Dynamic Improvement of Inductive Power Transfer Systems With Maximum Energy Efficiency Tracking Using Model Predictive Control: Analysis and Experimental Verification

Liu, Shunpan; Mai, Ruikun; Zhou, Lingyun; Li, Yong; Hu, Jiefeng; He, Zhengyou; Yan, Zhaotian; Wang, Shiqi

doi:10.1109/tpel.2020.2992517

Cited by 44 publications

(13 citation statements)

References 46 publications

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“…By using ( 9), the controller evaluates the duty cycle in set ( 11) and identifies the one that minimizes the cost function (10). Here, the controller has a choice of evaluating all or some of the duty cycle values in set (11). However, considering the computational burden involved, in practice only a subset of ( 11) is evaluated by MDCS-MPC in each time step, and the duty cycle that gives rise to the smallest cost function is applied to the IPT system in the next time step.…”

Section: Moving Discretized Control Set Model Predictive Controlmentioning

confidence: 99%

“…It thus follows that MDCS-MPC typically does not perform well under large step disturbances. On the contrary, the proposed group-based optimization model predictive control (GBO-MPC) obtains a close approximation to the global optimum duty cycle by evaluating a reduced and fixed number of points in set (11) regardless of the initial and final states of the system. The following example illustrates the operating principle of the proposed GBO-MPC.…”

Section: Group-based Optimization Model Predictive Controlmentioning

confidence: 99%

“…Frequent power oscillations in IPT systems will give rise to electromagnetic interference problems, which can degrade the operation and performance of motors, batteries and other electrical equipment connected to it. In addition, poor dynamic performance will also degrade system efficiency, shorten the service lifetime of the connected electrical equipment and even damage them [11]. Therefore, it is of great importance to improve the dynamic performance of IPT systems.…”

Section: Introductionmentioning

confidence: 99%

“…It offers several advantages, such as flexibility to incorporate multiple control variables and control objectives, and suitability for application in nonlinear and constrained systems. By solving the rolling optimization problem, FCS-MPC is used in [11] and [23] for achieving fast dynamic response in BIPT systems. A large number of calculations are needed in each control interval, which incurs large computational burden.…”

Section: Introductionmentioning

confidence: 99%

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A computationally efficient hybrid optimization‐based model predictive control for inductive power transfer systems

Cao

Sun

et al. 2022

IET Power Electronics

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Model predictive control (MPC) has been actively researched in recent years for power electronics applications due to its intuitive concept, flexibility, and superior dynamic performance. However, current research on MPC for inductive power transfer (IPT) systems is rather limited. Several emerging applications such as dynamic electric vehicle charging, vehicle-to-grid services, and ad hoc power transfer between mobile devices require IPT systems to possess fast dynamic response characteristic. Here, a new MPC method based on computationally efficient hybrid optimization scheme is proposed to meet the needs of these applications. The proposed MPC method adaptively selects the moving discretized control set-based optimization or the newly proposed group-based optimization under different system's states to minimize the number of iterations required for determining the optimum control variable, thus offering the advantage of low computational burden. The paper also proposes a new prediction error compensation scheme that effectively improves the control accuracy of the proposed MPC method. All the proposed works are experimentally verified on a laboratory prototype.

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Section: Moving Discretized Control Set Model Predictive Controlmentioning

confidence: 99%

Section: Group-based Optimization Model Predictive Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A computationally efficient hybrid optimization‐based model predictive control for inductive power transfer systems

Cao

Sun

et al. 2022

IET Power Electronics

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show abstract

“…Several user-end control methods have been proposed, which can be classified into "Communication-based" and "Communication-free" manners [17,[23][24][25][26][27][28][29][30]. These methods are usually designed for primary-side inverters only, or both for front-end inverters and user-side buck converters.…”

Section: Introductionmentioning

confidence: 99%

Stability Improvement of Dynamic EV Wireless Charging System with Receiver-Side Control Considering Coupling Disturbance

et al. 2021

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Receiver-side control has been a reliable practice for regulating the transferred energy to the batteries in the electric vehicle (EV) wireless power transfer (WPT) systems. Nonetheless, the unpredictable fluctuation of the mutual inductance in dynamic wireless charging brings extreme instability to the charging process. This overshoot that appears in instant vibrations may largely increase the voltage/current stress of the system, and even cause catastrophic failure in the battery load. In addition, the speed of the vehicles may lead to untraceable steady-state operation. However, existing solutions to the above two issues suffer from either long communication time delay or significantly compromised output regulation. In this paper, the slow dynamics and the overshoot issues of the WPT system are elaborated in theory, and the small-signal model mainly considering mutual inductance disturbance is established. A simple feedforward control is proposed for overshoot damping and fast system dynamics. Experimental results validate that the overshoot can be reduced by 13% and the settling time is improved by 50% in vehicle braking or acceleration. In constant speed driving, the battery charging ripple is decreased by 12% and ensures better system stability.

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A hybrid approach for fault location in power distributed networks: Impedance-based and machine learning technique

Tavoosi

Shirkhani

Azizi

et al. 2022

Electric Power Systems Research

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Dynamic Improvement of Inductive Power Transfer Systems With Maximum Energy Efficiency Tracking Using Model Predictive Control: Analysis and Experimental Verification

Cited by 44 publications

References 46 publications

A computationally efficient hybrid optimization‐based model predictive control for inductive power transfer systems

A computationally efficient hybrid optimization‐based model predictive control for inductive power transfer systems

Stability Improvement of Dynamic EV Wireless Charging System with Receiver-Side Control Considering Coupling Disturbance

A hybrid approach for fault location in power distributed networks: Impedance-based and machine learning technique

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