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A machine learning approach for evaluation of battery state of health

Davide Aloisio, Giuseppe Campobello, Salvatore Gianluca Leonardi, Francesco Sergi, Giovanni Brunaccini, Marco Ferraro, Vincenzo Antonucci, Antonino Segreto, Nicola Donato
  • Abstract:
    Ageing estimation of lithium ion (Li-Ion) batteries is a key point for their massive application in the market. In this work, different Machine Learning (ML) techniques were applied and compared to evaluate the State of Health (SoH) of a cobalt based Li-Ion battery, cycled under a stationary application profile. Experimental results show that ML can be profitably used for SoH estimation.
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  • DOI:
    _unreg_tc4-2020.25

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