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

Autor: Gonçalo C. Abrantes, Valter S. Costa, Marina S. Perdigão, Sérgio Cruz
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Energies, Vol 17, Iss 7, p 1615 (2024)
Druh dokumentu: article
ISSN: 17071615
1996-1073
DOI: 10.3390/en17071615
Popis: 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.
Databáze: Directory of Open Access Journals
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