Hardware-in-the-loop simulation for the analyzing of smart speed control in highly nonlinear hybrid electric vehicle

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Abstract

Owing to the severe limitations imposed by the Intergovernmental panel on climate change and the rapid development of the automobile industry, the utilize of energy storage units in vehicle systems has been increasingly attracting attention. Hence, this study proposes a new fuzzy Proportional Derivative + Integral (PD+I) controller based on a non-integer system for the robust speed control of highly nonlinear hybrid electric vehicles. In order to have an optimal and adaptive controller, the controller coefficients are tuned online by a novel optimization algorithm, which is called Adaptive Black Hole. In addition, the performance and robustness of the proposed method are tested by the experimental data, the Supplemental Federal Test Procedure (SFTP - US06). In order to prove the superiority and effectiveness of the suggested novel smart controller, a valid comparison is conducted between the results of the proposed method and recent studies on the same topic like the Model Predictive Control and the conventional online fuzzy PD+I (OFPD+I) controllers. Finally, extensive studies and hardware-in-the-loop simulations are presented to prove that the proposed controller can track a desired reference signal with lower deviation and show that the performance of suggested method is more robust in comparison with the prior-art controllers for all the case studies.
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Detaljer

Owing to the severe limitations imposed by the Intergovernmental panel on climate change and the rapid development of the automobile industry, the utilize of energy storage units in vehicle systems has been increasingly attracting attention. Hence, this study proposes a new fuzzy Proportional Derivative + Integral (PD+I) controller based on a non-integer system for the robust speed control of highly nonlinear hybrid electric vehicles. In order to have an optimal and adaptive controller, the controller coefficients are tuned online by a novel optimization algorithm, which is called Adaptive Black Hole. In addition, the performance and robustness of the proposed method are tested by the experimental data, the Supplemental Federal Test Procedure (SFTP - US06). In order to prove the superiority and effectiveness of the suggested novel smart controller, a valid comparison is conducted between the results of the proposed method and recent studies on the same topic like the Model Predictive Control and the conventional online fuzzy PD+I (OFPD+I) controllers. Finally, extensive studies and hardware-in-the-loop simulations are presented to prove that the proposed controller can track a desired reference signal with lower deviation and show that the performance of suggested method is more robust in comparison with the prior-art controllers for all the case studies.
OriginalsprogEngelsk
TitelTransactions of the Institute of Measurement and Control
Publikationsdato2018
DOI
StatusUdgivet - 2018
PublikationsartForskning
Peer reviewJa
ID: 283640210