Research On Energy Management Strategy Of Plug-in Hybrid Electric Vehicle Based On Multi-objective Particle Swarm Optimization

In order to solve the problems of environmental deterioration and resource shortage,hybrid electric vehicles came into being.The structure of the engine motor combined driving power assembly can not only effectively improve the engine efficiency,vehicle emissions,but also enhance the driving ability of the vehicle.For the energy management problem of how to distribute torque among powertrain to improve fuel economy,the existing research is relatively perfect.However,the real-time energy management,which considers both the fuel economy and the emission and current limitation,needs to be improved.In order to solve this problem,this paper studies the energy strategy based on multi-objective optimization.Taking the three engine hybrid vehicle as the research object,firstly,the required torque in the driving process is modeled,then the components of the power assembly are selected,and then the driving cost,emissions,power battery current and other parameters of the whole vehicle under two typical working conditions are obtained by using particle swarm optimization algorithm.In order to solve the problems of exhaust emission and power battery current,two energy management strategies based on multi-objective particle swarm optimization and improved adaptive grid multi-objective particle swarm optimization are designed.The energy management strategy based on particle swarm optimization(PSO)aims at the minimum fuel economy,discretizes the required torque during driving,and distributes the torque between motor and engine reasonably.In the case of unknown driving conditions,the required torque can be obtained through the opening of acceleration and deceleration pedals.The experimental results show that the energy management strategy based on particle swarm optimization algorithm can effectively improve the fuel economy compared with the rule-based energy management strategy.Aiming at the problem that the energy management strategy based on particle swarm optimization only takes fuel economy as the objective function,a control strategy based on multi-objective particle algorithm is proposed.The objective function is the minimum fuel economy,the minimum harmful exhaust emission and the minimum charge and discharge current of power battery.Distribute the required torque between the engine and the motor.The experimental results show that the multi-objective particle swarm optimization algorithm can significantly reduce the harmful exhaust emissions and limit the current of power battery at the cost of certain fuel economy.Aiming at the problem that the control strategy based on multi-objective particle swarm optimization can’t guarantee the diversity of the optimal solution set,the energy management strategy based on the improved adaptive mesh multi-objective particle swarm optimization algorithm is proposed.Several typical multi-objective test functions are used to test the proposed algorithm.The test results show that the improved adaptive mesh multi-objective particle swarm optimization algorithm can effectively improve the diversity of the optimal solution.When the scale of the optimal solution set is the same,the solution distribution of the improved adaptive mesh multi-objective particle swarm optimization algorithm is more uniform.Forthe energy management of hybrid electric vehicles,the improved adaptive mesh multi-objective particle swarm optimization algorithm and multi-objective particle swarm optimization algorithm have more advantages in fuel economy.