A Reconfigurable IPT System with CC-CV Output Characteristics and High Misalignment Tolerance
INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS(2024)
Xian Technol Univ
Abstract
Misalignment between transmission coils is an inevitable challenge in inductive power transfer (IPT) systems, particularly affecting the constant current (CC) and constant voltage (CV) outputs essential for battery charging. A reconfigurable IPT system with high misalignment tolerance and CC-CV output characteristics is proposed in this article. The main circuit of the system is composed of a LCC-LCC and LCC-S topology with two intermediate coils, and two switches equipped with single unidirectionally blocking MOSFETs are incorporated on the secondary side to transition from CC to CV mode. A comprehensive analysis of the output characteristics for the proposed hybrid topology in both CC and CV modes is provided. Furthermore, the parameter optimization design based on BP coil for wide misalignment tolerance is presented. The experimental results demonstrate that the proposed reconfigurable IPT system can maintain stable CC and CV outputs within 50% lateral and vertical offset (the coupling factor varies from 0.1 to 0.21), when the load varies from 11 to 80 Omega, and the output current and voltage fluctuations remain below 5%. A reconfigurable IPT system with high misalignment tolerance and CC-CV output characteristics is proposed in this article. The experimental results demonstrate that the proposed reconfigurable IPT system can maintain stable CC and CV outputs within 50% lateral and vertical offset (the coupling factor varies from 0.1 to 0.21), when the load varies from 11 to 80 Omega, and the output current and voltage fluctuations remain below 5%.image
MoreTranslated text
Key words
battery charging,CC-CV output,inductive power transfer,misalignment tolerance
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined