Research On Energy Management Strategy Of Plug-in Extended-Range Electric Vehicles Integrating Internet Of Vehicles

With the promulgation and implementation of increasingly stringent emission regulations and fuel consumption regulations,we are currently in the transition stage from traditional vehicles to electric vehicles.Whether in the aspect of technology development or market performance,plug-in Extended-Range Electric Vehicle(EREV)performed well.It is equipped with an internal combustion engine range extender as an auxiliary power unit(APU),which can charge the power battery at any time and balance the problems of fuel consumption and mileage anxiety well.In this paper,a certain EREV is taken as the research object,combined with the simulated data of the Internet of Vehicles(IoV),to study its energy management strategy under urban road conditions,and develop predictive cruise control(PCC)function applied to suburban driving conditions.The specific research contents are as follows:Firstly,the four working modes and energy flow of EREV are analyzed,and the performance requirements of the whole vehicle are analyzed from three aspects:extreme working conditions,acceleration capability,and efficiency optimization.Selection and matching of main components such as motors,power battery packs,APU,and generators are finished under CD and CS mode.After that,Matlab/Simulink,LMS AMESim and SUMO are used to model the forward simulation platform,including the APU model using the steady-state test model,and the motor model using the test model,and the power battery model using the second-order RC model,and the PID-based driver model,and the vehicle longitudinal dynamics model considering the tire model,and the vehicle controller model using the hierarchical control mode.Combined with laboratory resources,a powertrain experimental platform is built to verify the proposed energy management strategy and the developed PCC algorithm,which greatly shortens the development cycle and verification cost.Then,the Simulation of Urban Mobility(SUMO)software is used to simulate the traffic flow environment as the data of IoV.The BP neural network is used to identify the real-time road conditions.The fuzzy logic is used to classify the intention of driver,which improves the accuracy of the identification result.The Equivalent Consumption Minimization Strategy(ECMS)method is selected as the energy management strategy,and parameters such as equivalent factors and SOC thresholds are optimized in combination with real-time road conditions and driver intentions,and tested on the forward simulation platform and the powertrain experimental platform.The results show that the energy management control strategy proposed in this paper is lower than the equivalent fuel consumption of the engine with fixed-point power following strategy.Finally,the Predictive Cruise Control(PCC)function of EREV in suburban working conditions is planned.The GPS sensor and IMU sensor are used to collect road data which is filtered smoothly in advance to simulate the information system of IoV.The dynamic programming algorithm calculates the optimal speed sequence of the test road.The output planned speed is selected after coordinate transformation and Kalman filter processing on the vehicle position.Comparing with the ACC strategy on the powertrain experimental platform,PCC strategy shows better economic performance.The research has a certain reference value for the parameter matching,energy management strategy and integration with the IoV information of EREV,and also has a promotion significance for the related applications of IoV.