| 用BP神经网络提高锂离子电池化成系统采样精度 |
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| 引用本文： | 吴免利,李劼,肖昕,邹忠.用BP神经网络提高锂离子电池化成系统采样精度[J].中南大学学报(自然科学版),2010,41(1). |
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| 作者姓名： | 吴免利 李劼 肖昕 邹忠 |
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| 作者单位： | 1. 中南大学,冶金科学与工程学院,湖南,长沙,410083
2. 中南大学,信息科学与工程学院,湖南,长沙,410083 |
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| 基金项目： | 国家"十一五"科技支撑计划项目 |
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| 摘 要： | 针对自行设计的YX-20A型锂离子电池化成柜采样精度不高的问题,分别采用动量梯度下降法和L-M优化法以三层BP神经网络为预测模型对采样电流数据进行校正;并用校正后的采样数据通过TL494芯片调节设定基准和充放电电流实测值的偏差。研究结果表明:L-M算法能快速收敛,效果优于动量梯度下降法,当隐含层节点数为9时,L-M算法效果最佳;校正后的电流最大相对误差由原来的5%降到1.1%左右,平均误差小于0.5%;校正后基准电流和实测值间的相对误差波动较平缓,其最大相对误差比校正前有明显下降。
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| 关 键 词： | BP神经网络 锂离子电池 化成系统 |
Improvement of sampling precision in Li-ion battery formation system by using BP neural network |
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| WU Mian-li,LI Jie,XIAO Xin,ZOU Zhong.Improvement of sampling precision in Li-ion battery formation system by using BP neural network[J].Journal of Central South University:Science and Technology,2010,41(1). |
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| Authors: | WU Mian-li LI Jie XIAO Xin ZOU Zhong |
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| Institution: | WU Mian-li1,LI Jie1,XIAO Xin2,ZOU Zhong1 (1. School of Metallurgical Science , Engineering,Central South University,Changsha 410083,China,2. School of information Science , Engineering,China) |
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| Abstract: | Aiming at the solution of low sampling precision problem of developed YX-20A Li-ion formation equipment, two improved algorithms of three layers back-propagation neural network, namely gradient descent with momentum and Levenberg-Marquardt optimization, were introduced as forecasting models to correct the sampling electric current data; then the corrected sampling data were used to adjust deviation between basic set-point values and measured ones through TL494 chip. The results show that Levenberg-Marquardt... |
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| Keywords: | L-M |
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