Maximum efficiency tracking design of wireless power transmission system based on machine learning

Huang, Zhiyong; Guan, Tianhui; Wang, Zhiming; Wang, Jie; Wang, Shiwei; Liu, Man; Sun, Dihua; Zeng, Xiaoping

doi:10.1016/j.egyr.2022.10.139

Cited by 4 publications

(2 citation statements)

References 9 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%

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%

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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“…Research about Rx-side sensorless WPT systems has been undertaken in [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. The study in [ 15 ] proposes a machine learning (ML) assisted method that estimates the power delivered to the Rx by using only measurements at the Tx-side.…”

Section: Introductionmentioning

confidence: 99%

Predicting Receiver Characteristics without Sensors in an LC–LC Tuned Wireless Power Transfer System Using Machine Learning

Kim,

Niada,

Park

2024

Sensors

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Improvement of wireless power transfer (WPT) systems is necessary to tackle issues of power transfer efficiency, high costs due to sensor and communication requirements between the transmitter (Tx) and receiver (Rx), and maintenance problems. Analytical techniques and hardware-based synchronization research for Rx-sensorless WPT may not always have been available or accurate. To address these limitations, researchers have recently employed machine learning (ML) to improve efficiency and accuracy. The objective of this work was to replace Tx–Rx communication with ML, utilizing Tx-side parameters to predict the load and coupling coefficients on an LC–LC tuned WPT system. Based on current and voltage features collected on the Tx-side for various load and coupling coefficient values, we developed two models for each load and coupling prediction. This study demonstrated that the extra trees regressor effectively predicted the characteristics of LC–LC tuned WPT systems, with coefficients of determination of 0.967 and 0.996 for load and coupling, respectively. Additionally, the mean absolute percentage errors were 0.11% and 0.017%.

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A WPT System Applied to Space Solar Arrays MPPT/Constant Voltage Mode Control Strategy

Liu²,

Qiao

et al. 2023

2023 IEEE International Conference on Control, Electronics and Computer Technology (ICCECT)

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Maximum efficiency tracking design of wireless power transmission system based on machine learning

Cited by 4 publications

References 9 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

Predicting Receiver Characteristics without Sensors in an LC–LC Tuned Wireless Power Transfer System Using Machine Learning

A WPT System Applied to Space Solar Arrays MPPT/Constant Voltage Mode Control Strategy

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