@inproceedings{dbb5c7f8d6384d7daefc5510388bc511,
title = "Fast battery SoC trajectory planning for predictive energy management of PHEBs",
abstract = "The battery state of charge (SoC) trajectory is an essential characteristic that represents the balance of consuming fossil fuel and electric energy in hybrid electric vehicles. An optimally planned global SoC trajectory is extremely helpful to the energy management of hybrid powertrains. In this paper, a fast SoC planning method based on supervised learning is proposed, while the global driving cycle is forecasted. The planned SoC trajectory is applied as a guidance for model predictive control (MPC) of the hybrid powertrain in real-time. Real driving cycles are collected from a plug-in hybrid electric bus (PHEB) running in Zhengzhou, China. The SoC planning accuracy and its performance in improving the vehicle fuel economy are validated through a comparison with dynamic programming (DP) results. Simulations demonstrate that our proposed fast SoC planning method is able to reduce the computation time from several minutes to within 1 seconds, and the fuel economy improvement is over 40%.",
keywords = "Energy management, Model predictive control, Plug-in hybrid electric bus, Supervised learning",
author = "Qingyun Min and Junqiu Li and Chao Sun and Changjun Su",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 ; Conference date: 14-10-2019 Through 17-10-2019",
year = "2019",
month = oct,
doi = "10.1109/VPPC46532.2019.8952336",
language = "English",
series = "2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings",
address = "United States",
}