The Optimization of MOP Control Strategy for a Range-Extended Electric Vehicle Based on GA

The range-extended electric vehicle (REEV) is a complex nonlinear system powered by internal combustion engine and electricity stored in battery. This research proposed a Multiple Operation Points (MOP) control strategy of REVV based on operation features of REEV and the universal characteristic curve of the engine. The switching logic rules of MOP strategy are designed for the desired transition of the operation mode, which makes the engine running at high efficiency region. A Genetic algorithm (GA) is adapted to search the optimal solution. The fuel consumption is defined as the target cost function. The demand power of engine is defined as optimal variable. The state of charge (SOC) and vehicle speed are selected as the state variables. The dynamic performance of vehicle and cycling life of battery is set as the constraints. The optimal switching parameters are obtained based on this control strategy. Finally, a dynamic simulation model of REEV is developed in Matlab/Simulink. The REEV perform simulated over of the New European Driving Cycle (NEDC)shows that the proposed strategy can significantly improve the fuel economy when compared with the rule-based operation strategy.