Research On Energy Management Strategy Of Dual Motor Plug-in Hybrid Electric Vehicle

As global energy scarcity becomes increasingly prominent,more and more automotive companies are intensifying their research and development efforts in the field of new energy vehicles.Among them,the Plug-in Hybrid Electric Vehicle(PHEV)stands out with its ability to charge from an external power source,combining the advantages of both electric vehicles and conventional fuel-powered vehicles.This makes it highly valuable and significant for research purposes.This article aims to assist a certain automotive company in transforming their existing fuel-powered MPV into a dual-motor plug-in hybrid electric vehicle,and to conduct research on energy management strategies for this vehicle model.The specific research contents are as follows:First,complete the selection and parameter matching of the entire vehicle powertrain system.Based on the project requirements,design the powertrain system architecture for the PHEV with dual electric motors,and determine the performance indicator parameters that meet the requirements.Select and match the key components of the entire vehicle powertrain system to ensure that they can work effectively together.Secondly,develop and establish a PHEV energy management strategy model based on acceleration intent.Based on the driver’s acceleration demands,design a fuzzy controller to identify the driver’s real-time acceleration intent,adjust the original vehicle demand torque,and establish a model based on acceleration intent recognition.Meanwhile,considering the PHEV operating mode,adopt a CD-CS energy management strategy and design rules for vehicle torque distribution and mode switching.Combining the acceleration intent recognition module,establish an energy management strategy model based on acceleration intent.Then,research a multi-objective particle swarm optimization-based energy management strategy for acceleration intent and driving cycle recognition.Combining the energy management strategy based on acceleration intent,design a driving cycle recognition module to extract characteristic parameters from standard driving cycles,and use back propagation neural network to recognize driving cycles.A new energy management strategy based on acceleration intent and driving cycle recognition is proposed.Meanwhile,the multi-objective particle swarm optimization(MOPSO)algorithm is used for iterative optimization of the vehicle transmission coefficients under the energy management strategy based on acceleration intent and driving cycle recognition to improve fuel economy and achieve more environmentally friendly driving.Finally,the vehicle model is built and simulated using CRUISE software,and joint simulations are performed with MATLAB/Simulink to verify the dynamic performance of the whole vehicle and the advantages and disadvantages of different energy management strategies in terms of fuel economy.The research results show that:(1)Under WLTC test conditions,the simulated parameters of the vehicle’s dynamic performance can meet the performance target set by the company.Comparing the PHEV energy management strategy based on acceleration intention with the CD-CS energy management strategy,it is found that the former can effectively improve fuel economy,highlighting the importance of driver’s acceleration intention recognition.(2)Under comprehensive testing conditions,the energy management strategy based on acceleration intention + driving cycle recognition optimized by MOPSO algorithm is more effective in reducing fuel consumption and saving costs compared to the pre-optimized and single driving cycle recognition strategies.In conclusion,adopting the MOPSO(Multi-Objective Particle Swarm Optimization)based approach for accelerating intention recognition along with operating condition identification strategy not only enables accurate detection of the driver’s acceleration intention and real-time operational information but also significantly enhances fuel economy,thereby playing a crucial role in resource and cost savings.