@inproceedings{cd4c25580e0e4604a2f562011dfa8e1f,
title = "Neural network and efficiency-based control for dual-mode hybrid electric vehicles",
abstract = "Now hybrid electric vehicle (HEV) control strategies are mainly aiming at the optimal fuel economy. The performance of most control strategies depends on the driving cycle pre-known. Changing driving condition will influence the optimal results greatly. Therefore, a neural network controller (NNC) is proposed for a dual-mode hybrid vehicle, which can improve fuel efficiency and maintain battery's state of charge (SOC) in most driving conditions. The NNC combined with an efficiency-based strategy can further reducing vehicle fuel consumption by improving the transmission efficiency. The proposed NNC is testified through the hardware-in-the-loop simulation. The test results show that, the control strategy combined neural network and efficiency-based strategy can reduce vehicle fuel consumption and control the battery SOC in a reasonable range. The control strategy has good prospects in the controller design for dual-mode HEVs.",
keywords = "Dual-mode Hybrid Electric Vehicle, Efficiency-based Control Strategy, Hardware-In-the-Loop, Neural Network",
author = "Yunlong Qi and Weida Wang and Changle Xiang",
note = "Publisher Copyright: {\textcopyright} 2015 Technical Committee on Control Theory, Chinese Association of Automation.; 34th Chinese Control Conference, CCC 2015 ; Conference date: 28-07-2015 Through 30-07-2015",
year = "2015",
month = sep,
day = "11",
doi = "10.1109/ChiCC.2015.7260929",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "8103--8108",
editor = "Qianchuan Zhao and Shirong Liu",
booktitle = "Proceedings of the 34th Chinese Control Conference, CCC 2015",
address = "United States",
}