D,Liu Z Y.Battery State-of-charge(SOC) Estimation Using Adaptive Neuro-fuzzy Inference System(ANFIS)[C]//IEEE International Conference on Fuzzy Systems. St. Louis,MO,United States,2003:1068-1073.
[3]林成涛,王军平,陈全世,等.用改进的安时计量法估计电动汽车动力电池SOC[J].清华大学学报,2006,46(2):247-251.
Lin Chengtao,Wang Junping,Chen Quanshi,et al.Improved Ah Counting Method for State of Charge Estimation of Electric Vehicle Batteries[J]. Journal of Tsinghua University(Science and Technology),2006,46(2):247-251.
[4]Mark E.Adaptive State of Charge Algorithm for Nickel Metal Hydride Batteries Including Hysteresis Phenomena[J]. Journal of Power Source,2004,126(1):236-249.
[5]曾洁,卜凡涛.基于多项式回归算法的锂电池SOC估测[J].大连交通大学学报,2011,32(4):70-74.
Zeng Jie,Bu FanTao.Study of SOC Estimation and Measurement of Li-Ion Battery Based on Polynomial Regression Algorithm[J]. Journal of Dalian Jiaotong University, 2011,32(4):70- 74.
[6]Plett G L.Extended Kalman Filtering for Battery Management Systems of LiPB-based HEV Battery Packs[J]. Journal of Power Sources, 2004, 134(2):277-292.
[7]范波,田晓辉,马建伟.基于EKF的动力锂电池SOC状态预测[J].电源技术,2010,34(8):797-799.
Fan Bo,Tian Xiaohui,Ma Jianwei.Ekf-Based Estimation of Lithium-Ion Traction-Battery SOC[J]. Chinese Journal of Power Sources, 2010, 34(8): 797-799.
[8]夏超英,张术,孙宏涛.基于推广卡尔曼滤波算法的SOC估算策略[J].电源技术,2007,31(5):414-417.
Xia Chaoying,Zhang Shu,Sun Hongtao.A Strategy of Estimating State of Charge Based on Extended Kalman Filter[J]. Chinese Journal of Power Sources,2007,31(5):414-417.
[9]李超,刘忠庆.基于参数与状态联合估算的SOC预测法[J].电源技术,2012,36(3):362-365.
Li Chao,Liu Zhongqing. SOC Estimate Based on Joint Determination of Parameters and States[J]. Chinese Journal of Power Sources, 2012,36(3):362-365.
[10]戴海峰,魏学哲,孙泽昌.基于扩展卡尔曼滤波算法的燃料电池车用锂离子动力电池荷电状态估计[J].机械工程学报, 2007, 43(2): 92-95.
Dai Haifeng,Wei Xuezhe,Sun Zechang,Estimate State of Charge of Power Lithium-Ion Batteries Used on Fuel Cell Hybrid Vehicle with Method Based on Extended Kalman Filtering[J]. Chinese Journal of Mechanical Engineering, 2007, 143(2): 92-95.
[11]Liye Wang,Lifang Wang,Chenglin Liao.Research on Improved EKF Algorithm Applied on Estimate EV Battery SOC[C]//2010 Adia-Pacific Power and Engergy Engineering Conference.Chengdu,China,2010:1-4.
[12]张洁.基于粒子滤波算法的电动汽车剩余电量动态估计研究[D].北京:北京交通大学,2012.
[13]刘新天.电源管理系统设计及参数估计策略研究[D].合肥:中国科学技术大学,2011.
[14]庞中华,崔红.系统辨识与自适应控制MATLAB仿真[M].北京:北京航空航天大学出版社,2009.