基于无迹卡尔曼滤波估算电池SOC
Battery SOC estimation based on unscented Kalman filtering
查看参考文献15篇
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文摘
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为了实现在线估计汽车动力电池的荷电状态(SOC),提出了结合神经网络的无迹卡尔曼滤波算法。以Thevenin电路为等效电路模型,建立了状态空间表达式,采用最小二乘算法对模型参数进行辨识。在此基础上,利用神经网络算法拟合电池的荷电状态与模型各个参数之间的函数关系,经过多次实验,确定了神经网络算法的收敛曲线,此方法比传统的曲线拟合精度高。介绍了扩展卡尔曼滤波和无迹卡尔曼滤波的原理,并设计了等效电路模型验证实验、电池的SOC测试实验和算法的收敛性实验。实验结果表明,在不同的工况环境下,该方法估计SOC具有可在线估算、估算精度高和环境适应度高等优点,最大误差小于4%。最后验证了结合神经网络的无迹卡尔曼滤波的算法具有较好的收敛性和鲁棒性,可以有效解决初值估算不准确和累计误差的问题。 |
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其他语种文摘
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In order to estimate the State-Of-Charge (SOC) of automobile power lithium-ion battery online, an Unscented Kalman Filtering (UKF) algorithm was proposed combined with neural network. First of all, Thevenin circuit was treated as an equivalent circuit, the state space representation of the battery model was established and the least square method was applied to identify the parameters of model. Then on this basis, the neural network algorithm was expected to fit the functional relationships between SOC of battery and model parameters respectively. After many experiments, the convergence curve of the neural network algorithm was determined. The proposed method was more accurate than the traditional curve fitting. In addition, the Extended Kalman Filtering (EKF) principle and the UKF principle were introduced separately and some tests were designed including the validation experiment of battery equivalent circuit model, the test experiment of SOC and the convergence experiment of the algorithms. The experimental results show that, the proposed method which can be used for SOC estimation online has higher estimation precision and stronger environmental adaptability than simple extended Kalman filtering algorithm under different conditions, its maximum error is less than 4%. Finally, the proposed algorithm combining UKF and neural network has better convergence and robustness, which can be used to solve the problems of inaccurate estimation of initial value and cumulative error effectively. |
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来源
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计算机应用
,2016,36(12):3492-3498 【扩展库】
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DOI
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10.11772/j.issn.1001-9081.2016.12.3492
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关键词
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无迹卡尔曼滤波
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神经网络
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荷电状态
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Thevenin等效电路
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地址
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中国科学院沈阳自动化研究所, 沈阳, 110016
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1001-9081 |
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学科
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电工技术 |
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基金
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2016工信部智能制造标准化项目
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文献收藏号
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CSCD:5874162
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参考文献 共
15
共1页
|
|
1.
孟良荣. 电动车电池现状与发展趋势.
电池工业,2006,11(3):202-206
|
被引
9
次
|
|
|
|
|
2.
Piller S. Methods for state-of-charge determination and their applications.
Journal of Power Sources,2001,96(1):113-120
|
被引
76
次
|
|
|
|
|
3.
宫学庚. 电动汽车动力电池模型和SOC估算策略.
电源技术,2004,28(10):633-636
|
被引
7
次
|
|
|
|
|
4.
卢居霄. 三类常用电动汽车电池模型的比较研究.
电源技术,2006,30(7):535-538
|
被引
19
次
|
|
|
|
|
5.
Nejad S. A systematic review of lumped-parameter equivalent circuit models for real-time estimation of lithium-ion battery states.
Journal of Power Sources,2016,316:183-196
|
被引
20
次
|
|
|
|
|
6.
谭晓军.
电动汽车动力电池管理系统设计,2011:67-86
|
被引
2
次
|
|
|
|
|
7.
何耀.
动力锂电池组状态估计策略及管理系统技术研究,2012:13-18
|
被引
1
次
|
|
|
|
|
8.
姜久春. 纯电动汽车用锂离子电池的建模和模型参数识别.
电力科学与技术学报,2010,25(1):67-74
|
被引
11
次
|
|
|
|
|
9.
魏学哲. 锂离子动力电池参数辨识与状态估计.
同济大学学报(自然科学版),2008,36(2):231-235
|
被引
26
次
|
|
|
|
|
10.
麻友良. 电动汽车用电池SOC定义与检测方法.
清华大学学报(自然科学版),2001,41(11):95-97
|
被引
35
次
|
|
|
|
|
11.
时玮. 磷酸铁锂电池SOC估算方法研究.
电子测量与仪器学报,2010,24(8):769-774
|
被引
40
次
|
|
|
|
|
12.
Charkhgard M. State-of-charge estimation for lithium-ion batteries using neural networks and EKF.
IEEE Transactions on Industrial Electronics,2011,57(12):4178-4187
|
被引
43
次
|
|
|
|
|
13.
He W. State of charge estimation for li-ion batteries using neural network modeling and unscented Kalman filter-based error cancellation.
International Journal of Electrical Power & Energy Systems,2014,62(11):783-791
|
被引
15
次
|
|
|
|
|
14.
Aung H. State-of-charge estimation of lithium-ion battery using square root spherical unscented Kalman filter (Sqrt-UKFST) in nanosatellite.
IEEE Transactions on Power Electronics,2014,30(9):4774-4783
|
被引
18
次
|
|
|
|
|
15.
Partovibakhsh M. An adaptive unscented Kalman filtering approach for online estimation of model parameters and state-of-charge of lithium-ion batteries for autonomous mobile robots.
IEEE Transactions on Industrial Electronics,2015,23(1):357-363
|
被引
2
次
|
|
|
|
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