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基于IPSO-Elman的锂电池剩余寿命预测 被引量:8

IPSO-Elman based prediction of lithium battery remaining life
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摘要 针对传统埃尔曼(Elman)神经网络在预测过程中初始权值和阈值随机性选取,易陷入局部极小化问题,为提高锂电池剩余寿命的预测精度,提出一种基于自适应权重的改进粒子群(IPSO)-埃尔曼(Elman)神经网络预测锂电池剩余寿命的方法。针对锂电池测量数据中伴随的噪声,利用高斯去噪,削弱数据中的噪声影响,提取原始数据;再利用IPSO全局搜索的能力对Elman神经网络的初始参数进行优化;最后基于美国国家航空航天局(NASA)提供的锂电池测量数据,对提出的方法进行有效性验证,并与常规的BP,Elman算法进行对比。预测结果表明,IPSO-Elman预测误差在不同训练样本下都小于BP,Elman算法,表现出较强的适应能力。 The random selection of initial weights and thresholds in the prediction process of traditional Elman neural network is easy to fall into the problem of local minimization. A method of lithium battery remaining life prediction based on IPSO(improved particle swarm optimization)-Elman neural network is proposed to increase the prediction accuracy of the remaining life of the lithium battery. In allusion to the concomitant noise in the lithium battery measurement data,Gaussian is used to reduce the noise and the impact of noise in data,and extract the original data;the initial parameter of the Elman neural network is optimized by using the IPSO global search capability;and then the effectiveness of the proposed method is verified based on the lithium battery measurement data provided by NASA(national aeronautics and space administration),and compared with conventional BP and Elman algorithms. The prediction results show the prediction error of IPSO-Elman is smaller than that of BP and Elman algorithms under different training samples,which shows a stronger adaptability.
作者 刘子英 钱超 朱琛磊 LIU Ziying;QIAN Chao;ZHU Chenlei(School of Electrics and Automation Engineering,East China Jiaotong University,Nanchang 330013,China)
出处 《现代电子技术》 北大核心 2020年第12期100-105,共6页 Modern Electronics Technique
基金 国家自然科学基金资助项目(51767006)。
关键词 锂电池 剩余寿命预测 IPSO-Elman 预测建模 高斯去噪 参数优化 lithium battery remaining life prediction IPSO-Elman predictive modeling Gaussian de-noising parameter optimization
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  • 1王海燕,杨方廷,刘鲁.标准化系数与偏相关系数的比较与应用[J].数量经济技术经济研究,2006,23(9):150-155. 被引量:99
  • 2赵莉,陈泉林.基于Kalman滤波器的车辆检测与跟踪系统的实现[J].电子测量技术,2007,30(2):165-168. 被引量:8
  • 3MIKOLAJCZYK K, SCHM1D C. A performance eval- uation of local descriptor[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2005, 27(10) : 1615-1630.
  • 4FUKUNAGE K, HOSTETLER L. The estimation of the gradient of a density function with application in pattern recognition[J]. IEEE Transaction on Informa- tion Theory, 1975,21 (1) : 32-40.
  • 5COMANICIU D, RAMESH V, MEER P. Real-time tracking of non-rigid objects using Meanshift[C]. IEEE conference on Computer Vision and Pattern Recognition, 2000 : 142-149.
  • 6COMANICIU D, RAMESH V, MEER P. Kernel- based object tracking[J]. IEEE Transaction on Pat- tern Analysis and Machine Intelligence, 2003,25 (5): 564-577.
  • 7BLAIR W D, RICE T R, MCDOLE B S. Least squares approach to asynchronous data fusion[J]. SPIE Acquisition Tracking and Pointing VI, 1992:130-141.
  • 8YU H J, ZHANG T Z, YUAN J, et al. Trial study on EV battery re- cycling standardization development [J]. Advanced Material Re- search, 2012, 610/613: 2170-2173.
  • 9HEI W, WILLIARD N, OSTERMAN M, et al. Prognos- tics of lithium-ion batteries based on Dempster-Shaier theory and the Bayesian Monte Carlo method [ J]. J Power Sources, 2011, 196(23):10314-10321.
  • 10LEE S, KIM J, LEE J, et al. State-of-charge and ca- pacity estimation of lithium-ion battm7 using a new open-circuit voltage versus state-of-charge E j]. Journal of Power Sources, 2008, 185(2) : 1376-1373.

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