摘要
针对在非高斯噪声干扰下,传统扩展卡尔曼滤波(EKF)算法估计锂离子电池荷电状态(SOC)时精度低的问题,该文提出一种基于最大相关熵的扩展卡尔曼滤波新算法(MCC-EKF),用于估计锂离子电池的荷电状态。首先对锂离子电池进行Thevenin等效电路建模,并对该模型中的参数进行辨识;然后在不同噪声类型干扰下,分别运用所提出新算法MCC-EKF和EKF算法对电池进行SOC估计。实验结果表明,与EKF算法相比,新算法在高斯噪声干扰下,运行时间增加0.282s,估计精度提高19%;在非高斯噪声干扰下,运行时间增加0.418s,估计精度提高51%;可见新算法的估计精度高于EKF算法,尤其是在非高斯噪声干扰下,新算法的估计精度有显著性提高。另外,新算法在给定错误初始SOC值的情况下,在电池开始工作后10s内就能够收敛到真实值,说明新算法具有较好的鲁棒性。故新算法在运行时间增加很小的情况下,估计精度高且鲁棒性好,是一种非常有效的SOC估计方法。
The traditional extended Kalman filter(EKF)algorithm has low accuracy in estimating the state of charge(SOC)of lithium-ion battery under the non-Gaussian noise interference.Therefore,a new extended Kalman filter(MCC-EKF)algorithm based on maximum correlation-entropy criterion was proposed.Firstly,the Thevenin equivalent circuit of the lithium-ion battery was model and its parameters was identified.Secondly,the proposed algorithm MCC-EKF and EKF algorithm were used to estimate the SOC under different noise interference.The experimental results show that,compared with the EKF algorithm,the running time of the new algorithm increases by 0.282s and the estimation accuracy increases by 19%under Gaussian noise interference;under non-Gaussian noise interference,the running time of the new algorithm increases by 0.418s and the estimation accuracy increases by 51%.In addition,given the wrong initial SOC value,the new algorithm can converge to the true value within 10s after the battery starts working,indicating that the new algorithm has better robustness.The proposed algorithm has high estimation accuracy and good robustness while the increase of running time is small,and it is an effective SOC estimation method.
作者
巫春玲
胡雯博
孟锦豪
刘智轩
程琰清
Wu Chunling;Hu Wenbo;Meng Jinhao;Liu Zhixuan;Cheng Yanqing(School of Electronics and Control Engineering Chang’an University,Xi’an 710061 China;College of Electrical Engineering Sichuan University,Chengdu 610065 China)
出处
《电工技术学报》
EI
CSCD
北大核心
2021年第24期5165-5175,共11页
Transactions of China Electrotechnical Society
基金
国家自然科学基金(61701044)
陕西省重点研发计划(2019ZDLGY15-04-02)资助项目。
关键词
锂离子电池
参数辨识
非高斯噪声
荷电状态
Lithium-ion battery
parameter identification
non-Gaussian noise
state of charge
