A Real-time MPC-based Energy Management of Hybrid Energy Storage System in Urban Rail Vehicles. (October 2018)

• Record Type: Journal Article
• Authors: Jia, Zhidong; Jiang, Jiuchun; Lin, Hongtao; Cheng, Long
• Is Part Of: Energy procedia. Volume 152(2018)
• Page Start: 526
• Page End: 531
• Publication Date: 2018-10
• Subjects: HESS -- energy management strategy -- MPC -- neural networks; Power resources -- Congresses; Power resources -- Periodicals; Power resources; Conference proceedings; Periodicals; 333.7905
• DOI: 10.1016/j.egypro.2018.09.205
• Languages: English
• ISSNs: 1876-6102

Abstract

Abstract: The most challenges for the hybrid energy storage system made up of the battery and super capacitor (SC) are the reasonable energy management strategy (EMS) and real-time implementation. Therefore, a variable-step multistep prediction MPC-based energy management strategy is proposed in this paper, which minimizes the system energy losses of the whole operating process and ensures the battery current and SC SOC in a suitable range. In addition, the neural networks (NN) are applied in this paper for real-time implementation, which are trained by using MPC optimization results. To do this, the loss models of the battery, SC and DC/DC converter are built and Simulation is carried out in MATLAB/Simulink, which shows that the proposed EMS can keep the SC SOC in a suitable range. At the same time, the proposed online energy management method can achieve excellent results of MPC optimization.