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            <name>Title</name>
            <description>A name given to the resource</description>
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                <text>Faculty Publications</text>
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    <name>Conference Paper</name>
    <description>Faculty Publications- Conference Papers</description>
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          <name>Creator</name>
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              <text>Uthaman, Ujwal; Dsilva, Alan Chris; Johny, Danny; Parag Jose, C.; Venkataswamy, R.; Wang, Yong</text>
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          <name>Title</name>
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              <text>Digital Twinning of an Electric Two-Wheeler for Real-Time Battery State Estimation and Augmented Reality Visualization</text>
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          <name>Date</name>
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              <text>01-01-2025</text>
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          <name>Source</name>
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              <text>2025 IEEE International Conference on Vehicular Technology and Transportation System, ICVTTS 2025;</text>
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              <text>&lt;a href="https://doi.org/10.1109/ICVTTS67119.2025.11296457" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICVTTS67119.2025.11296457&lt;/a&gt; &lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/pages/publications/105032070394?origin=resultslist" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/pages/publications/105032070394?origin=resultslist&lt;/a&gt;</text>
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              <text>Uthaman U., CHRIST (Deemed to be University), Karnataka, Bangalore, India; Dsilva A.C., CHRIST (Deemed to be University), Karnataka, Bangalore, India; Johny D., CHRIST (Deemed to be University), Karnataka, Bangalore, India; Parag Jose C., CHRIST (Deemed to be University), Karnataka, Bangalore, India; Venkataswamy R., CHRIST (Deemed to be University), Karnataka, Bangalore, India; Wang Y., Binghamton University, New York, United States</text>
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              <text>This paper describes the design and structure of a digital twin system for an electric two-wheeler. It enables real-time battery state estimation and augmented reality visualization. Using digital twin technology, the system improves battery monitoring, rider experience, and operational efficiency through real-time simulation and optimization. The structure includes a physical sensing layer, an ESP32-based processing unit, cloud-based data management using ThingSpeak, and an AR front-end. It tackles key challenges in estimating the state of LiFePO4 batteries, such as voltage plateau behavior, temperature sensitivity, and accuracy of algorithms. The system also considers the limits of microcontroller-based sensing and data interfacing, ensuring reliable performance in real-time. 2025 IEEE.</text>
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          <name>Subject</name>
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              <text>Augmented Reality; battery management system; Digital twin; electric two-wheeler; ESP32; Internet of Things; LiFePO4 battery; Model Viewer; range estimation; State of Charge; State of Health; ThingSpeak</text>
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          <name>Publisher</name>
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              <text>Institute of Electrical and Electronics Engineers Inc.</text>
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              <text>ISBN: 979-833156522-0;</text>
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              <text>English</text>
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              <text>Restricted Access; Hardcopy may be available in the library</text>
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              <text>online</text>
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