To cope with the increasing demand of a more sustainable mobility, the main Original Equipment Manufacturers are producing vehicles equipped with hybrid propulsion systems that increase the overall vehicle efficiency and mitigate the emission problem at a local level. The newly gained degrees of freedom of the hybrid powertrain need to be handled by advanced energy management techniques that allow to fully exploit the system capabilities. In this thesis we propose an optimal control approach to the solution of the energy management problem, putting emphasis on the importance of accurate models for the reliability of the optimization solution. In the first part of the thesis we address the energy management problem for a hybrid electric vehicle, including the mitigation of the battery aging mechanisms. We show that, with an optimal management strategy, we could extend the battery life up to 25% for some driving cycles while keeping the fuel savings performance substantially unaltered. In the second part of the thesis we focus on the hydrostatic hybrid transmission, a different hybridization solution that is able to fulfill the high power demand of heavy duty off-highway vehicles. Also in this case, we formulate the energy management problem as an optimal control problem, dealing with the complexity introduced by the discrete valve actuations in the framework of mixed-integer optimal control. We show that, using hydraulic accumulators to recover energy from the regenerative braking, we could reduce fuel consumption up to 13% for a typical driving cycle. In the third and last part of the thesis we show how the optimization approach can be used to systematically design and calibrate control algorithms, casting the calibration problem into a Linear Matrix Inequality. We first develop a non-overshooting closed-loop control for the actuation pressure of a wet clutch, proving the effectiveness of the control on an experimental setup. Finally, we focus on the design of a dead-zone based kinematic observer for the estimation of the lateral velocity of a road vehicle. The structure of the observer presents good noise rejection performance, allowing for the selection of a higher observer gain that improves the estimation accuracy.
Luca De Pascali (Tue,) studied this question.