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Robust mode transition control of four-wheel-drive hybrid electric vehicles based on radial basis function neural network estimation-a simulation study

Ling Li (School of Information Engineering, Henan University of Science and Technology, Luoyang, China and College of Physics and Electronic Information, Luoyang Normal University, Luoyang, Henan, China)
Fazhan Tao (School of Information Engineering, Henan University of Science and Technology, Luoyang, China and Henan Key Laboratory of Robot and Intelligent Systems, Henan University of Science and Technology, Luoyang, China)
Zhumu Fu (School of Information Engineering, Henan University of Science and Technology, Luoyang, China and Henan Key Laboratory of Robot and Intelligent Systems, Henan University of Science and Technology, Luoyang, China)

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering

ISSN: 0332-1649

Article publication date: 1 September 2021

Issue publication date: 8 October 2021

102

Abstract

Purpose

The flexible mode transitions, multiple power sources and system uncertainty lead to challenges for mode transition control of four-wheel-drive hybrid powertrain. Therefore, the purpose of this paper is to improve dynamic performance and fuel economy in mode transition process for four-wheel-drive hybrid electric vehicles (HEVs), overcoming the influence of system uncertainty.

Design/methodology/approach

First, operation modes and transitions are analyzed and then dynamic models during mode transition process are established. Second, a robust mode transition controller based on radial basis function neural network (RBFNN) is proposed. RBFNN is designed as an uncertainty estimator to approximate lumped model uncertainty due to modeling error. Based on this estimator, a sliding mode controller (SMC) is proposed in clutch slipping phase to achieve clutch speed synchronization, despite disturbance of engine torque error, engine resistant torque and clutch torque. Finally, simulations are carried out on MATLAB/Cruise co-platform.

Findings

Compared with routine control and SMC, the proposed robust controller can achieve better performance in clutch slipping time, engine torque error, vehicle jerk and slipping work either in nominal system or perturbed system.

Originality/value

The mode transition control of four-wheel-drive HEVs is investigated, and a robust controller based on RBFNN estimation is proposed. Compared results show that the proposed controller can improve dynamic performance and fuel economy effectively in spite of the existence of uncertainty.

Keywords

Acknowledgements

This work was partially supported by National Natural Science Foundation of China (Grant No. 61473115), the Scientific and Technological Innovation Leaders in Central Plains (Grant No. 194200510012), the Natural Science Foundation of Henan Province (Grant No. 202300410149), the Key Scientific Research Projects of Universities in Henan Province (Grant No. 20A120008), the Scientific and Technological project of Henan Province (Grant Nos. 202102310200, 212102210153).

Citation

Li, L., Tao, F. and Fu, Z. (2021), "Robust mode transition control of four-wheel-drive hybrid electric vehicles based on radial basis function neural network estimation-a simulation study", COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, Vol. 40 No. 4, pp. 870-887. https://doi.org/10.1108/COMPEL-10-2020-0344

Publisher

:

Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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