Research On Driving Condition Prediction Energy Management Strategy For Range Extend Electric Vehicles

Faced with increasingly stringent fuel consumption restrictions and emission regulations,hybrid vehicles that combine the advantages of traditional vehicles and new energy vehicles have become the best choice at the moment.Range Extend Hybrid Electric Vehicles(REEV),which has the highest energy mixing degree and the simplest transmission system among hybrid electric vehicles,has become a current research hotspot.However,the actual energy-saving and emission-reduction effects of existing hybrid vehicles are far from the designed energy management strategy.If the future driving conditions of the vehicle can be predicted and the control strategy can be improved,the economy of the entire vehicle can be effectively improved.This paper takes a certain compact traditional energy passenger vehicle as the research object,completes the development of the Range Extend power system,carries out the design and optimization problem research of driving condition prediction energy management strategy,and builds the whole vehicle co-simulation analysis software platform and test platform to verify the proposed control strategy.The specific research content is as follows:First of all,based on the analysis of hybrid electric vehicle configuration,REEV working mode and energy flow,according to the performance requirements of the target vehicle model,the components of the powertrain are selected and matched with parameters according to the three design levels of different working modes,including the type and peak power of the drive motor,the type of power battery cell and the seriesparallel connection,the type of reducer and transmission ratio,the type and continuous power of the generator,the capacity and fuel consumption of engine,etc.Then,AMESim and MATLAB/Simulink software are used to complete the construction of the REEV forward joint simulation analysis platform.Using the combining method of theoretical modeling and actual test modeling,the drive motor model,the power battery model,the reducer model,the engine model,the vehicle driver model,longitudinal power and control system models were respectively established to pave the way for follow-up research.Third,the vehicle tracking method was used to collect real vehicle driving data,combined with the mature open source computer vision algorithm of the monocular camera,the detection of the number of vehicles in the field of view and the estimation of the distance between the vehicles ahead to finish the construction of the vehicle cycle with environment information and slope information.The actual vehicle cycle conditions are compared with all driving data,typical driving cycle NEDC and UDDS.Tthe identification of road conditions and driver intention recognition are completed using BP neural network and fuzzy logic control respectively on the constructed real-vehicle cycle conditions.The results show that the accuracy of road conditions identification results is higher than that of traditional identification methods.The predictability of driver intention recognition is better than traditional recognition methods.Finally,The Equivalent Consumption Minimization Strategy(ECMS)control strategy for battery charging and discharging mode is established respectively,and the equivalent factor and the average power value of braking energy recovery in the model are set according to different identification road conditions.According to the road conditions and driver’s intention identification results obtained by the prediction of the working conditions,the predictive control strategies of the SOC threshold and the power demand of the whole vehicle are respectively established and verified by simulation and experimental analysis.The results show that compared with the fixedpoint power plus power follow control strategy,the established control strategy reduces the equivalent fuel consumption per hundred kilometers and improves the vehicle economy.The research in this paper has certain reference value for the system selection and development,working condition predictive control,and energy management strategy development of Range Extend Hybrid Electric Vehicles,and has positive significance for the promotion and application of Range Extend Hybrid Electric Vehicles.