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<!doctype html>
<html>
<head>
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<meta charset="utf-8">
<title> Active Battery Health Management for Structural Health Monitoring </title>
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<meta name="author" content="Information about the author here">
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<h1> Active Battery Health Management for Structural Health Monitoring </h1>
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<ul class="main-menu">
<li><a href="#Home">Home</a></li>
<li><a href="#People">People</a></li>
<li><a href="#Publications">Publications</a></li>
</ul>
</header>
<div id="container">
<div class="inner">
<div id="content">
<div id="Home" class="content-region hide">
<h2> Project Summary </h2>
<p>
The Internet of Things (IoT) has become one of the major future trends. Its proliferation has led to an explosive number of battery powered wireless devices. Battery life has always been one of the biggest limiting factors of those wireless devices which poses different challenges thourghout their operation.
</p>
<p>
This project deals with battery health management under battery group replacement requirement. In practice, there are many applications where IoT wireless devices (sensors in particular) are installed at hard-to-reach areas or remote locations, where the planning and logistics of a maintennace trip can be very costly. To reduce the cost, battery group replacement is often required in these scenarios, i.e. replace all batteries in a sensor network on a maintenance trip, instead of replacing just one or a few. Unfortunately, existing methods for battery management exclusively aim to extend lifetime of individual batteries as much as possible, lacking a system level view. A consequence of applying such algorithms is that batteries in a sensor network tend to fail at very different times, posing significant difficulty on planning and scheduling of group replacement activities.
</p>
<p>
The research objective of this project is to develop an active battery health management framework such that batteries in a sensor network will degrade more uniformly to achieve similar end-of-life times, which will significantly simplify the implementation of a group battery replacement policy. In addition, it will effectively extend lifetime of batteries as a group, as it reduces early failures of the battery individuals that cripples the sensor network and triggers a group replacement request. The controlled battery health degradation processes are made possible by dynamically adjusting the quality of service levels assigned to wireless nodes, based on the predicted battery remaining useful lifetime as a measure of battery health. The success of this project will significantly reduce the overall battery maintenance cost.
</p>
</div>
<div id="People" class="content-region hide">
<h2>Leaders</h2>
<p>
1. Dr. Hongki Jo (PI) <[email protected]>
Associate Professor, Department of Civil Engineering
University of Arizona,Tucson
</p>
<p>
2. Dr. Jian Liu (Co-PI) <[email protected]>
Associate Professor, Department of Systems and Industrial Engineering
University of Arizona, Tucson
</p>
<h2>Contributors</h2>
<p>
1. Jong-Hyun Jeong <[email protected]>
CAEM Department, University of Arizona, Tucson
</p>
<p>
2. Tahsin Afroz Hoque Nishat <[email protected]>
PhD student in the CAEM Department, University of Arizona, Tucson
</p>
</div>
<div id="Publications" class="content-region hide">
<h2>Articles</h2>
<p>
N/A
</p>
</div>
</div>
</div>
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<footer>
University of Arizona, Tucson, USA
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