Backstepping Control of High-Frequency Link Matrix Rectifier for Battery Chargers

Song, Jinqiu; Fu, Cheng; Zhang, Guanguan; Duan, Bin; Zhang, Chenghui

doi:10.1109/tpel.2021.3063418

Cited by 20 publications

(5 citation statements)

References 34 publications

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“…According to (2) and (3), the currents i m = i md i mq T flowing through the MDU is not directly regulated by the terminal voltages u e = u ed u eq T , but controlled by the currents i e = i ed i eq T flowing through the EC. Inspired by the idea of backstepping control, in which the complex nonlinear control system is decomposed into several subsystems by introducing virtual control variables [28], [29], the current control of the LCL filter-based EME can also be decomposed into two simple subsystems, one to adjust the currents i m to track the reference ones i * m by accurately calculating the desired currents i * e , and the other to force the currents i e to track the desired by exactly controlling the command voltages u * e . That is to say, the currents i * e are selected as the intermediate control variables.…”

Section: ) Dynamic Equation Of the Interface Filtermentioning

confidence: 99%

“…Therefore, it is necessary to derive the command voltage errors for further analysis. According to (10), the exact command voltages under parameter mismatch can be expressed as (28). Further, the voltage errors can be derived as (29) by substracting (10) from (28).…”

Section: Disturbance Analysis and Suppressionmentioning

confidence: 99%

“…According to (10), the exact command voltages under parameter mismatch can be expressed as (28). Further, the voltage errors can be derived as (29) by substracting (10) from (28). Clearly, the errors can be divided into two parts, one is the error (defined as u il ) caused by the aforementioned disturbance currents (i.e., ∆i * e,k , ∆i e,k+1 and ∆i m,k+1 ), and the other comes from the disturbance voltages (defined as u l ) generated by parameter mismatch (i.e., ∆A e , ∆B e and ∆C e ) alone.…”

Section: Disturbance Analysis and Suppressionmentioning

confidence: 99%

See 2 more Smart Citations

Design and Implementation of LCL Filter-Based Electric Machine Emulator With Disturbance Compensation

Sun

Wang

et al. 2022

IEEE Access

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The electric machine emulator (EME), using digital simulation and power electronics to emulate the characteristics of actual machines, can greatly accelerate the testing of electric drives. However, most existing EMEs are based on typical L filter and linear controller, which causes control conflicts and bandwidth limitation. To address this issue, this paper presents an EME based on LCL filter with passive damping for a three-phase permanent magnet synchronous motor. To improve the dynamic emulating accuracy, a dual closed-loop deadbeat predictive current control algorithm is proposed, which is computationally efficient and easy to implement. The system stability is analyzed in the discrete domain, and the parameter constraints of the filter are obtained. Then, two unknown input observers are designed to compensate for the disturbance currents and voltages caused by modeling errors. Moreover, instead of the empirical method, a theoretical one considering the harmonic suppression, bandwidth, stability and resonance is presented for filter design. Finally, the performance of the proposed EME is validated through simulation and experimental results under various conditions such as machine start-up, torque step change, and speed reversal.INDEX TERMS Electric machine emulator (EME), LCL filter, dual closed-loop deadbeat predictive current control, unknown input observer. NOMENCLATURE

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Section: ) Dynamic Equation Of the Interface Filtermentioning

confidence: 99%

Section: Disturbance Analysis and Suppressionmentioning

confidence: 99%

Section: Disturbance Analysis and Suppressionmentioning

confidence: 99%

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Design and Implementation of LCL Filter-Based Electric Machine Emulator With Disturbance Compensation

Sun

Wang

et al. 2022

IEEE Access

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“…Literature [22] uses large signal analysis modeling and inverse system decoupling control to solve the problem of large voltage disturbance in Buck-Boost converters. Literature [23] proposes high order sliding mode control to maintain the output voltage stability of Buck-Boost converter under large disturbance. DC microgrid is a typical complex nonlinear system, and DAB control strategy which is highly dependent on model knowledge is limited to adapt to scenarios.…”

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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“…In the context of EV chargers, several control approaches have been developed by contemporary researchers such as model predictive control (MPC) [8], sliding mode control (SMC) [9,10], active disturbance rejection control (ADRC) [11], backstepping control [12], and LSTM neural network [13]. However, most the robust controllers need the system model be accurately identified.…”

Section: Introductionmentioning

confidence: 99%

Smart Emergency EV-to-EV Portable Battery Charger

et al. 2022

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With the increase in the number of electric vehicles (EVs) and developments in their related charging infrastructures, consumers have still some concerns about some limiting factors in the EV industry such as battery life, charging station availability, electric grid capacity, limited driving range, and slow charging of batteries. Although some solutions are proposed for these limitations, they are not sufficiently efficient and cost-effective. Moreover, charging of EVs on-the-road is still a challenging issue which requires more innovation. This paper proposes a novel battery charger, known as an Emergency EV-to-EV Portable Battery Charger (EPBC), which provides a cost-effective solution for charging EVs on-the-road in emergency mode. The suggested smart charger can charge another EV based on the state of charge (SOC), capacity, and other important technical specifications of batteries in a safe and reliable manner. The smart charger can regulate the output voltage and the injected current to the EV simultaneously. To realize these features, a model free nonlinear integral backstepping control (MF-NIBC) is adopted to regulate the output voltage of the battery charger. By utilizing the actor and critic networks, a deep deterministic policy gradient (DDPG) is adopted to adjust the MF-NIBC controller. Finally, real-time tests based on the OPAL-RT setup are conducted to confirm the applicability and feasibility of the proposed EV-to-EV portable battery charger.

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Backstepping Control of High-Frequency Link Matrix Rectifier for Battery Chargers

Cited by 20 publications

References 34 publications

Design and Implementation of LCL Filter-Based Electric Machine Emulator With Disturbance Compensation

Design and Implementation of LCL Filter-Based Electric Machine Emulator With Disturbance Compensation

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

Smart Emergency EV-to-EV Portable Battery Charger

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