AutoChargerX
Team A1: Bruce Cheng, Steven Zhang, Harry Huang
18-500 Capstone Design, Fall 2024
Electrical and Computer Engineering Department
Carnegie Mellon University
System Architecture
Product Pitch
There are two main approaches for people to charge their electronic devices: wired charging and wireless charging. However, wired charging forces people to stay close with their devices and wireless charging requires perfect alignment for charging thus having a high failure rate.
To address the problems mentioned above, our project, AutoChargerX, introduces a new way in wireless charging technology, addressing the frustrations of wired and misaligned wireless chargers. By utilizing a 3-axis stepper motor gantry system, an underdesk device detection and control feedback computer vision system, monitoring softwares, and a sandwich glass table top with 4 wireless charging pads, our smart charging table automatically detects and aligns with electronic devices such as your cell phone, allowing seamless wireless charging for multiple devices without manual intervention.
Overall, the system has a ~93.3% electronic device detection accuracy within 1 second. The charging pad - electronic devices automatch time is ~5.47 seconds on average and the charging starts ~0.8s on average after successful alignment.
System Description
System Evaluation
Conclusions & Additional Information
System Assessment
The system delivers automated device charging through intelligent detection and integrated software solutions. It identifies compatible devices, manages charging pads, and enables real-time monitoring through iOS, macOS, and web applications, allowing users to track multiple devices charging simultaneously via their preferred interface.
Takeaways
�Future Development
Metric | Target | Actual |
Auto Device Detection | <=1sec, <=1cm | Success Rate 93.33% |
Auto Match and Alignment | <= 10sec, <= 1cm | Avg Alignment Error: 4.76mm Avg Alignment Time: 5.47sec |
Charging Start | <=2sec after alignment | Avg Charging start time: 1.4s |
Fully Charge Time | <=45min | Completed |
Phone Live Battery Display | <= 500ms Update rate | Completed |
Temperature Control Monitor | Threshold at 50℃ | Completed |
Use-Case Requirements
Gantry Subsystem
It use timing belt and 3 motors to achieve the manipulator precise movement in 3-axis. The manipulator has four magnetics tips which will be used to attached to the glass desktop and move the charging pads inside.
Charging Pads
The wireless charging modules has a LED light on the PCB for charging status indication. The 3D-printed case with magnetics tips for attaching to gantry manipulator.
Vision Subsystem
It features a wide-angle camera under table. By utilizing YOLOv5 model that is running on cloud, it could detect devices on tabletop and send location command to gantry system�for charging pad movement.�It also monitor lights under�the charging pads which �serves as a indicator for�success alignment.�
Software System
Y-Axis
X-Axis
Z-Axis
Motor
Manipulator
Figure 5: Phone App,Web App, and MacOS App (from left to right)
The system leverages a Raspberry Pi 5 as its central controller, utilizing GPIO pins to generate PWM signals for precise motor driver and motor control. A wide-angle camera captures continuous image frames, which are transmitted to a cloud VM for real-time phone detection using YOLOv5 machine learning model, and then detected coordinates are send back to Raspberry Pi. The platform also features a comprehensive device management system, which allows users to monitor multiple devices simultaneously. Upon installing the mobile application, user data synchronizes with cloud storage, enabling device tracking through either a macOS application or web interface.
https://course.ece.cmu.edu/~ece500/projects/f24-teama1/
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Design Requirements (30 Tests)
Sandwiched tabletop
\w 4 charging modules
Charging Initiation: Automatically align charging pad with user’s device
Fast detection/ Accurate alignment/ Quick Response
During & After Charging: Device monitoring with software and device APIs
Status Tracking/ Overheating Prevention
Finish charging fast/ Automatically stop
Figure 1. Block Diagram for system architecture
Figure 2. Gantry Subsystem Mechanism
Figure 3. Charging Module with Custom Case & Magnets
Figure 4. Computer Vision for Device & Charging Light Detection
Gantry System
Power
Supply
Camera
Motor
Drivers
Raspberry Pi
Figure 6: Overview of System Design
The software ecosystem �consists of three
interconnected applications:
an iOS app, macOS app, and web interface. When installed, the iOS app generates a unique device identifier and streams real-time data to Firebase, including battery level, charging status, thermal state, and timestamps. Both the macOS and web applications pull this data from Firebase, enabling users to monitor comprehensive charging metrics across all their registered devices.