Driving Cycle Identification and Parameters Optimization for Parallel Hybrid Electric Vehicle

In view of limitations of current energy management strategies for hybrid electric vehicle (HEV), take a certain type of parallel hybrid electric vehicle (PHEV) as the research object, the synthetic cycle was built based on 24 standard driving cycles of ADVISOR vehicle simulation software, three parameters, namely mean speed, mean absolute acceleration and idle time ratio of the combined cycle were selected as the features of driving cycles recognition. 4 typical driving cycles were obtained by K means clustering algorithm.An optimization function of vehicle energy consumption was established, and the particle swarm optimization (PSO) algorithm was implemented to optimize the main control parameters of charge depletion-charge sustaining (CD-CS) rule-based control strategy, power distribution weights of 4 typical driving cycles were determined. The vehicle simulation model was built based on the MATLAB/Simulink platform, the simulation results show that the proposed control strategy can accurately identify driving cycle. Compared with the CD-CS rule-based control strategy without driving cycles recognition, the energy allocation between motor and engine is more reasonable, and the vehicle fuel consumption is reduced by 5.45%, besides, the battery state of changel is more stable.