Optimal Energy Management Strategy Of Plug-in Hybrid Electric Vehicles Based On Dynamic Programming

Since invented, vehicles have carried great weight in the history of human civilization and development. The vehicle history of one hundred years has not only witnessed the vigorous development of the automobile industry, but also brought serious challenges of energy security and ecological environment. In the21st century, oil shortage and environment pressure lead to unprecedented energy tsunami in human society, so the human beings arrived at a new era when the new energy system should be fully developed. The automobile industry in the whole world have gradually reached a consensus, that is, the development of New Energy Vehicles including Hybrid Electric Vehicles is the only choice to maintain the sustainable development of the vehicles.Plug-in Hybrid Electric Vehicles are regarded as the focal point of attention for their high fuel economy, low emissions and more electricity from external electrical outlets. The key to Plug-in Hybrid Electric Vehicles to achieve advantages mentioned above lies in energy management strategy which controls the magnitude and flow direction of energy between internal combustion engine and battery. In this paper, energy management strategies of Plug-in Hybrid Electric Vehicles are researched, and the main contents are described as follows:Firstly, dynamic programming (DP) based energy management of Plug-in Hybrid Electric Vehicles is designed. Optimal control model based on the Plug-in parallel hybrid system is built. State of charge and motor torque of the battery are selected as the state and control variables of the model respectively. Using dynamic programming algorithm for solving optimal control strategy (motor torque sequence) aims at the performance indicators:the lowest fuel consumption, which is applied to energy management in the Plug-in Parallel Hybrid Electric Vehicles. Pure electric-power retention strategy is also designed combining with the characteristics of the Plug-in Hybrid Electric Vehicles. Plug-in hybrid vehicle fuel economy under the two control strategies is tested basing on electric vehicle simulator ADVISOR.Secondly, trip model from Qianfoshan Campus to Zhongxin Campus during off-peak hours is built. A school bus of Shandong University is selected to carry out investigations and collect driving data. Based on information extraction and coordinate transformation as well as historical traffic data based trip modeling, a school bus individual section of the road model for Shandong University during off-peak hours is built. The traffic off-peak hours from Qianfo mountain Campus-Center of campus traffic model is built after incorporating the traffic flow, which laid the foundation for the establishment of the traffic model based optimal control strategy in the following part.Finally, optimal control strategy basing on the traffic model of Plug-in hybrid electric vehicle is proposed. Under the conditions that Qianfo mountain Campus-Center of campus traffic mode regards as estimates on the whole road model and a measured driving curve as the actual road conditions, the goal of the optimal control of the Plug-in hybrid vehicles is achieved from the perspectives of the global optimization and local optimization combining with dynamic programming algorithm.The simulation results demonstrate that:(a) compared with pure electric-power retention strategy, Plug-in hybrid vehicle energy management strategy based on dynamic programming and optimal control strategy based on the traffic model performs a better distribution between the motor and engine torque and improves fuel economy.(b) compared with the energy management strategy based on dynamic programming, the optimal control strategy based on the traffic model obtains a less fuel economy, but can better adapt to changing of road conditions and more practical.