Energy management strategy of hybrid electric vehicle is a complex decision problem of optimal control problem of nonlinear system. Energy management and distribution strategy is the key to improve fuel economy. Energy distribution strategy based on optimization algorithm can solve above problems effectively. Around the hybrid electric vehicle energy management strategy design and optimization, the following several aspects of research work has been done:Firstly, the models of parallel hybrid electric vehicle were studied in this paper. with the numerical modeling method and theoretical modeling method, the mathematical models of the vehicle and components were established, include engine model, motor model, battery model, transmission model, transmission model, wheel model, driver model and vehicle dynamics model, etc. Vehicle simulation model is established in the Cruise simulation software, and a simulation platform was provided for the follow-up research and development of energy management strategy.Secondly, the logic threshold control strategy based on rules was studied. Simulation model of control strategy is established in the State flow environment, and control strategy simulation model is compiled into a DLL file using automatic code generation function of the Matlab software, and the DLL files are integrated into the Cruise software realized the simulation of Matlab with Cruise.Thirdly, energy management strategy based on dynamic programming is studied. Optimal control mathematical model, with the goal of fuel economy, was set up, using state variables of Battery SOC and the transmission ratio and control variable of the engine torque and transmission gear. In order to reduce the amount of calculation, the components mathematical model of hybrid electric vehicle were simplified, using a numerical model based on the experimental data. Simulation results indicate that the energy control strategy based on dynamic programming is superior to power management control strategy based on logic threshold method.Fourthly, energy management strategy based on stochastic dynamic programming is studied. In order to get the minimum fuel consumption of current moment and future moment, vehicle power demand was modeled into the driver power demand markov model; the next moment vehicle power demand can be predicted through the power demand transfer probability matrix based on current power. Policy iteration method is used to solve the stochastic dynamic programming optimal control problem, it is concluded that the control of engine and motor torque sequence. Simulation results indicate that the energy control strategy based on stochastic dynamic programming is superior to energy management control strategy based on logic threshold method, and stochastic dynamic programming based energy management strategy is a global suboptimal solution compared to the energy management strategy based on dynamic programming. |