Domenico Carlucci
Battery state of health and state of charge estimation: comparison between classical and machine learning techniques.
Rel. Carlo Novara, Stefano Carabelli. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2021
|
Preview |
PDF (Tesi_di_laurea)
- Tesi
Licenza: Creative Commons Attribution Non-commercial No Derivatives. Download (5MB) | Preview |
Abstract
The topics covered in this thesis work are related to the field of electric vehicles (EVs) optimization. Thanks to their attractive properties, the majority of EVs adopt lithium-ion batteries as main energy source introducing new challenges in the car manufacturer's world. In order to guarantee the optimal management and the safety of the operations performed on the battery, a vehicle subsystem, called Battery Management System (BMS), has to estimate the state of the battery through two fundamental parameters: the State of Charge (SoC) and the State of Health (SoH). Precisely knowing these quantities in a real driving context is actually a challenging task and, for the remarkable industrial value, it has become a hot research topic in the last decade.
In the first part of the thesis report the key aspects of the problem are introduced with a top-down approach, and a state of the art analysis is performed by describing the most relevant SoC and SoH estimation approaches that exist in the literature
Relatori
Tipo di pubblicazione
URI
![]() |
Modifica (riservato agli operatori) |
