| KEYWORD |
Model Predictive Control of the Electrical Powers on a H2-hybrid vehicle
Thesis in external company
keywords AUTOMOTIVE, HYBRID VEHICLES, PREDICTIVE CONTROL
Reference persons ALESSANDRO RIZZO
External reference persons Dr. Francesco Cianflone, Punch Torino
Description Thesis in company: Punch Torino https://www.punchtorino.com/
A H2-hybrid vehicle is considered, as composed by a H2 Fuel Cell System and a battery, that provide electrical powers in order to achieve what an E-drive motor is requiring to satisfy the engine torque requested by the driver. Optimal electrical power share is sought in order to minimize H2 consumption and maximize components lifetime, through a Model Predictive Control (MPC) strategy.
Given the optimization problem structure, including constraints, proper system models and input data coming from a co-simulation and/or experimental environment, the candidate should develop a tool (in Python, preferably) that is required to:
- Convert the optimization problem into a Quadratic Programming (QP) problem, by building the QP matrices
- Solve the QP optimization problem, by exploiting a proper algorithm
- Perform additional investigations, based on model predictions with the optimal sequence, such as a sensitivity analysis on MPC weights
Notes The thesis will be carried out at Punch, Torino. https://www.punchtorino.com/
Deadline 15/11/2021
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