Model Predictive Control Based Energy Management Algorithm for a Hybrid Excavator

Abstract: The Volvo Group has an ambitious plan to efficiencies its construction equipment vehicles with an introduction of a hybrid engine. This Master thesis is based on, as a first approach, to control the energy split in a hybrid construction equipment vehicle (HCEV) with the control strategy Model Predictive Control (MPC). The work is divided into two main parts, where the first one is to create a piece-wise affine system (PWA) of the nonlinear plant and secondly to define the control strategy. Multiple linear models are created from the different mathematical descriptions with the Taylor expansion and then divided in space with polytopes to represent the hybrid dynamics properly. After that, the MPC strategy is to be created and this is done by defining a cost function, where selected variables should follow some trajectory. At each sample, the MPC controller should compute an optimal control signal during the prediction and control horizon to minimize the fuel consumption, make the internal combustion engine run at a favourable rotational speed and save the durability on the battery. The control problem is simulated in Simulink with a pre-defined driving cycle, as can be seen as an artificial user for the HCEV, which generates a power demand that needs to be satisfied