Stability analysis of a phase-shifted full-bridge circuit for electric vehicles based on adaptive neural fuzzy PID control

Abstract In the paper, adaptive neural fuzzy (ANF) PID control is applied on the stability analysis of phase-shifted full-bridge (PSFB) zero-voltage switch (ZVS) circuit, which is used in battery chargers of electric vehicles. At first, the small-signal mathematical model of the circuit is construct...

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Autores principales:	Yan Liu, Yan Huang, He Zhang, Qiang Huang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/99ce4f8c848a46dd81ad754ea7294674
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spelling	oai:doaj.org-article:99ce4f8c848a46dd81ad754ea72946742021-12-02T16:56:36ZStability analysis of a phase-shifted full-bridge circuit for electric vehicles based on adaptive neural fuzzy PID control10.1038/s41598-021-99559-42045-2322https://doaj.org/article/99ce4f8c848a46dd81ad754ea72946742021-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-99559-4https://doaj.org/toc/2045-2322Abstract In the paper, adaptive neural fuzzy (ANF) PID control is applied on the stability analysis of phase-shifted full-bridge (PSFB) zero-voltage switch (ZVS) circuit, which is used in battery chargers of electric vehicles. At first, the small-signal mathematical model of the circuit is constructed. Then, by fuzzing the parameters of PID, a closed-loop system of the small-signal mathematical model is established. Further, after training samples collected from the fuzzy PID system by adaptive neural algorithm, an ANF PID controller is utilized to build a closed-loop system. Finally, the characteristics of stability, overshoot and response speed of the mathematical model and circuit model systems are analyzed. According to the simulation results of PSFB ZVS circuit, the three control strategies have certain optimizations in overshoot and adjustment time. Among them, the optimization effect of PID control in closed-loop system is the weakest. From the results of small-signal model and circuit model, the ANF PID system has highest optimization. Experiments demonstrate that the ANF PID system gives satisfactory control performance and meets the expectation of optimization design.Yan LiuYan HuangHe ZhangQiang HuangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yan Liu Yan Huang He Zhang Qiang Huang Stability analysis of a phase-shifted full-bridge circuit for electric vehicles based on adaptive neural fuzzy PID control
description	Abstract In the paper, adaptive neural fuzzy (ANF) PID control is applied on the stability analysis of phase-shifted full-bridge (PSFB) zero-voltage switch (ZVS) circuit, which is used in battery chargers of electric vehicles. At first, the small-signal mathematical model of the circuit is constructed. Then, by fuzzing the parameters of PID, a closed-loop system of the small-signal mathematical model is established. Further, after training samples collected from the fuzzy PID system by adaptive neural algorithm, an ANF PID controller is utilized to build a closed-loop system. Finally, the characteristics of stability, overshoot and response speed of the mathematical model and circuit model systems are analyzed. According to the simulation results of PSFB ZVS circuit, the three control strategies have certain optimizations in overshoot and adjustment time. Among them, the optimization effect of PID control in closed-loop system is the weakest. From the results of small-signal model and circuit model, the ANF PID system has highest optimization. Experiments demonstrate that the ANF PID system gives satisfactory control performance and meets the expectation of optimization design.
format	article
author	Yan Liu Yan Huang He Zhang Qiang Huang
author_facet	Yan Liu Yan Huang He Zhang Qiang Huang
author_sort	Yan Liu
title	Stability analysis of a phase-shifted full-bridge circuit for electric vehicles based on adaptive neural fuzzy PID control
title_short	Stability analysis of a phase-shifted full-bridge circuit for electric vehicles based on adaptive neural fuzzy PID control
title_full	Stability analysis of a phase-shifted full-bridge circuit for electric vehicles based on adaptive neural fuzzy PID control
title_fullStr	Stability analysis of a phase-shifted full-bridge circuit for electric vehicles based on adaptive neural fuzzy PID control
title_full_unstemmed	Stability analysis of a phase-shifted full-bridge circuit for electric vehicles based on adaptive neural fuzzy PID control
title_sort	stability analysis of a phase-shifted full-bridge circuit for electric vehicles based on adaptive neural fuzzy pid control
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/99ce4f8c848a46dd81ad754ea7294674
work_keys_str_mv	AT yanliu stabilityanalysisofaphaseshiftedfullbridgecircuitforelectricvehiclesbasedonadaptiveneuralfuzzypidcontrol AT yanhuang stabilityanalysisofaphaseshiftedfullbridgecircuitforelectricvehiclesbasedonadaptiveneuralfuzzypidcontrol AT hezhang stabilityanalysisofaphaseshiftedfullbridgecircuitforelectricvehiclesbasedonadaptiveneuralfuzzypidcontrol AT qianghuang stabilityanalysisofaphaseshiftedfullbridgecircuitforelectricvehiclesbasedonadaptiveneuralfuzzypidcontrol
_version_	1718382814821351424

Stability analysis of a phase-shifted full-bridge circuit for electric vehicles based on adaptive neural fuzzy PID control

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