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Implementation and optimization of an innovative control algorithm for the electrification of vintage vehicles powered by an Internal Combustion Engine

Michele Danese

Implementation and optimization of an innovative control algorithm for the electrification of vintage vehicles powered by an Internal Combustion Engine.

Rel. Marco Vacca. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2024

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Abstract:

A retrofit kit serves as a system facilitating the transformation of a traditional internal combustion engine vehicle into a fully electric one. This thesis project aim to enhance a tailored algorithm designed for a vehicle control unit (VCU), an integral part of a conversion kit intended for installation in vintage cars. Specifically, the development of both Powertrain Control Module (PCM) and Battery Management System (BMS) features and how they communicate with each other is addressed. The algorithm is developed within the LabVIEW environment and subjected to testing based on the model-based design approach. The initial section involves a thorough requirements analysis of the PCM and BMS algorithms and presents their preliminary implementation. The PCM developed features encompass throttle mapping, torque output control for an inverter, execution of vehicle-management functions through a state machine, management of charging phase enablement and fault handling. The latter two are related to the BMS developed functions, which include organizing battery management through a state machine, contactors management, current limits computation, state of charge estimation and cells balancing management. Subsequently, a battery module is modeled based on available data and it’s used to carry out the model-in-the-loop testing phase to check the algorithm functionality.

Relators: Marco Vacca
Academic year: 2023/24
Publication type: Electronic
Number of Pages: 132
Subjects:
Corso di laurea: Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica)
Classe di laurea: New organization > Master science > LM-25 - AUTOMATION ENGINEERING
Aziende collaboratrici: eXaV srl
URI: http://webthesis.biblio.polito.it/id/eprint/30955
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