IGBT Thermal Model-Based Predictive Energy Management Strategy for Plug-In Hybrid Electric Vehicles Using Game Theory

Abstract

Energy management strategy (EMS) of plug-in hybrid electric vehicle (PHEV) can coordinate the efficient coupling operation between multiple power sources, which is of great significance to the improvement of fuel economy and transmission efficiency. During the actual operation, EMS needs the electric drive system operate frequently to achieve excellent vehicle performance. In this case, the temperature of insulated gate bipolar transistor (IGBT) module inevitably increases and the thermal-mechanical stress caused by temperature fluctuation will lead to failure. To solve the above problems, a model predictive control (MPC)-based EMS considering IGBT temperature is proposed. Firstly, Markov chain-based driver model is built for the prediction of future vehicle speed. Secondly, according to the thermoelectric coupling model description of the IGBT module, a predictive energy management framework is constructed with fuel economy and IGBT temperature indexes as objective functions. Then, under the MPC framework, a non-cooperative game model with engine and IGBT as participants is established, and Nash equilibrium is taken as the solution. Finally, the simulation results indicate that the proposed strategy not only improves the fuel economy by 13.88% and 13.48% compared with the rule-based strategy under two driving conditions, but also effectively reduces the temperature of IGBT.