Giuseppe Gusto
Gear shift and power-split optimization of a P2.5 parallel hybrid electric vehicle through Dynamic Programming.
Rel. Federico Millo, Luciano Rolando, Luigi Tresca. Politecnico di Torino, Corso di laurea magistrale in Automotive Engineering (Ingegneria Dell'Autoveicolo), 2023
Abstract: |
Nowadays, governments are making harder rules to reduce pollution and greenhouse gas emissions. To comply, carmakers are moving toward renewable energy. Battery Electric Vehicles (BEVs) have limited range, which worries some people while Hybrid Electric Vehicles (HEVs) could be a good option since they combine traditional and electric power. However, to fully exploit the hybrid power system benefits, it is necessary to introduce the Energy Management System (EMS) into the vehicle control hierarchy in order to coordinate the power flow from the Internal Combustion Engine (ICE) and the Electric Motor/s (EM). This thesis work is focused on the EMS optimization through a global optimization strategy, named Dynamic Programming (DP). In fact, with this technique is possible to find the global minimum of the related control problem. In particular, the DP has been used in order to manage the power split and gear shift of a P2.5 HEV, which allows the hybridization of the vehicle using a gearbox with the same dimension of a conventional Dual Clutch Transmission (DCT). Finally, the developed methodology was firstly tuned on the WLTC driving cycle and tested on different Real Driving Emissions (RDE) driving cycles by means of MATLAB software. The proposed methodology was able to effectively minimizes the CO2 emissions for this type of vehicle, while achieving a realistic gear shifting profile. This thesis work will cover the following topics: an overview of the Emission Regulations and future goals for the automotive sector will be presented in the introduction section, followed by the description of the main hybrid powertrain architecture, with a particular focus on P2.5 architecture in Chapter 1; then the main control strategy will be presented, mainly focusing on Dynamic Programming in Chapter 2. Finally the case study will be described in Chapter 3, followed by the main results presentation and a brief recap of the activity in the conclusion chapter. |
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Relators: | Federico Millo, Luciano Rolando, Luigi Tresca |
Academic year: | 2022/23 |
Publication type: | Electronic |
Number of Pages: | 80 |
Additional Information: | Tesi secretata. Fulltext non presente |
Subjects: | |
Corso di laurea: | Corso di laurea magistrale in Automotive Engineering (Ingegneria Dell'Autoveicolo) |
Classe di laurea: | New organization > Master science > LM-33 - MECHANICAL ENGINEERING |
Aziende collaboratrici: | UNSPECIFIED |
URI: | http://webthesis.biblio.polito.it/id/eprint/26304 |
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