基于扩展卡尔曼神经网络算法估计电池SOC

针对汽车锂电池的荷电状态（SOC）的问题,基于Thevenin电路为等效电路并且应用扩展卡尔曼算法（EKF）结合神经网络算法进行估计。在进行卡尔曼滤波算法估算过程中,需要用到实时的估算模型参数值（最新值）,即在不同的SOC下模型的参数不同。传统做法是把SOC与各个参数的关系进行普通的拟合,这种方法在拟合过程中存在较大误差。为了解决这个问题,利用神经网络拟合各个电路模型参数与SOC关系曲线。试验结果表明,与单纯的扩展卡尔曼算法相比,该方法能够准确估计电池剩余电量,误差小于3%。...

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Bibliographic Details
Published in电子技术应用 Vol. 42; no. 7; pp. 76 - 78
Main Author 韩忠华 刘珊珊 石刚 董挺
Format Journal Article
LanguageChinese
Published 中国科学院沈阳自动化研究所,辽宁沈阳110000%中国科学院沈阳自动化研究所,辽宁沈阳,110000%中国电子技术标准化研究院,北京,100007 2016
沈阳建筑大学信息与控制工程学院,辽宁沈阳110000
Subjects
RC电路模型
扩展卡尔曼算法
神经网络
锂离子电池SOC
RC电路模型
锂离子电池SOC
RC equivalent circuit
SOC of Li_ion battery
extended Kalman filter algorithm
神经网络
neural network
扩展卡尔曼算法
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ISSN0258-7998
DOI10.16157/j.issn.0258-7998.2016.07.019

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Summary:针对汽车锂电池的荷电状态（SOC）的问题,基于Thevenin电路为等效电路并且应用扩展卡尔曼算法（EKF）结合神经网络算法进行估计。在进行卡尔曼滤波算法估算过程中,需要用到实时的估算模型参数值（最新值）,即在不同的SOC下模型的参数不同。传统做法是把SOC与各个参数的关系进行普通的拟合,这种方法在拟合过程中存在较大误差。为了解决这个问题,利用神经网络拟合各个电路模型参数与SOC关系曲线。试验结果表明,与单纯的扩展卡尔曼算法相比,该方法能够准确估计电池剩余电量,误差小于3%。
Bibliography:An extended Kalman filter algorithm（EKF） with neural network is used to estimate the state of lithium battery（SOC）,which is based on Thevenin equivalent circuit.In the process of extended Kalman filter estimation,the real-time model parameters should be updated with the different SOC regard to the different SOC the different model parameters.The traditional approach which has a big error is that the fitting curve between SOC and the various separate parameters is common.To solve this problem neural net-work is applied to fit curve between the parameters of circuit model and the SOC separately.Finally,the results with the error less than 3 % show that compared with the pure extended Kalman algorithm,the method can realize the more accurate estimation of the remaining battery power.
SOC of Li_ion battery;extended Kalman filter algorithm;neural network;RC equivalent circuit
11-2305/TN
Han Zhonghua,Liu Shanshan,Shi Gang,Dong Ting（1 .Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyan
ISSN:0258-7998
DOI:10.16157/j.issn.0258-7998.2016.07.019