@inproceedings{f5ede11d685a4c0d83092c31842f9e69,
title = "Connected PHEV Energy Management based on Global Driving Cycle Construction",
abstract = "In this paper, a global vehicle driving cycle construction method is newly proposed, to enhance the energy management performance of a connected plug-in hybrid electric vehicle (Connected PHEV). We propose a three-step novel real-time future driving cycle construction method. First, historical driving cycles are collected and each of them is divided into a number of speed segments to form a database. Artificial neural network (ANN) is employed to learn the nonlinear correlation between the key features of adjacent speed segments along the entire trip. Finally, this trained ANN model is deployed in real-time to predict the next most possible speed segment based on current driving condition of the vehicle. By sequential operating, the global driving cycle can be constructed. The method is validated in a fixed-route city bus driving scenario using real-world data. Model predictive control (MPC) are adopted to solve the energy management problem. Simulation results illustrate that the driving cycle construction method is able to improve the fuel economy of PHEV by over 29\% compared with traditional energy management method.",
keywords = "Driving Cycle Construction, Energy Management, Fuel Economy, Neural network, Plug-in Hybrid Electric vehicle (PHEV)",
author = "Biao Liang and Chao Sun and Bo Liu",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 33rd Chinese Control and Decision Conference, CCDC 2021 ; Conference date: 22-05-2021 Through 24-05-2021",
year = "2021",
doi = "10.1109/CCDC52312.2021.9601731",
language = "English",
series = "Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6683--6688",
booktitle = "Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021",
address = "United States",
}