Predictive Energy Management Strategy for Range-Extended Electric Vehicles Based on ITS Information and Start–Stop Optimization with Vehicle Velocity Forecast

Range-extended Electric Vehicles (REVs) have become popular due to their lack of emissions while driving in urban areas, and the elimination range anxiety when traveling long distances with a combustion engine as power source. The fuel consumption performance REVs depends greatly on energy management strategy (EMS). This article proposes practical solution for based an Adaptive Equivalent Fuel Consumption Minimization Strategy (A-ECMS), wherein equivalent factor is dynamically optimized by battery’s State Charge (SoC) traffic information provided Intelligent Transportation Systems (ITS). Furthermore, penalty function incorporated A-ECMS achieve quasi-optimal start–stop control extender. designed more precise vehicle velocity forecasting through nonlinear autoregressive network exogeneous input (NARX). A model studied REV established AVL Cruise environment proposed set up Matlab/Simulink. Lastly, evaluated over multiple Worldwide Light-duty Test Cycles (WLTC) real-world cycles simulation. simulation conditions are preset such that extender must be switched finish planned route. Compared basic Charge-Depleting Charge-Sustaining (CD-CS) strategy, achieves fuel-consumption benefit 9%. With implementation optimization, which forecasting, saving rate can further improved 6.7% 18.2% compared base A-ECMS. efficient, simple structure, it intended implemented vehicle, will available market at end October 2022.