GA based Optimization of MOP Control Strategy for an Range- Extended Electric Vehicle

Paper #:
  • 2017-01-2464

Published:
  • 2017-10-08
Abstract:
The Range-extended electric vehicle (RE-EV) is a complex nonlinear system. The control strategy of REEV can be affected by numerous parameters. Firstly, the Multiple Operation Points (MOP) control strategy is proposed based on operation features of the RE-EV and combining with the optimal efficiency region of the engine. The switching logic rules of MOP strategy are designed for the desired operation mode transition, which makes the engine running at high efficiency region. Then,GA(Genetic algorithm) is implemented 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 SOC (State of Charge) and 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 combination is obtained based on this control strategy. Finally,a dynamic simulation model of EREV is developed in Matlab/Simulink. The simulated perform over a driving cycles of NEDC(New European Driving Cycle)shows that the proposed strategy can significantly improve the fuel economy when compared with the rule-based operation strategy.
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