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Alternative TitleEstimation of battery SOC based on extended Kalman filter with neural network algorithms
韩忠华; 刘珊珊; 石刚; 董挺
Source Publication电子技术应用
Contribution Rank1
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
Other AbstractAn 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.
Document Type期刊论文
Corresponding Author韩忠华
Recommended Citation
GB/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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