Implementation of Constant Current or Constant Voltage Output Based on Switching Circuit for Battery Charging Applications
2021 IEEE 5th Conference on Energy Internet and Energy System Integration (EI2)(2021)
Taiyuan University of Science and Technology
Abstract
The charging technology based on wireless power transmission technology has a good application prospect. Due to the real-time change of the battery equivalent load and the relative position of the charging coils on both sides, the output current and voltage of the wireless charging system will change, which can not meet the requirements of the constant current or constant voltage charging for the lithium battery. The compensation topology on both sides of the wireless charging system has a decisive impact on the constant current or constant voltage output of the system, but the existing compensation topology has some problems, such as the constant current or constant voltage output and the zero phase of the input impedance can not be realized at the same time, and the constant current or constant voltage output parameters change with the mutual inductance of the coupling coils; In this paper, a hybrid compensation topology based on switching circuit is proposed. Through the topology switching on the secondary side, the constant current or constant voltage output can be realized when the load changes, the zero phase input impedance can be realized in both working modes, and the parameters of the two working modes are independent of the mutual inductance of the coupling coils; The constant current or constant voltage output of wireless charging system can also be realized by phase-shifting control on the primary side when the mutual inductance changes. The simulation results show that the proposed hybrid compensation topology can meet the design requirements.
MoreTranslated text
Key words
constant current or constant voltage,switching circuit,Battery Charging,Phase shift control
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