Predictive control for Plug-in Microturbine powered Hybrid Electric Vehicles using telemetry information

Abstract

In this paper, the energy management control problem is studied for a Microturbine powered Plug-in Hybrid Electric Vehicle (MT PHEV). A predictive on/off controller is proposed to deplete the battery state of charge (SOC) at the end of the driving cycle. The predictive on/off controller depends on predictive driving information such as the remaining driving distance, the average DC bus energy demand per mile and a defined time correction factor, which can be estimated by the onboard historical vehicle data and the GPS navigation system. The optimal solution derived by the dynamic programming (DP) strategy is used as an optimal benchmark. A case study performed in the Matlab/Simulink environment shows that the proposed predictive on/off control results in 31.4%-48.0% less driving cost than the traditional on/off control, and requires only 0.38%-4.31% more driving cost compared with the DP strategy.