Energy management in hybrid vehicles through joint optimization of torque split and gear shift

In recent years, the research in the automotive industry focused on finding alternative solutions to conventional internal combustion engines, to cope with the depletion of fossil fuels and to reduce the amount of CO2 emissions. In this context, Hybrid Electric Vehicles seem to be the most promising solution in the short-term future. In such vehicles, the traction effort is provided by summing the contributions of an internal combustion engine and an electric motor. Given the structure of the powertrain, a complex control strategy is needed to properly manage and optimize the torque-split between the engine and the motor. The introduction of a suitable torque-split optimization strategy leads to performance improvement in terms of fuel consumptions with respect to conventional vehicles. Typically, torque-split optimization strategies rely on an high level selection of the gear based on heuristic laws. Further improvements in fuel consumption can be obtained if the gear selection is jointly optimized with the torque split. In this project, an original joint optimization of torque-split and gear shift is proposed. To account for the discrete characteristics of the engaged gear, hybrid modeling techniques have been employed to describe the system dynamics. Then, the control problem has been formulated within the framework of Hybrid Model Predictive Control and using Mixed Integer Quadratic Programming optimization techniques. The effectiveness of the proposed solution is shown through simulation tests performed on standard driving cycles, such as the New European driving cycle and the World harmonized Light-duty vehicles Test Procedure. The simulation tests highlighted significant improvements in terms of fuel consumption performance with respect to the torque-split optimization approach based on heuristic gear shifting strategy.