Self-organising map based classification of LiFePO4 cells for battery pack in EV

Fengxian He, Wei Xiang Shen, Qiang Song, Ajay Kapoor, Damon Robert Honnery, Daya Dayawansa

Research output: Contribution to journalArticleResearchpeer-review

8 Citations (Scopus)


This paper presents a self-organising map (SOM) based classification of lithium iron phosphate cells for a battery pack in EVs. The experimental data of the cells have been obtained to train the SOM. The temperature variation, internal resistance and available capacity of the cells are used as the inputs of the SOM, and the output of the SOM classifies the cells into three groups with the similar characteristics in terms of its input parameters. Then, the cells in the same group are connected in series to build a sorted battery pack whereas the randomly chosen cells are connected in series to build an unsorted battery pack. The comparison of the consistency between these two battery packs under different discharging conditions has demonstrated the effectiveness of the proposed classification method. In addition, the experimental results show that state of charge based approaches for cell balancing are more effective than voltage based approaches.
Original languageEnglish
Pages (from-to)151 - 167
Number of pages17
JournalInternational Journal of Vehicle Design
Issue number1-4
Publication statusPublished - 2015

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