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汽车安全与节能学报 ›› 2011, Vol. 2 ›› Issue (2): 157-164.DOI: 10.3969/j.issn.1674-8484.2011.02.009

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用自组织图方法选择电动车电池堆的锂电池

Paolo Raspa1, Leonardo Frinconi1, Adriano Mancini1, Matteo Cavalletti1, Sauro Longhi1, Luca Fulimeni1, 2, Paolo Bellesi1, 2, Roberto Isidori2   

  1. 1. DIIGA, Università Politecnica delle Marche, Via Brecce Bianche, Ancona, 60131, 意大利;
    2.  FAAM Group S.p.A., Via Monti Z.I., Monterubbiano (AP), 63026, 意大利
  • 收稿日期:2011-01-24 出版日期:2011-07-11 发布日期:2011-07-11
  • 通讯作者: Sauro Longhi,Full Professor, the coordinator of the PhD course in IntelligentArtificial Systems. E-mail:sauro.longhi@univpm.it
  • 作者简介: Paolo Raspa,PhD candidate at DIIGA, research interests are the Battery Management Systems for Electric Vehicles. E-mail: p.raspa@diiga.univpm.it

Selection of Lithium Cells for EV Battery Pack Using Self-Organizing Maps

Paolo Raspa1, Leonardo Frinconi1, Adriano Mancini1, Matteo Cavalletti1, Sauro Longhi1, Luca Fulimeni1, 2, Paolo Bellesi1, 2, Roberto Isidori2   

  1. 1. DIIGA, Università Politecnica delle Marche, Via Brecce Bianche, Ancona, 60131, 意大利;
    2.  FAAM Group S.p.A., Via Monti Z.I., Monterubbiano (AP), 63026, 意大利
  • Received:2011-01-24 Online:2011-07-11 Published:2011-07-11
  • Contact: Sauro Longhi,Full Professor, the coordinator of the PhD course in IntelligentArtificial Systems. E-mail:sauro.longhi@univpm.it
  • About author:Paolo Raspa,PhD candidate at DIIGA, research interests are the Battery Management Systems for Electric Vehicles. E-mail: p.raspa@diiga.univpm.it

摘要: 堆,运用自组织图神经网络方法(SOM),开发了一种对于同源电池的选择与分类的方法。在FAAM的实验室中,搜集了测试过的LiFePO4 电池的实验数据。选择中考虑的实验数据和辨识特征有:放电电压、开路电压、总容量,以及Randle等效电路模式得来的辨识参数。以每一组备选电池的充电状态(SOV)作为聚群判据,以便找到能给出电池均匀性最好结果的方法。模拟中考察了实验的电动车负荷剖面。结果表明:相比于随机的选择,在电池堆平衡的条件下,本文选用的所有方法都能使SOV变量大幅降低。基于容量和放电电压的方法给出了其中的最佳结果。

关键词: 电动车, 电池选择, 聚群, 锂电池分类, 自组织图(SOM), 神经网络方法

Abstract: A challenging problem in energy storage systems for electric vehicles (EVs) is the effective use of lithium multicell batteries. Because of production tolerances, unbalanced cells can be overstressed during usage, thus leading to the reduction of the available capacity and premature failure of the battery pack. A method for the selection and classification of homogenous cells was developed to form uniform battery pack using self-organizing maps (SOMs) neural networks. Experimental data are collected from a set of LiFePO4 batteries tested in FAAM laboratories. The selection considers both experimental data and identified characteristics: discharge voltage, open circuit voltage, total capacity and identified parameters from Randle’s equivalent circuit modeling. The state of charge (SOV) variability within each selected group of cells has been chosen as the clustering criterion to find the method which gives the best results in terms of homogeneity of the battery. Simulation results consider an experimental EV load profile and show a great reduction of the SOC variability and, consequently, in the balance of the battery pack for all the methods presented compared to random selection. Capacity and discharge voltage based method gives the best results over all.

Key words:  electric vehicles (EVs), cell selection, clustering, lithium cell classification, self-organizing maps (SOMs), neural  networks

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