Tommaso Monopoli
A novel procedure for real-time SOH estimation of EV battery packs based on Time Series Extrinsic Regression.
Rel. Edoardo Patti, Alessandro Aliberti, Raimondo Gallo. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2022
Abstract
With the increasing awareness on environmental issues, research and development revolving around automotive industry – one of the most important yet pollutant industries world-wide – has gained momentum. In recent years, electric vehicles (EVs) have been widely accepted as a clean and reliable alternative to fossil fuel vehicles, both in private and public transportation sectors, and are expected to quickly take over the market in the upcoming years. Lithium-ion batteries (LIBs) have emerged as the main enabling technology in the development of EVs, due to their high energy density and long lifespan. One of the key challenges posed by the spread of EV LIB packs is the real-time estimation of their state of health (SOH), commonly regarded as the main indicator of EV aging.
However, SOH estimation is still a challenging task due to the electro-chemical complexity of LIBs and their non-linear charge and discharge dynamics
Relatori
Anno Accademico
Tipo di pubblicazione
Numero di pagine
Informazioni aggiuntive
Corso di laurea
Classe di laurea
Aziende collaboratrici
URI
![]() |
Modifica (riservato agli operatori) |
