A Novel Control Scheme of Four Switch Buck-Boost Converter for Super Capacitor Pre-Charger

Kwon, Hyeon-Jun; Kim, Ku-Yong; Kim, Jun-Ho

doi:10.1109/access.2024.3382715

IEEE Access

2024

DOI: 10.1109/access.2024.3382715

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A Novel Control Scheme of Four Switch Buck-Boost Converter for Super Capacitor Pre-Charger

Hyeon-Jun Kwon,

Ku-Yong Kim,

Jun-Ho Kim

Abstract: This paper presents the novel control method of the Four-Switch Buck-Boost (FSBB) converter for Super Capacitor Pre-Charger (SCPC) in hydrogen electric vehicles. The method is proposed to improve voltage regulation characteristic during super capacitor pre-charging, and is able to maintain continuity of duty ratio and voltage conversion ratio. Even though operation mode changes for achieving unity voltage gain, the additional switch state diminishes voltage fluctuation at transition point compared to conventio… Show more

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2024

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Cited by 2 publications

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Adaptive Neural Network Control of Four-Switch Buck–Boost Converters

Ren,

Wang,

Zhang

2024

Actuators

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Based on the adaptive control structure of neural networks, this paper proposes a novel output voltage control strategy for DC converters. The strategy regulates the inductor current to maintain a constant voltage by adjusting the duty cycle of four-switch buck—boost (FSBB) converters. A nonlinear average model for the FSBB converter, derived from its energy consumption, is introduced, and its effectiveness is demonstrated through simulations. The simulations confirm that the FSBB converter enables zero-voltage switching (ZVS) of the four switches across the entire operating voltage range. The comparative simulation results show that the proposed control strategy achieves faster voltage regulation while ensuring ZVS, leading to improved converter performance across the full power range.

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Adaptive Neural Network Control of Four-Switch Buck–Boost Converters

Ren,

Wang,

Zhang

2024

Actuators

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Optimal Control of FSBB Converter with Aquila Optimizer-Based PID Controller

Ren,

Wang,

Zhang

2024

Micromachines

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This paper presents a new methodology for determining the optimal coefficients of a PID controller for a four-switch buck–boost (FSBB) converter. The main objective of this research is to improve the performance of FSBB converters by fine-tuning the parameters of the PID controller using the newly developed Aquila Optimizer (AO). PID controllers are widely recognized for their simple yet effective control in FSBB converters. However, to further improve the efficiency and reliability of the control system, the PID control parameters must be optimized. In this context, the application of the AO algorithm proves to be a significant advance. By optimizing the PID coefficients, the dynamic responsiveness of the system can be improved, thus reducing the response time. In addition, the robustness of the control system is enhanced, which is essential to ensure stable and reliable operation under varying conditions. The use of AOs plays a key role in maintaining system stability and ensuring the proper operation of the control system even under challenging conditions. In order to demonstrate the effectiveness and potential of the proposed method, the performance of the AO-optimized PID controller was compared with that of PID controllers tuned by other optimization algorithms in the same test environment. The results show that the AO outperforms the other optimization algorithms in terms of dynamic response and robustness, thus validating the efficiency and correctness of the proposed method. This work highlights the advantages of using the Aquila Optimizer in the PID tuning of FSBB converters, providing a promising solution for improving system performance.

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A Novel Control Scheme of Four Switch Buck-Boost Converter for Super Capacitor Pre-Charger

Cited by 2 publications

References 17 publications

Adaptive Neural Network Control of Four-Switch Buck–Boost Converters

Adaptive Neural Network Control of Four-Switch Buck–Boost Converters

Optimal Control of FSBB Converter with Aquila Optimizer-Based PID Controller

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