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Maximum Power Transfer01:16

Maximum Power Transfer

458
Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
By substituting the entire circuit with...
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Batteries and Fuel Cells03:12

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A battery is a galvanic cell that is used as a source of electrical power for specific applications. Modern batteries exist in a multitude of forms to accommodate various applications, from tiny button batteries such as those that power wristwatches to the very large batteries used to supply backup energy to municipal power grids. Some batteries are designed for single-use applications and cannot be recharged (primary cells), while others are based on conveniently reversible cell reactions that...
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Node Analysis for AC Circuits01:14

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Consider an angioplasty system featuring a catheter equipped with a turbine, a critical tool for removing plaque deposits from coronary arteries. This intricate medical device operates using a circuit model reminiscent of a dual-node RLC circuit powered by a current-controlled voltage source.
To unravel the complexities of this system, nodal analysis is employed, a powerful technique founded on Kirchhoff's current law (KCL), which remains valid for phasors. AC circuits can effectively be...
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Maximum Power Flow and Line Loadability01:23

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The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
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Related Experiment Video

Updated: Sep 26, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Downlink Performance Modeling and Evaluation of Batteryless Low Power BLE Node.

Ashish Kumar Sultania1, Carmen Delgado2, Chris Blondia1

  • 1IDLab-Department of Computer Science, University of Antwerp-imec, 2000 Antwerp, Belgium.

Sensors (Basel, Switzerland)
|April 23, 2022
PubMed
Summary
This summary is machine-generated.

This study models batteryless Internet of Things (IoT) devices using Bluetooth Low Energy (BLE) Low Power Nodes (LPNs). It analyzes performance based on energy harvesting and capacitor size to optimize data latency and packet delivery for long-life IoT systems.

Keywords:
Bluetooth Low EnergyIoTbatterylessenergy harvestinglow power node

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Area of Science:

  • Wireless Communication Systems
  • Internet of Things (IoT)
  • Energy Harvesting Technologies

Background:

  • Emerging commercial Internet of Things (IoT) solutions require low-maintenance, long-life systems.
  • Batteryless IoT devices powered by ambient energy harvesting and long-lifetime capacitors offer a sustainable alternative to traditional batteries.
  • Unpredictable energy availability can hinder communication initiation in batteryless IoT devices.

Purpose of the Study:

  • To present an analytical model for characterizing the performance of batteryless IoT devices using Bluetooth Low Energy (BLE) Low Power Nodes (LPNs).
  • To analyze the impact of different energy harvesting powers and capacitor sizes on downlink (DL) data latency and packet delivery ratio (PDR).
  • To provide insights for optimally configuring batteryless LPNs for network deployment.

Main Methods:

  • Development of an analytical model to evaluate the performance of batteryless LPNs.
  • Characterization of performance metrics including DL data latency and PDR.
  • Simulation of the system to validate the analytical model's predictions.

Main Results:

  • The analytical model accurately predicts DL data latency and PDR for batteryless LPNs.
  • Model and simulation results show high consistency, with an average error of 2.23% for DL data latency and 0.09% for PDR.
  • The study quantifies the trade-offs between energy harvesting, capacitor size, and communication performance.

Conclusions:

  • The proposed analytical model is effective for understanding and optimizing the performance of batteryless, energy-harvesting IoT devices using BLE LPNs.
  • Optimal configuration of harvesting power and capacitor size is crucial for achieving desired DL data latency and PDR.
  • This work facilitates the deployment of sustainable and long-life IoT solutions.