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An optimal control strategy for plug-in hybrid electric vehicles based on enhanced model predictive control with efficient numerical method

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journal contribution
posted on 2022-05-05, 14:19 authored by Yuanjian ZhangYuanjian Zhang, Yanjun Huang, Zheng Chen, Guang Li, Yonggang Liu
Advances in machine learning inspire novel solutions for the validation of complex vehicle models, and spur an easy manner to promote energy management performance of complexly configured vehicles, such as plug-in hybrid electric vehicles (PHEVs). A constructed PHEV model, based on the four-wheel drive passenger vehicle configuration, is validated through an efficient virtual test controller (VTC) developed in this paper. The VTC is designed via a novel approach based on the least square support vector machine and random forest with the inner-interim data filtered by the ReliefF algorithm to validate the vehicle model as necessary. The paper discusses the process and highlights the accuracy improvements of the PHEV model that is achieved by implementing the VTC. The validity of the VTC is addressed by examining the PHEV model to mimic the characteristics of internal combustion engine, motor and generator behaviors observed through the benchmark test. Sufficient simulations and hardware-in-loop test are employed to demonstrate the capability of the novel VTC based model validation method in practical applications. The major novelty of this paper lies in the development of a VTC, by which the vehicle model can be efficiently developed, providing solid framework and enormous convenience for control strategy design.

Funding

National Natural Science Foundation of China under Grant 61763021 and Grant 51775063

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

IEEE Transactions on Transportation Electrification

Volume

8

Issue

2

Pages

2516 - 2530

Publisher

IEEE

Version

  • AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Acceptance date

2021-12-29

Publication date

2022-01-07

Copyright date

2022

ISSN

2332-7782

eISSN

2332-7782

Language

  • en

Depositor

Dr Yuanjian Zhang. Deposit date: 3 May 2022

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