@inproceedings{9bd5b2eda5c8415ba7a598962a215e9b,
title = "Energy Management Strategy based on Hybrid-Systems Algorithm and Radial Basis Function Neural Network for HEV",
abstract = "In the paper, an energy management strategy (EMS) based on the hybrid system predictive control algorithm and radial basis function neural network algorithm (RBF-NN) is constructed for hybrid electric vehicle(HEV). First, an velocity predictor is proposed based on the RBF-NN and the Chebyshev filter, which aim to improve the calculated accuracy. Based on the detailed analysis, the driveline system is simplified into an hybrid dynamic system and optimized by Mixed integer linear programming algorithm. Through the simulation, the effectiveness of the proposed method is validated in two driving cycles. Results show that the proposed approach improve the internal combustion engine (ICE) fuel economy.",
keywords = "HEV, RBF-NN, energy management, hybrid systems",
author = "Baoshuai Liu and Hui Liu and Lijin Han and Xiaolei Ren",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE; 2021 China Automation Congress, CAC 2021 ; Conference date: 22-10-2021 Through 24-10-2021",
year = "2021",
doi = "10.1109/CAC53003.2021.9728620",
language = "English",
series = "Proceeding - 2021 China Automation Congress, CAC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "571--576",
booktitle = "Proceeding - 2021 China Automation Congress, CAC 2021",
address = "United States",
}