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一种用于电动汽车充电的具有双输入和双输出的双级高增益转换器。

A dual-stage high-gain converter with dual inputs and dual outputs for electric vehicle charging.

作者信息

Gopalasami Ramanathan, Chokkalingam Bharatiraja, Verma Rajesh, Munda Josiah Lange

机构信息

Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Kattankulathur, 603203, Chennai, Tamil Nadu, India.

Electrical Engineering Department, College of Engineering, King Khalid University, Abha, 61411, Asir, Kingdom of Saudi Arabia.

出版信息

Heliyon. 2024 Sep 19;10(19):e38048. doi: 10.1016/j.heliyon.2024.e38048. eCollection 2024 Oct 15.

DOI:10.1016/j.heliyon.2024.e38048
PMID:39398024
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11467528/
Abstract

The DC-DC converter is an essential subsystem in electric vehicle (EV) chargers, and most converters depend on a single-input single-output structure, which can be costly when multiple charging units are needed. Additionally, these converters offer limited voltage gain, restricting charging configurations. This paper proposed a dual-stage high-gain converter that operates in a boost mode with reduced components, using an inductor, capacitor, and 2-diodes (LC2D) to provide high-gain output. The proposed Dual Inputs and Dual Outputs (DIDO) converter tied with Photovoltaic (PV) and constant DC acting as inputs and two different voltage levels EV chargers are serving as the output. The proposed converter operates at continuous conduction mode (CCM), achieving high efficiency and reliability with fewer losses. The proposed work was designed for 418V and 85V systems in MATLAB/Simulink, and the results were validated with hardware implementation. The proposed converter delivers high-gain output to the electric vehicle application with 92.2 % efficiency.

摘要

DC-DC 转换器是电动汽车 (EV) 充电器中的一个重要子系统，大多数转换器依赖于单输入单输出结构，当需要多个充电单元时，这种结构成本可能很高。此外，这些转换器的电压增益有限，限制了充电配置。本文提出了一种双级高增益转换器，该转换器以降压模式运行，元件数量减少，使用一个电感、一个电容和两个二极管 (LC2D) 来提供高增益输出。所提出的双输入双输出 (DIDO) 转换器与光伏 (PV) 相连，恒定直流作为输入，两个不同电压等级的 EV 充电器作为输出。所提出的转换器在连续导通模式 (CCM) 下运行，具有较少的损耗，实现了高效率和高可靠性。所提出的工作在 MATLAB/Simulink 中针对 418V 和 85V 系统进行了设计，并通过硬件实现对结果进行了验证。所提出的转换器以 92.2% 的效率为电动汽车应用提供高增益输出。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e62e/11467528/798e3ba6b6ba/gr20.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e62e/11467528/798e3ba6b6ba/gr20.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e62e/11467528/01ef724feada/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e62e/11467528/09308e9d2bd5/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e62e/11467528/ccdcc27d7331/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e62e/11467528/d3f810c52350/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e62e/11467528/8eba77de2871/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e62e/11467528/ba3804676263/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e62e/11467528/89f5a124abb5/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e62e/11467528/fbd033360cef/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e62e/11467528/8c050b096863/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e62e/11467528/fdb663657e7c/gr10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e62e/11467528/cef95b1c5562/gr11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e62e/11467528/52baea6a73a6/gr12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e62e/11467528/c30513425f52/gr13.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e62e/11467528/32701c6b7ad6/gr14.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e62e/11467528/d91134848a72/gr15.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e62e/11467528/710bf2cf663b/gr16.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e62e/11467528/ededf05c28de/gr17.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e62e/11467528/5ad308499edc/gr18.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e62e/11467528/7d76d6641c1e/gr19.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e62e/11467528/798e3ba6b6ba/gr20.jpg

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本文引用的文献

1
Investigating the input power quality of multi-pulse AC-DC power converter fed induction motor drives.研究多脉冲交-直流电力变换器供电的感应电机驱动系统的输入电能质量。
Heliyon. 2022 Nov 29;8(12):e11733. doi: 10.1016/j.heliyon.2022.e11733. eCollection 2022 Dec.
2
Model predictive control of DC/DC boost converter with reinforcement learning.基于强化学习的DC/DC升压变换器模型预测控制
Heliyon. 2022 Nov 5;8(11):e11416. doi: 10.1016/j.heliyon.2022.e11416. eCollection 2022 Nov.