Alessandro Di Mauro
Design optimization of Hybrid Electric Vehicles based on Deep Learning algorithms.
Rel. Daniela Anna Misul, Claudio Maino, Alessandro Falai, Alessia Musa. Politecnico di Torino, Master of science program in Mechanical Engineering, 2020
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Abstract
Recent years have seen the flourishing of the so called green wave and environment-related topics have been discussed in many ways for various reasons. The use of Hybrid Electric Vehicles (HEVs) is a valid way to achieve tank-to-wheel (TTW) CO2 emissions reduction. The choice of the design parameters, such as engine displacement or power of the electric machine, remains of fundamental importance. To this end, various algorithms have been deployed to effectively calculate the TTW CO2 emissions of a specific HEV layout. One of this is Dynamic Programming (DP). However, it cannot always be used as it requires high computational power and time.
The main goal of this study is to develop an algorithm that can be used in the context of optimized design of HEVs
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