A Reflected Impedance Estimation Technique for Inductive Power Transfer

Lan, Lingxin; Arteaga, Juan M.; Yates, David C.; Mitcheson, Paul D.

doi:10.1109/wow45936.2019.9030654

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…This section summarises the design and construction of the experimental-rig to estimate the reflected impedance of an inductively coupled receiver to a known transmitter. This tool was constructed based on our previous works [12], [13].…”

Section: Construction Of An Experimental Impedance Estimation Toolmentioning

confidence: 99%

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Characterisation of High Frequency Inductive Power Transfer Receivers Using Pattern Recognition on the Transmit Side Waveforms

Arteaga

Lan

Kwan

et al. 2020

2020 IEEE Applied Power Electronics Conference and Exposition (APEC)

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This paper demonstrates the characterisation of inductively coupled receivers for high frequency inductive power transfer (HF-IPT) systems using pattern recognition on the inverter waveforms at the transmit side. The impedance reflected by the candidate receivers to the transmit coil was estimated using a model programmed to associate the experimental drain-voltage waveforms of the inverter when it drives a receiver under test to those when driving known loads. The necessity of employing this technique is due to the difficulty of accurately measuring current and voltage across the coil given the parasitic effects of probing and the precise skewing required to measure an impedance, especially at high Q-factor. The proposed technique is convenient for characterising and comparing the impedance reflected by candidate receivers for a particular application where there is a choice to be made with respect to the rectifier topologies and semiconductor technologies. Experimental results, using a 13.56 MHz 100 W inductive power transfer system, were obtained for a full-wave Class D rectifier using silicon (Si) and silicon carbide (SiC) Schottky diodes, and two Class E rectifiers using SiC diodes.

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Section: Construction Of An Experimental Impedance Estimation Toolmentioning

confidence: 99%

“…In this work we implement an alternative to estimate Z eq (introduced in our previous work [12]) where the variable to observe is the inverter transistor drain voltage (V ds ). V ds is easier to measure since the inverter topology proposed (Class EF) has a capacitance across the switch.…”

Section: Introductionmentioning

confidence: 99%

Characterisation of High Frequency Inductive Power Transfer Receivers Using Pattern Recognition on the Transmit Side Waveforms

Arteaga

Lan

Kwan

et al. 2020

2020 IEEE Applied Power Electronics Conference and Exposition (APEC)

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“…In the final step, based on the results of the second step, adjustments are made to the parameterization of the ANN, including the number of neurons per hidden layer, as well as the size of the training dataset. Typically, for regression tasks [17,22,[43][44][45], the metrics that are commonly used are MAE, MAPE and R 2 . Therefore, these metrics were selected to evaluate the overall performance of the network.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, these metrics were selected to evaluate the overall performance of the network. Nevertheless, the R 2 metric was the main reference since it has been recurrently and independently used to accurately evaluate the performance of regression models [37,[43][44][45]. Furthermore, to better understand the improvements applied to the network, Table 4 provides a compilation of the various stages involved in the training and tuning of the network.…”

Section: Methodsmentioning

confidence: 99%

Mutual Inductance Estimation Using an ANN for Inductive Power Transfer in EV Charging Applications

Abrantes,

Costa,

Perdigão

et al. 2024

Energies

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In the context of inductive power transfer (IPT) for electric vehicle (EV) charging, the precise determination of the mutual inductance between the magnetic pads is of critical importance. The value of this inductance varies depending on the EV positioning, affecting the power transfer capability. Therefore, the precise determination of its value yields various advantages, particularly by contributing to the optimization of the charging process of the EV batteries, since it offers the possibility of adjusting the position of the vehicle depending on the level of misalignment. Within this framework, algorithms grounded in artificial intelligence (AI) techniques emerge as promising solutions. This research work revolves around the estimation of the mutual inductance in a wireless inductive power transfer system using a resonant converter topology, implemented in MATLAB/Simulink® R2021b. The system output was developed to emulate the behavior of a battery charger. To estimate this parameter, an artificial neural network (ANN) was developed. Given the characteristics of the system, the features were chosen in a way that they could provide a clear indication to the ANN if the vehicle position changed, independently of the charging power. In the pursuit of creating a robust AI model, the training dataset contained approximately 1% of the available data. Upon the analysis of the results, it was verified that the largest estimation error observed was around 3%, occurring at the lowest charging power considered. Hence, it can be inferred that the proposed ANN exhibits the capability to accurately estimate the value of mutual inductance in this type of system.

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Load Characterization in High-Frequency IPT Systems Using Class EF Switching Waveforms

Arteaga

Pucci

Lan

et al. 2021

IEEE Trans. Power Electron.

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Large magnetic field volumes associated with coreless high frequency inductive power transfer (HF-IPT) systems allow multiple receivers to be powered from one transmitter, but also provide greater probability for foreign objects to couple to the system. Knowledge of the types of objects (legitimate receivers or otherwise) that are coupled to the transmitter is critical. Such knowledge on the transmit side would allow the system to be deactivated in the presence of foreign objects, and to determine the exact state of tuning of the receivers so that it may adjust itself accordingly to optimise system performance. This paper introduces a technique to calculate the induced voltage generated by coupled receivers and foreign objects on the transmit coil in real-time. Changes in the position or electrical quantities of the receivers, and foreign objects, alter the induced voltage on the transmit coil, and with it the trajectory of the switching waveforms of the inverter driving the transmit coil. From the shape of these waveforms, information on the phase and amplitude of the induced voltage can be extracted, thus enabling the induced voltage on the primary to be estimated with a single, easy to access, voltage measurement, which is easier than estimating the induced voltage from measurements of coil current and total coil voltage. We used a support-vector-machine (SVM) to perform regression analysis on the drain voltage data. The experimental setup uses a 100 W, 13.56 MHz Class EF inverter, and the model was generated from a large number of samples of the drain voltage waveforms operating at different known loads. These were generated from our in-house HF-IPT test load, which uses a Class EF synchronous rectifier. The results allow the induced voltage on the transmit coil to be estimated in real time from the drain voltage waveform alone, with a normalised root mean square error of 1.1 % for the real part (reflected resistance) and 1.2 % for the imaginary part (reflected reactance).This paper is accompanied by a video file demonstrating the experiments.

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A Reflected Impedance Estimation Technique for Inductive Power Transfer

Cited by 6 publications

References 13 publications

Characterisation of High Frequency Inductive Power Transfer Receivers Using Pattern Recognition on the Transmit Side Waveforms

Characterisation of High Frequency Inductive Power Transfer Receivers Using Pattern Recognition on the Transmit Side Waveforms

Mutual Inductance Estimation Using an ANN for Inductive Power Transfer in EV Charging Applications

Load Characterization in High-Frequency IPT Systems Using Class EF Switching Waveforms

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