[1] Fu Zhumu, Zhao Rui,. SOC estimation of lithium-ion power battery for HEVbased on advanced wavelet neural network [J]. Journal of Southeast University (English Edition), 2012, 28 (3): 299-304. [doi:10.3969/j.issn.1003-7985.2012.03.008]

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SOC estimation of lithium-ion power battery for HEVbased on advanced wavelet neural network()

基于先进小波神经网络的HEV动力锂离子电池SOC估计

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

28

Issue:

2012 3

Page:

299-304

Research Field:

Automation

Publishing date:

2012-09-30

Info

Title:

SOC estimation of lithium-ion power battery for HEVbased on advanced wavelet neural network

基于先进小波神经网络的HEV动力锂离子电池SOC估计

Author(s):

Fu Zhumu¹;  2;  Zhao Rui¹

¹Electronic Information Engineering College, Henan University of Science and Technology, Luoyang 471003, China
²School of Control Science and Engineering, Shandong University, Jinan 250061, China

付主木¹;  2;  赵瑞¹

¹河南科技大学电子信息工程学院, 洛阳471003; ²山东大学控制科学与工程学院, 济南250061

Keywords:

wavelet neural network;  state of charge(SOC);  hybrid electric vehicle;  lithium-ion power battery

小波神经网络;  荷电状态;  混合动力汽车;  动力锂离子电池

PACS:

TP273

DOI:

10.3969/j.issn.1003-7985.2012.03.008

Abstract:

In order to improve the estimation accuracy of the battery’s state of charge(SOC)for the hybrid electric vehicle(HEV), the SOC estimation algorithm based on advanced wavelet neural network(WNN)is presented. Based on advanced WNN, the SOC estimation model of a lithium-ion power battery for the HEV is first established. Then, the convergence of the advanced WNN algorithm is proved by mathematical deduction. Finally, using an adequate data sample of various charging and discharging of HEV batteries, the neural network is trained. The simulation results indicate that the proposed algorithm can effectively decrease the estimation errors of the lithium-ion power battery SOC from the range of ±8% to ±1.5%, compared with the traditional SOC estimation methods.

为了提高混合动力汽车(HEV)电池荷电状态(SOC)的估计精度, 提出了一种基于先进小波神经网络的HEV动力电池SOC估计算法.首先, 建立了基于先进小波神经网络的电池SOC估计模型.然后, 通过数学推导证明了先进小波神经网络的收敛性.最后, 利用大量HEV动力电池在行驶过程中充放电的数据样本, 对神经网络进行网络训练.仿真结果表明, 所提出的估计算法与传统SOC估计算法相比, 提高了电池SOC的估计精度, 有效地将估计误差从±8%减小到±1.5%.

References:

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[11] Xia Z, Li X, Cui Y. Response time estimation of blinking backlight LCD based on wavelet transformation [J]. Journal of Southeast University:Natural Science Edition, 2011, 41(1): 37-40.(in Chinese)
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Memo

Memo:

Biography: Fu Zhumu(1974—), male, doctor, associate professor, fzm1974@163.com.
Foundation item: The National Natural Science Foundation of China(No.60904023).
Citation: Fu Zhumu, Zhao Rui.SOC estimation of lithium-ion power battery for HEV based on advanced wavelet neural network[J].Journal of Southeast University(English Edition), 2012, 28(3):299-304.[doi:10.3969/j.issn.1003-7985.2012.03.008]

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