@inproceedings{b7c60d104fe4403ba3e823ab5d4a30a6,
title = "Energy management strategy based on GIS information and MPC for a heavy-duty dual-mode power-split HEV",
abstract = "The control performance of energy management strategy (EMS) in heavy-duty dual-mode power-split hybrid electric vehicles (PSHEV) are highly dependent on the forecasted velocity and battery state of charge (SOC) planning. In this paper, a model predictive control (MPC)-based energy management strategy is proposed, in which the predicted velocity and SOC trajectory is regarded as reference signal. The velocity predictor is designed based on radial basis function neural network (RBF-NN), and the battery SOC trajectory is planned using the road grade information from Geographic Information System (GIS). The proposed strategy is verified by a Matlab/simulink model. The results indicate that the fuel economy of PSHEV is improved by considering velocity prediction and SOC trajectory planning.",
keywords = "EMS, gIS, hEV, mPC, sOC trajectory planning",
author = "Hui Liu and Xunming Li and Weida Wang and Yang Wang and Lijin Han and Wei Wei",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 3rd IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2018 ; Conference date: 18-07-2018 Through 20-07-2018",
year = "2019",
month = jan,
day = "11",
doi = "10.1109/ICARM.2018.8610835",
language = "English",
series = "ICARM 2018 - 2018 3rd International Conference on Advanced Robotics and Mechatronics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "380--385",
booktitle = "ICARM 2018 - 2018 3rd International Conference on Advanced Robotics and Mechatronics",
address = "United States",
}