Energy Management Strategy Of Hybrid Electric Vehicle Based On The Driving Intention And Road Condition

Energy management strategy is one of the key technologies to decide the performance of HEV, and a reasonable and effective energy management strategy plays an important role in reducing total fuel consumption. Therefore, research on energy management strategy of HEV has great theoretical significance and application value.This thesis relies on National Natural Science Fund Project "Research on Components Sizing and System Control of The Dual Clutch Full Hybrid Electric Vehicle with Single ISG Motor"(51305468). The main works are as follows:(1)This thesis takes the single motor plug-in hybrid electric vehicle as the object of study. Coordinate optimization for parameters of powertrain and control strategy is the key to improving fuel economy. In order to reduce fuel consumption, a methodology using Simulated Annealing Particle Swarm Optimization(SAPSO) and multi-cycle is presented to achieve parameter optimization for both the powertrain and the control strategy based on the constraint of the dynamic performance requirements. The simulation has been carried out based on the MATLAB/SIMULINK software, and the result show that the proposed approach can find a set of control parameters to reduce fuel consumption with keeping power performance and balance of battery SOC. After optimization, the fuel consumption of vehicle will be reduced 5.49% at comprehensive driving cycle.(2)According to the limitations existed in the energy management strategy of hybrid electric vehicle(HEV) under fixed driving cycle condition, the existing energy management strategies which were based on driving pattern recognition fail to fully consider the influence of battery state of charge(SOC) on fuel economy in some driving cycle in the progress of driving. Twenty three typical driving cycles were chosen from ADVISOR software and five categories were divided by using clustering analysis method, key parameters of each category were optimized by using Particle Swarm Algorithm, with the goal of reducing fuel consumption, relevant optimized results were saved in database, an energy management strategy optimization method of HEV based on driving pattern recognition was proposed. Finally, MATLAB/SIMULINK simulations for the energy management was carried out under a comprehensive test cycle.(3)Considering the driving intention has certain influence to the vehicle energy consumption economy. The vehicle torque correction coefficient is determined by driving intention which is recognized through the vehicle jerk analysis, and the vehicle demand torque is corrected using the fast transient response characteristics of the motor, combined with the energy management strategy based on driving cycle recognition, the comprehensive energy management strategy based on the recognition of driving intention and road condition is proposed. Finally, MATLAB/SIMULINK simulations for the energy management was carried out under a comprehensive test cycle, simulation results show that vehicle fuel consumption is cut down 1.71% compared with the energy management strategy under driving cycle recognition, and the motor energy recovery is increased.(4)The vehicle control software for HEV are developed based on Matlab/ Simulink/ Stateflow and D2 P platform. Then the data acquisition and calibration control system are set up by ATI-VISION. Finally, road tests are conducted, and the results partly demonstrate the effectiveness of the energy management strategy in this work.