On the effect of prediction model complexity on the performance of a nonlinear model predictive controller for the energy management of a parallel hybrid electric vehicle
Gaetano Tavolo
On the effect of prediction model complexity on the performance of a nonlinear model predictive controller for the energy management of a parallel hybrid electric vehicle.
Rel. Mauro Velardocchia, Aldo Sorniotti. Politecnico di Torino, Master of science program in Mechanical Engineering, 2020
Abstract
This master’s thesis work deals with the control system development and performance evaluation for a through-the-road-parallel (TTRP) hybrid electric vehicle with an objective of reducing the fuel consumption and emission. A conventional 2.0 l diesel ICE vehicle is downsized to 1.6 l diesel engine and an electric motor is integrated at the rear axle allowing the implementation of through-the-road-parallel hybrid architecture. Further, a motor generator unit is coupled with the ICE at the front axle to implement a start and stop control strategy. The aim of this work is to study the effect of prediction model complexity on the performance of a nonlinear model predictive controller for the energy management of a downsized through-the-road-parallel hybrid electric vehicle, demonstrating the viability in terms of fuel economy and emission performance.
An adaptive equivalent consumption minimization strategy (A-ECMS) and a nonlinear model predictive control (NMPC) with different levels of the prediction model complexity are proposed for the hybrid vehicle along with the imposition of optimal gear shift selection
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