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Requirement Analysis and System Design of a Retrofit Kit for Electric Conversion of an ICE Vehicle

Chiara Rizzo

Requirement Analysis and System Design of a Retrofit Kit for Electric Conversion of an ICE Vehicle.

Rel. Stefano Carabelli, Giovanni Guida. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2022


The electric vehicles (EVs) industry is currently experiencing a rapid growth. The reasons can be attributed to a rise in environmental awareness, fueled by regulatory interventions that simultaneously encourage the adoption of EVs and strictly regulate the emissions of internal combustion engine (ICE) vehicles. In this expanding trend, retrofit kits are a valid solution that does not involve the production of new cars. The EVERGRIN project, which is the focus of this thesis, intends to develop an approved conversion kit able to turn a modern ICE vehicle into an electric one. The difference with respect to other projects is the existence of a Vehicle Management Unit (VMU), a control unit that allows to centralize into a single device the monitoring activities of the original electric and electronic elements of the converted car. This thesis will focus on the process of obtaining functional specifications for the retrofitted vehicle, which will be described graphically (flowcharts, state machines) and by means of textual requirements, written in accordance with the ISO/IEC/IEEE 29148 standard. Moreover, once the vehicle behavior was set, the implementation of requirements was performed. That involved the usage of the Modular Technical Model (MTM), a template tool built using MATLAB and Simulink. The MTM is a complete model of the system and can be used to simulate its behavior, for MIL testing, and for code generation, both for Rapid Control Prototyping and VMU deployment. Lastly, a demo evaluating the acceleration functionality has been built. Its success demonstrated that the established procedure, easy to replicate, may be used in future project developments.

Relators: Stefano Carabelli, Giovanni Guida
Academic year: 2021/22
Publication type: Electronic
Number of Pages: 69
Additional Information: Tesi secretata. Fulltext non presente
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: Brain technologies
URI: http://webthesis.biblio.polito.it/id/eprint/22774
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