期刊
APPLIED ENERGY
卷 194, 期 -, 页码 578-587出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2016.09.071
关键词
Plug-in hybrid electric vehicles; Model predictive control; Markov; Energy management; Battery SOC constraint
资金
- National Key Technology R&D Program of China [2013BAG05B00]
- Program for New Century Excellent Talents in University of China [NCET-11-0785]
In this paper, model predictive control (MPC) is employed to resolve the energy management problem of a plug-in hybrid electric bus (PHEB). Dynamic programming (DP), as a global optimization method, is inserted at each time step of the MPC, to solve the optimization problem regarding the prediction horizon. A multi-step Markov prediction model is constructed to forecast the near future driving velocities for the MPC. The battery SOC is restrained to fluctuate near a reference trajectory to ensure the global performance of MPC. Three novel restraining methods are proposed and compared in this paper. The resultant fuel economy performance with different SOC constraint methods are evaluated. Simulation results indicate that by restraining the battery SOC adaptively to the control variables gains the best fuel economy performance, and the fuel consumption of MPC is 8.7% less than a ruled based strategy. (C) 2016 Elsevier Ltd. All rights reserved.
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