Data-Driven Modeling of Wireless Power Transfer Systems With Slowly Time-Varying Parameters

Chen, Fengwei; Padilla, Arturo; Young, Peter C.; Garnier, Hugues

doi:10.1109/tpel.2020.2986224

Cited by 22 publications

(4 citation statements)

References 27 publications

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“…For example, the order of the model of a resonant tank with four energy-storage elements (two inductors and two capacitors for series-series topology) is eight. This problem leads to complicated model formulae, high computational cost, and difficulties in data-driven identification [16,17].…”

Section: Introductionmentioning

confidence: 99%

Wireless Power Transfer System Model Reduction with Split Frequency Matching

Wang,

Wu,

Peng

et al. 2024

Electronics

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Reduced-order dynamic models of wireless power transfer (WPT) systems are desired to simplify the analysis and design of power control, phase synchronization, and maximum efficiency tracking. The reduced-order dynamic phasor model is a good choice because of its straightforward physical meaning and concise mathematical formula. However, the model relies on the assumption of loose coupling and loses accuracy when the coupling becomes stronger. In this paper, a model reduction method with split frequency matching is proposed to improve model accuracy under relatively strong coupling conditions, which is suitable for most short-distance WPT applications, such as wireless electrical vehicle charging. Split frequency matching is achieved through a pair of conjugate equivalent mutual inductances, which are derived from the asymmetry characteristics of the full-order dynamic phasor model in the positive and negative frequency domains. The proposed model retains the advantages of the existing model while significantly improving the accuracy under strong coupling conditions. Its characteristics are verified by comparing the experimental results and model predictions under both large step changes and small-signal perturbations.

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Section: Introductionmentioning

confidence: 99%

Wireless Power Transfer System Model Reduction with Split Frequency Matching

Wang,

Wu,

Peng

et al. 2024

Electronics

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“…Wireless communication is a standard means of transmitting information by WPT [5], [6], [7], [8]. The Bluetooth module enables information interaction, status monitoring, The associate editor coordinating the review of this manuscript and approving it for publication was Alon Kuperman .…”

Section: Introductionmentioning

confidence: 99%

A Digital Twin Based Real-Time Parameter Identification for Mutual Inductance and Load of Wireless Power Transfer Systems

2023

IEEE Access

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The transmitter and receiver for wireless power transfer (WPT) need information interaction to achieve optimal efficiency if the mutual inductance and load are movable. This paper proposes a receiver parameter estimation system based on the digital-twin concept aiming for condition monitoring of WPT. The digital replica and physical experimental demonstrate an application for a series-series WPT system. A hybrid optimization algorithm based on Particle Swarm Optimization and Simulated Annealing (PSO-SA) is applied to estimate mutual inductance and load based on the voltage and current of the transmitting coil from both the digital twin and the physical prototype. Compared with the conventional methods, the digital twin is real-time, without auxiliary circuits and invasion. In a validation experiment, the estimated parameters fit well with the actual values, verifying the accuracy of the proposed approach in comprehensively describing the state of the receiver. The outcomes of this study serve as a critical step for achieving noncommunication, cost-effectiveness, and power control for WPT.INDEX TERMS Digital twin, parameter monitoring, particle swarm optimization and simulated annealing (PSO-SA), wireless power transfer (WPT).

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“…Data-driven methods, including neural networks, logistic regression, genetic algorithms, Bayesian methods, support vector machines, and other machine learning methods, are extensively used in engineering practices. [23][24][25][26][27][28] For example, Landi et al proposed a back-propagation neural network (BPNN) to estimate the SOC. The BPNN combined with the extended Kalman filter (EKF), can get more accurate SOC estimates.…”

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confidence: 99%

The Elastic Net Regularized Extreme Learning Machine for State of Charge Estimation

Wang,

Chen,

Liu

2023

J. Electrochem. Soc.

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The state of charge (SOC) is a crucial component of battery management, and the reliability of its assessment is crucial for predicting battery life. In this paper, an elastic net regularized extreme learning machine is developed for SOC estimation. Unlike traditional neural networks, the extreme learning machine does not require updating all the weights/parameters of the network to obtain accurate SOC estimates, thus, it has a simpler structure. In addition, the elastic net regularization combining the L1 regularization and L2 regularization, can better describe the relationship between current, voltage and SOC. The simulation results show the effectiveness of the proposed method.

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Data-Driven Modeling of Wireless Power Transfer Systems With Slowly Time-Varying Parameters

Cited by 22 publications

References 27 publications

Wireless Power Transfer System Model Reduction with Split Frequency Matching

Wireless Power Transfer System Model Reduction with Split Frequency Matching

A Digital Twin Based Real-Time Parameter Identification for Mutual Inductance and Load of Wireless Power Transfer Systems

The Elastic Net Regularized Extreme Learning Machine for State of Charge Estimation

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