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.
Download:

IMEKO-TC4-2020-25.pdf
DOI:

_unreg_tc4-2020.25

Event details:

IMEKO TC:

TC4
Event name:

TC4 Symposium 2020 (ONLINE)
Title:

24^th IMEKO TC4 Symposium and 22^nd International Workshop on ADC and DAC Modelling and Testing (IWADC)
Place:

Palermo, ITALY
Time:

14 September 2020 - 16 September 2020