Deep Neural Network Based Inductance Calculations of Wireless Power Transfer Systems

Sato, Kai; Kanamoto, Toshiki; Kudo, Ryotaro; Hachiya, Koutaro; Kurokawa, Atsushi

doi:10.1109/gcce56475.2022.10014111

Cited by 3 publications

(4 citation statements)

References 5 publications

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“…This kind of network is able to predict the inductances of the WPTS by knowing the parameters of the system, i.e., geometrical and material parameters. A similar result is obtained in [24], where a deep NN accepts five structural parameters as the input to estimate the self-and mutual inductances of the coupled coils of a WPTS. In [25], a fully connected neural network is used for estimating the mutual inductance, knowing the distance between the two coils in a WPTS.…”

Section: Introductionsupporting

confidence: 73%

A Deep Learning Approach to Improve the Control of Dynamic Wireless Power Transfer Systems

Bertoluzzo,

Di Barba,

Forzan

et al. 2023

Energies

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In this paper, an innovative approach for the fast estimation of the mutual inductance between transmitting and receiving coils for Dynamic Wireless Power Transfer Systems (DWPTSs) is implemented. To this end, a Convolutional Neural Network (CNN) is used; an image representing the geometry of two coils that are partially misaligned is the input of the CNN, while the output is the corresponding inductance value. Finite Element Analyses are used for the computation of the inductance values needed for CNN training. This way, thanks to a fast and accurate inductance estimated by the CNN, it is possible to properly manage the power converter devoted to charge the battery, avoiding the wind up of its controller when it attempts to transfer power in poor coupling conditions.

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Section: Introductionsupporting

confidence: 73%

A Deep Learning Approach to Improve the Control of Dynamic Wireless Power Transfer Systems

Bertoluzzo,

Di Barba,

Forzan

et al. 2023

Energies

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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%

“…Regarding general estimation, ANNs deliver great performance, encompassing a variety of parameters. In [22], using, once again, information from physical measurements of the coils, an ANN capable of estimating the values of self-and mutual inductances was developed.…”

Section: Mathematical Models Ai Models Referencesmentioning

confidence: 99%

“…With that said, it was decided to implement Multi-Layer Perceptron (MLP), i.e., a fully connected multi-layer neural network [38] in MATLAB ® software R2021b, using the fitrnet function. This AI model has been widely employed in IPT systems [14,15,[21][22][23][24][25]27,[39][40][41][42] due to its high reliability and performance in fulfilling regression tasks.…”

Section: Developed Ann Modelmentioning

confidence: 99%

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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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Neural Network-Based Simulation and Circuit Analysis of Sub-Centimeter Coils for Implantable Medical Devices

Wei,

Wang,

Liu

et al. 2023

2023 IEEE International Conference on Medical Artificial Intelligence (MedAI)

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Deep Neural Network Based Inductance Calculations of Wireless Power Transfer Systems

Cited by 3 publications

References 5 publications

A Deep Learning Approach to Improve the Control of Dynamic Wireless Power Transfer Systems

A Deep Learning Approach to Improve the Control of Dynamic Wireless Power Transfer Systems

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

Neural Network-Based Simulation and Circuit Analysis of Sub-Centimeter Coils for Implantable Medical Devices

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