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

doi:10.3969/j.issn.1674-8484.2011.02.009

Journal of Automotive Safety and Energy ›› 2011, Vol. 2 ›› Issue (2): 157-164.DOI: 10.3969/j.issn.1674-8484.2011.02.009

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. 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 E-mail: p.raspa@diiga.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

Abstract

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

CLC Number:

U 469.72+2

Paolo Raspa, Leonardo Frinconi, Adriano Mancini, Matteo Cavalletti, Sauro Longhi, Luca Fulimeni, , Paolo Bellesi, , Roberto Isidori. Selection of Lithium Cells for EV Battery Pack Using Self-Organizing Maps[J]. Journal of Automotive Safety and Energy, 2011, 2(2): 157-164.

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

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