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Development of innovative modular software architecture, based on artificial intelligence for replacing thermal motor from historic cars with an electric propulsion system.

Stefano Falconi

Development of innovative modular software architecture, based on artificial intelligence for replacing thermal motor from historic cars with an electric propulsion system.

Rel. Marco Vacca. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2022

Abstract:

The main argument of this thesis is to develop a flexible algorithm for the torque management of Battery Electric Vehicles. The project starts with Batteries’ vehicles and electric motor state of art analysis to be aware of the main issues and design a suitable algorithm for the client. Hence, the CAN network is introduced since it is the primary communication protocol used in automotive applications and employed in this project; the software environment used to set the network is Busmaster. The algorithm has been developed using LabVIEW, an object-based graphical programming development environment. Once all the program elements have been described, the simulation overview can begin. Miracle2 rapid control prototyping development board has been used as the Vehicle control unit, with Etas ES581.4 as the CAN interface module. The main focus of this section is to describe each step of the model-based design approach performed to design and simulate the algorithm. Concluding the following steps to be completed in the project has been presented.

Relatori: Marco Vacca
Anno accademico: 2021/22
Tipo di pubblicazione: Elettronica
Numero di pagine: 117
Informazioni aggiuntive: Tesi secretata. Fulltext non presente
Soggetti:
Corso di laurea: Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica)
Classe di laurea: Nuovo ordinamento > Laurea magistrale > LM-25 - INGEGNERIA DELL'AUTOMAZIONE
Aziende collaboratrici: Fev Italia Srl
URI: http://webthesis.biblio.polito.it/id/eprint/22824
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