[1]周坤雨,岳宁宁,邱柯妮.自供能系统中可配置DC-DC转换器的设计[J].郑州大学学报(工学版),2021,42(04):70-76.
 A Reconfigurable DC-DC Converter Design for Energy-harvesting System[J].Journal of Zhengzhou University (Engineering Science),2021,42(04):70-76.
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自供能系统中可配置DC-DC转换器的设计()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
42
期数:
2021年04期
页码:
70-76
栏目:
出版日期:
2021-07-30

文章信息/Info

Title:
A Reconfigurable DC-DC Converter Design for Energy-harvesting System
作者:
周坤雨岳宁宁邱柯妮
文献标志码:
A
摘要:
从环境中获取能量的自供能系统相比传统电池供电的系统具有绿色经济、无需更换/维护电池充电等优势,是物联网领域发展的一个热点方向。自供能系统中常常采用“收集-存储-使用”的能量采集架构为系统供能。该采集架构中储能电容和负载之间需要DC-DC转换器来传递能量,DC-DC转换器的转换效率直接影响着整个系统的性能和能效,因此如何平衡储能电容放电电压和负载之间的关系以实现较高的能量转换效率是一个关键问题。本文提出一种自供能存算一体系统中的动态可配置DC-DC转换器设计,对基于RRAM交叉阵列的加速器负载进行实验,分析了储能电容放电电压和负载与DC-DC转换效率的量化关系,该可配置DC-DC转换器最高转换效率可达87.93%,平均转换效率可达78.49%。本文提出的可配置DC-DC转换器可为自适应负载调度优化研究提供理论依据。
Abstract:
Compared with a traditional battery-powered system, an energy harvesting system that harvests energy from environment has the advantages of green economy, with no necessity of replacing or recharging betteries, etc. This is becoming a hot research topic in the field of Internet of Things(IOT). In the typical adopted "harvest-store-use" architecture, a DC-DC converter, between the storage capacitor and the load is essential to energy conversion efficiency, and further impact the performance and energy efficiency of the entire system. So it is a key issue to balance the relationship of the discharge voltage of the storage capacitor and the load in order to achieve high energy conversion efficiency. To address this issue, this paper proposes a reconfigurable DC-DC converter design for energy-harvesting computing-in-memory (CIM) system. ba<x>sed on experiments on the accelerator load which is on top of RRAM crossbar, we quantitatively analyze the relationship between the discharge voltage of capacitor as well as the RRAM load and the DC-DC conversion efficiency. The experimental results show that the maximum conversion efficiency and average conversion efficiency of the proposed reconfigurable DC-DC converter can reach 87.93% and 78.49%, respectively, providing a theoretical basis for future optimizations on adaptive load schedule.
更新日期/Last Update: 2021-08-26