This master's thesis focuses on optimizing energy consumption in Hybrid Electric Vehicles (HEVs) by leveraging data from Advanced Driver Assistance Systems (ADAS). The goal is to enhance existing ADAS functionalities to improve overall energy efficiency using intelligent software control.
- Develop advanced software to model and simulate HEV dynamics.
- Integrate real-time vehicle speed data from ADAS to guide control inputs.
- Minimize energy consumption across the vehicle's journey.
- Validate the solution through simulations and real-world performance comparisons.
- Developed a longitudinal dynamic model of HEVs using MATLAB/Simulink.
- Utilized Simscape Multibody, Driveline, and Electrical toolboxes to simulate vehicle behavior under various conditions.
- Designed a Model Predictive Control (MPC) framework to manage the traction system.
- Incorporated ADAS insights to improve real-time control decisions.
- Software-in-the-Loop (SIL) testing in Simulink for functional verification.
- Benchmarked against a Simulink-based ECMS (Equivalent Consumption Minimization Strategy) model.
- Performed real-world validation using data from actual HEVs applied to a high-fidelity simulation environment.
This project was developed in close cooperation with an industrial partner, integrating:
- Expertise from the ADAS development team.
- Academic knowledge from:
- Mechatronics Engineering, Università di Trento
- Computer Science for Autonomous Systems, ELTE Eötvös Loránd University
- MATLAB / Simulink
- Simscape Multibody, Driveline, Electrical
- Model Predictive Control (MPC)
- ECMS-based benchmark modeling
This thesis contributes to the future of sustainable mobility by:
- Enhancing existing ADAS capabilities.
- Reducing energy consumption in HEVs.
- Bridging the gap between academic research and industry needs.
