A Novel Intelligent Nonlinear Controller for Dual Active Bridge Converter With Constant Power Loads

Meng, Xiangqi; Jia, Yanbing; Xu, Qianwen; Ren, Chunguang; Han, Xiaoqing; Wang, Peng

doi:10.1109/tie.2022.3170608

Cited by 23 publications

(8 citation statements)

References 31 publications

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“…where * o V is the reference output voltage. Taking derivative of (25) and substituting (20) satisfies the following inequality for some unknown positive constant η…”

Section: Adaptive Stsmc Methodsmentioning

confidence: 99%

“…Additionally, the proposed methods are parameter-based. Online optimization modeling free controllers are proposed to simplify the optimization process [20], [21]. In [20], a deep reinforcement learning-based back-stepping control strategy is proposed for a DAB converter connected to constant power loads.…”

Section: Introductionmentioning

confidence: 99%

“…Online optimization modeling free controllers are proposed to simplify the optimization process [20], [21]. In [20], a deep reinforcement learning-based back-stepping control strategy is proposed for a DAB converter connected to constant power loads. This method designs a compensation method based on deep reinforcement learning to intelligently minimize output voltage tracking error and improve the operating efficiency of the system.…”

Section: Introductionmentioning

confidence: 99%

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An Adaptive Sliding Mode Control for a Dual Active Bridge Converter With Extended Phase Shift Modulation

et al. 2023

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This paper introduces an adaptive super-twisting sliding mode control (ASTSMC) approach for controlling a dual active bridge (DAB) converter with an extended phase shift (EPS) modulation. The conventional single-phase shift (SPS) modulation-based DAB converter is known to be inefficient. Hence, an optimization algorithm based on the Lagrange multiplier method (LMM) is proposed to minimize both backflow power and inductor current stress simultaneously. Unlike the conventional schemes that use an offline optimization (OFFO) method to derive the phase shift ratios, this paper proposes an online optimization method and an ASTSMC method for generating the inner and outer phase shift ratio respectively. Initially, a generalized average modeling (GAM) for the DAB converter under EPS modulation is derived, and then the proposed ASTSMC is introduced according to this model. The conventional STSMC with constant gains suffers from low performance under disturbances such as load current perturbations, input voltage variations, and output voltage reference variations. Additionally, it requires an overestimated gain under steady-state conditions. To address these issues, a variable gain-based STSMC scheme is proposed to enhance the performance of the converter under all operating conditions. The effectiveness of the proposed method is verified through simulation and experimental results, which are compared with the results of the conventional STSMC method.INDEX TERMS Dual active bridge converter, adaptive super twisting sliding mode control, extended phase shift modulation.

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“…where * o V is the reference output voltage. Taking derivative of (25) and substituting (20) satisfies the following inequality for some unknown positive constant η…”

Section: Adaptive Stsmc Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Adaptive Sliding Mode Control for a Dual Active Bridge Converter With Extended Phase Shift Modulation

et al. 2023

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“…Electric vehicles have been widely used because they reduce the use of fossil energy and are conducive to achieving the dual-carbon goal [1][2][3]. Due to the intermittency of photovoltaic and wind power generation, the voltage on the AC side of the grid fluctuates greatly [4][5][6][7], resulting in voltage disturbance on the DC side of the charging pile, which threatens the charging safety of electric vehicles [8][9][10][11]. The dual active bridge DC-DC converter (DAB) is a key device for electric vehicle charging piles, which has the advantages of high power density, high efficiency, bidirectional energy transmission and easy cascade and parallel connection [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Model predictive control strategy based on Kalman filter for dual active bridge converter

Yan,

Hua,

Xun

et al. 2024

IEICE Electron. Express

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With the widespread use of electric vehicles, the large disturbance caused by the uncertainty of the AC side of the power grid will adversely affect the voltage stability of the DC side of the charging pile, which will threaten the charging safety of electric vehicles. In order to achieve fast and adaptive DC voltage regulation under large disturbance, this paper proposes a dual active bridge (DAB) converter control strategy based on Kalman Filter (KF) and Model Predictive Control (MPC). The improved Euler method is used to obtain a more accurate DAB discrete model, and the MPC control system is designed to improve the control accuracy. The best observation results of KF are used as feedforward compensation to improve the accuracy of output voltage regulation and ensure the stability of electric vehicles in the face of various disturbance problems. Finally, the effectiveness of the control strategy is verified by experiment.

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“…Hence a LED driver related to dc-dc converters was needed for controlling LED current as LEDs have higher sensitivity to voltage variations are criteria that cause current to make a great increase in value if the volt has smaller changes. LED drivers related to buck converters with constant on-time control loops were provided, and optimum outcome in the LED current control was attained [4]. The LED chip has a lifespan of as long as 25 000-to 1,00,000 h, while LED or system lamps include a short life.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Hybrid Henry Gas Solubility Optimization Algorithm with Deep Learning-Based LED Driver System

Ahamed¹,

Sukhi²

2023

J CIRCUIT SYST COMP

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Light emitting diodes (LEDs) have become an effective lighting solution because of the characteristics of energy efficiency, flexible controllability and extended lifetime. They find use in numerous lighting systems for residents, industries, enterprises and street lighting applications. The efficiency and trustworthiness of the LED systems considerably based on the thermal mechanical loading improved several degradation schemes and respective interfaces. The complication of the LED systems limits the theoretic interpretation of the core reasons for the luminous variation or the formation of the direct correlation among the thermal aging loading and the luminous output. Therefore, this paper designs a new hybrid Henry gas solubility optimization with deep learning (HHGSO-DL) algorithm for LED driver system design. The presented HHGSO-DL technique mainly concentrates on the derivation of empirical relationships among the design parameters, thermal aging loading and luminous outcomes of the LED product. In the presented HHGSO-DL technique, bidirectional long short-term memory (BiLSTM) algorithm is executed for examining the empirical relationship and its hyperparameters can be tuned by the HHGSO algorithm. In this work, the HHGSO algorithm is derived by the integration of traditional HGSO algorithm with oppositional-based learning (OBL) concept. The performance of the HHGSO-DL technique can be investigated on LED chip packaging and LED luminaire with thermal aging loading. The extensive results demonstrate the promising performance of the HHGSO-DL technique over other state-of-the-art approaches.

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A Novel Intelligent Nonlinear Controller for Dual Active Bridge Converter With Constant Power Loads

Cited by 23 publications

References 31 publications

An Adaptive Sliding Mode Control for a Dual Active Bridge Converter With Extended Phase Shift Modulation

An Adaptive Sliding Mode Control for a Dual Active Bridge Converter With Extended Phase Shift Modulation

Model predictive control strategy based on Kalman filter for dual active bridge converter

Modeling of Hybrid Henry Gas Solubility Optimization Algorithm with Deep Learning-Based LED Driver System

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