Output Power Control of an S-S IPT System Based on Voltage and Frequency Tuning for EV Charging

Mostafa, Amr; Wang, Yao; Zhang, Hua; Lu, Fei

doi:10.1109/wow51332.2021.9462886

Cited by 5 publications

(1 citation statement)

References 7 publications

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“…The various elements in the circuit were parameterized in order to guarantee the operation at resonance, which includes the reactive elements of the circuit. Thus, considering that the values of the self-inductance of each coil were measured, Equation ( 2) allows the values of the capacitors to be calculated and guarantees the operation of the circuit in the resonant condition [30][31][32], where f represents the switching frequency of the inverter:…”

Section: Ipt Systemmentioning

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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Section: Ipt Systemmentioning

confidence: 99%