Secondary-side-only simultaneous power and efficiency control by online mutual inductance estimation for dynamic wireless power transfer

Lovison, Giorgio; Imura, Takehiro; Hori, Yoichi

doi:10.1109/iecon.2016.7793790

Cited by 12 publications

(4 citation statements)

References 16 publications

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“…Parameter estimation Accuracy Calculation time [8,13] Dependency on structural parameters [6][7][8][9][10][14][15][16][17][18][19] In order to build a foundation for the solutions available concerning parameter estimation in IPT systems using AI models, a literature review is presented, delving into the significant advancements made in leveraging AI in IPT systems and showcasing its potential across various domains. In this regard, to the best of the knowledge acquired, different AI techniques, including long short-term memory (LSTM), random forest (RF), decision trees (DTs), Adabooster with DT, eXtreme Gradient Boosting (XGBoost), random forest regression (RFR) and support vector machines (SVMs), have been implemented to estimate the parameters of IPT systems, as depicted in Table 2.…”

Section: Mathematical Models Ai Models Referencesmentioning

confidence: 99%

“…Traditionally, this estimation process has been carried out using mathematical models that combine electrical variable information applied primarily in dynamic IPT systems, i.e., in systems where the charging process of EV batteries occurs while the vehicle is moving [3][4][5]. To accomplish this, the developed models are based on the acknowledgment of various variables on the transmitter and receiver sides, including their structural parameters [6][7][8][9][10]. Hence, it is evident that these models require a substantial amount of information about the IPT system, and since the relationships between the parameters are significantly nonlinear, this will lead to high computational costs and long calculation times.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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: Mathematical Models Ai Models Referencesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Abrantes,

Costa,

Perdigão

et al. 2024

Energies

View full text Add to dashboard Cite

show abstract

“…The fundamental resonant angular frequency is selected to be ωo. The input impedance of DWPT, Zin, is expressed in (14) and the output to input voltage gain can be derived result in (15). The plots of the gain curve for various loads and mutual inductances in The current of the primary coil can be obtained by applying Kirchhoff's law as shown in (10).…”

Section: Analysis and Characteristic Of Lcc-s Dwptmentioning

confidence: 99%

“…Some developments in DWPT have been published in the recent years. In [14,15] the authors focused on the control method of the DWPT and used the Series-Series (SS) compensation in their studies. The SS compensation has a simple topology, an independent load, and against frequency.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Wireless Power Transfer for Logistic Robots

et al. 2018

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The prospect of using robots in warehouses or supply chain processes is increasing due to the growth of the online retail market. This logistic robot is available in the market and uses a battery as energy storage device. However, this battery is large and heavy. Therefore, it needs a long recharging time. Dynamic Wireless Power Transfer (DWPT) can be an alternative to the conventional charging system because of its safety and flexibility that enables in motion charging. DWPT reduces the battery requirement size and capacity. Hence the stored energy can be used effectively for load transportation. A compensation with an inductor and two capacitors in the transmitter side, and a series connected capacitor in the receiver side which is named LCC-S compensation type has the capability to maintain the transmitter current with a fixed frequency operation. It provides less variation of the output voltage in response to the load variation. Moreover, the compensation of the receiver side uses only a single series capacitor which is low-cost. The analysis, modeling, and design procedures are discussed in this paper as well as the hardware implementation and verification of a 1.5 kW maximum power DWPT. The experiment shows the capability of the proposed system and shows maximum efficiency can reach 91.02%.

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A practical static simulator for dynamic wireless charging of electric vehicle using receiver open circuit voltage equivalent

Song

Zhang

Zhu

et al. 2017

2017 IEEE Energy Conversion Congress and Exposition (ECCE)

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Secondary-side-only simultaneous power and efficiency control by online mutual inductance estimation for dynamic wireless power transfer

Cited by 12 publications

References 16 publications

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

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

Dynamic Wireless Power Transfer for Logistic Robots

A practical static simulator for dynamic wireless charging of electric vehicle using receiver open circuit voltage equivalent

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