Sensitivity Analysis of the Vehicle Model Mass for Model Predictive Control Based Power Management System of a Plug-in Hybrid Electric Vehicle

Model predictive control strategy (MPC) has been identified as an efficient path for reducing fuel consumption, greenhouse gasses (GHG) emission, or degradation of power-train components for electrified vehicles. MPC is an optimization-based control strategy that aims at finding the optimal control actions of a system by predicting its future behaviors. As a main contribution, this paper investigate, identify, and quantify the effects of inaccuracy on the vehicle model mass estimation on the performance of the MPC controller when the plug hybrid electric vehicle (PHEV) is in the charge sustaining (CS) mode. Simulations show that error in the vehicle mass estimation degrades the performance of the MPC controller regarding the equivalent fuel consumption, the battery aging, and the objective function.