Project

# Title Team Members TA Documents Sponsor
14 Design of Automated Guided Vehicle Wireless Charging System Based on DSP Position Adaptive Variable Frequency Control
Jiaxin Cao
Jingzhou Ding
Jinru Cai
Yaxin Li
 design_document1.pdf
final_paper1.pdf
final_paper2.pdf
other1.pdf
 Chushan Li
# **People**
Jingzhou Ding, Jinru Cai, Jiaxin Cao, Yaxin Li
# **Problem**
In light of national carbon peaking and carbon neutrality goals, smart electric vehicles are a backbone force in reducing carbon emission. However, one of the largest obstacles in the promotion of EVs is the capacity of batteries. Wireless Power Transfer (WPT) offers a promising solution, but efficiency has long been a problem due to low mutual induction and a low transmission factor between the transmitter and receiver coils. Furthermore, this inefficiency is often exacerbated by foreign metal objects and the misalignment of the vehicle while parking at the charging station.

# **Solution Overview**
We propose an integrated prototype of a wireless charging system consisting of a mobile robot (car) and a charging station. To solve the misalignment and efficiency issues, the system integrates computer vision to achieve environmental perception and precise position detection. Instead of relying solely on physical alignment, the system uses the visually calculated position offset to feed a DSP controller. The DSP dynamically adjusts the switching frequency of a Dual Active Bridge (DAB) resonant converter. By combining this position-adaptive frequency control with a hill-climbing algorithm for Maximum Power Point Tracking (MPPT), the circuits of the transmitter and receiver sides are continuously tuned to maintain resonance and maximize power transfer efficiency.

# **Solution Components**

## Visual and Navigation Subsystem (ECE)
- Raspberry Pi and Camera Module for integrating computer vision technology to detect the charging station and calculate spatial offset.
- STM32 Microcontroller for autonomous navigation and obstacle avoidance.

## Power Transfer and Control Subsystem (EE)
- Tx and Rx Coils arranged at the bottom of the car to save space.
- DSP controller for executing the MPPT algorithm and providing variable switching frequencies.
- Dual-Bridge Series Resonant Converter (DBSRC) circuit, including an inverter network, high-frequency transformer, and rectifier network.

## **Criterion for Success**
- The car must successfully and automatically detect the location of the wireless charging stations and navigate to the charging point.
- The visual tracking system must accurately output the relative position coordinates of the coils to the DSP.
- The WPT system must support a wireless fast charging capability of ≥ 20W to achieve efficient energy replenishment.
- The DBSRC and DSP control logic must successfully impose zero-current switching or zero-voltage switching under load or positional variations.

Low Cost Myoelectric Prosthetic Hand

Featured Project

According to the WHO, 80% of amputees are in developing nations, and less than 3% of that 80% have access to rehabilitative care. In a study by Heidi Witteveen, “the lack of sensory feedback was indicated as one of the major factors of prosthesis abandonment.” A low cost myoelectric prosthetic hand interfaced with a sensory substitution system returns functionality, increases the availability to amputees, and provides users with sensory feedback.

We will work with Aadeel Akhtar to develop a new iteration of his open source, low cost, myoelectric prosthetic hand. The current revision uses eight EMG channels, with sensors placed on the residual limb. A microcontroller communicates with an ADC, runs a classifier to determine the user’s type of grip, and controls motors in the hand achieving desired grips at predetermined velocities.

As requested by Aadeel, the socket and hand will operate independently using separate microcontrollers and interface with each other, providing modularity and customizability. The microcontroller in the socket will interface with the ADC and run the grip classifier, which will be expanded so finger velocities correspond to the amplitude of the user’s muscle activity. The hand microcontroller controls the motors and receives grip and velocity commands. Contact reflexes will be added via pressure sensors in fingertips, adjusting grip strength and velocity. The hand microcontroller will interface with existing sensory substitution systems using the pressure sensors. A PCB with a custom motor controller will fit inside the palm of the hand, and interface with the hand microcontroller.