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 基于扩展卡尔曼神经网络算法估计电池SOC Alternative Title Estimation of battery SOC based on extended Kalman filter with neural network algorithms 韩忠华; 刘珊珊; 石刚; 董挺 Department 工业控制网络与系统研究室 Source Publication 电子技术应用 ISSN 0258-7998 2016 Volume 42Issue:7Pages:76-78,82 Contribution Rank 1 Funding Organization 国家重大科技专项项目(2011ZX02601-005) ; 校涵育项目(XKHY2-61) Keyword 锂离子电池soc 扩展卡尔曼算法 神经网络 Rc电路模型 Abstract 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%。 Other Abstract 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. Language 中文 Document Type 期刊论文 Identifier http://ir.sia.cn/handle/173321/18820 Collection 工业控制网络与系统研究室 Corresponding Author 韩忠华 Affiliation 1.沈阳建筑大学信息与控制工程学院2.中国科学院沈阳自动化研究所3.中国电子技术标准化研究院 Recommended CitationGB/T 7714 韩忠华,刘珊珊,石刚,等. 基于扩展卡尔曼神经网络算法估计电池SOC[J]. 电子技术应用,2016,42(7):76-78,82. APA 韩忠华,刘珊珊,石刚,&董挺.(2016).基于扩展卡尔曼神经网络算法估计电池SOC.电子技术应用,42(7),76-78,82. MLA 韩忠华,et al."基于扩展卡尔曼神经网络算法估计电池SOC".电子技术应用 42.7(2016):76-78,82.
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