Optimization management of hybrid energy source of fuel cell truck based on model predictive control using traffic light information
- 18 Downloads
Energy optimization management can make fuel cell truck (FCT) power system more efficient, so as to improve vehicle fuel economy. When the structure of power source system and the torque distribution strategy are determined, the essence is to find the reasonable distribution of electric power between the fuel cell and other energy sources. The paper simulates the assistance of the intelligent transport system (ITS) and carries out the eco-velocity planning using the traffic signal light. On this basis, in order to further improve the energy efficiency of FCT, a model predictive control (MPC)-based energy source optimization management strategy is innovatively developed, which uses Dijkstra algorithm to achieve the minimization of equivalent hydrogen consumption. Under the scenarios of signalized intersections, based on the planned eco-velocity, the off-line simulation results show that the proposed MPC-based energy source management strategy (ESMS) can reduce hydrogen consumption of fuel cell up to 7% compared with the existing rule-based ESMS. Finally, the Hardware-in-the-Loop (HiL) simulation test is carried out to verify the effectiveness and real-time performance of the proposed MPC-based energy source optimization management strategy for the FCT based on eco-velocity planning with the assistance of traffic light information.
KeywordsFuel cell truck hybrid energy source management strategy model predictive control traffic light
Unable to display preview. Download preview PDF.
- H. Chen. Model Predictive Control. Beijing: Science Press, 2013 (in Chinese).Google Scholar
- Z. Zhao, P. Shen, Y. Jia, et al. Model predictive real-time optimal control of fuel cell car. Journal of Tongji University (Natural Science), 2018, 46(5): 88–97 (in Chinese).Google Scholar
- H. Rakha, R. K. Kamalanathsharma. Eco-driving at signalized intersections using V2I communication. Proceedings of the 14th International IEEE Conference on Intelligent Transportation Systems. Washington, D.C.: IEEE, 2011: 341–346.Google Scholar
- Q. Zhu. Study on the Control Strategy of Hybrid Electric Vehicle Based on Instantaneous Optimization. Changchun: Jilin University, 2009 (in Chinese).Google Scholar