@inproceedings{260aa430ca2f40beb64acbe6082415dd,
title = "A Discrete Operational Point Control-based Adaptive Dynamic Coordination Control Strategy for HEV",
abstract = "This paper proposes a limited discrete operation points control strategy for series hybrid electric tracked-vehicle. First, to optimize the control parameters of rule-based energy management strategy, particle swarm optimization algorithm is employed. the optimization results were applied to determine the optimal switch strategy between operation points of the engine. Second, a dynamic adaptive strategy is also proposed to achieve the coordinated control of the engine-generator set. Finally, with combination of above parts, a detailed energy management strategy and coordinated control system was proposed. The results demonstrate that the discrete operation points control strategy combined with coordinated control strategy can improve the electricity power supply performance, decrease the fluctuations of the system and stabilize the bus voltage and guarantee robust control performance.",
keywords = "Coordinated Control, Discrete Operational Points, Hybrid Electric Vehicle",
author = "Fei Qi and Yue Ma and Jiaxin Liu and Li, {Zhi Lin} and Qiang Hu",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021 ; Conference date: 14-05-2021 Through 16-05-2021",
year = "2021",
month = may,
day = "14",
doi = "10.1109/DDCLS52934.2021.9455472",
language = "English",
series = "Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "922--928",
editor = "Mingxuan Sun and Huaguang Zhang",
booktitle = "Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021",
address = "United States",
}