Welcome

    A significant movement occurring in society is a push for environmental awareness and conservation of energy. Alternative energy sources and enhanced energy efficiency are among the leading technical challenges in implementing this emerging ideal. The Electrical and Computer Engineering Department at Bradley University has decided to participate in meeting these challenges and launched a multi-year project that aims to develop a low carbon footprint electric vehicle for urban commuting that is commercially viable. This requires an ultra compact and lightweight vehicle that is also street legal. It will be capable of holding up to two passengers, traveling up to 55 mph, and traversing up to 50 miles in normal weather conditions, making it ideal for a daily commute for the average user.

      In the first phase of this project, the group performed research in order to design and implement a prototype test platform for a low carbon footprint, single passenger electric urban vehicle. It is currently capable of a maximum speed of 40mph and has a theoretical maximum range of 25 miles. In order to accomplish this, the team researched available battery technology, motors, electronics, and design concepts for regenerative braking (which will be implemented in a later version). While some of the issues surrounding mechanical design were addressed by the previous group, the detailed design of all the vehicle subsystems, particularly regenerative braking and optimized battery sizing, is the next major step in development. In order for this to be implemented, detailed models for vehicle subsystems must be developed.             

Phase II Goals

     Our ultimate goal is to create an accurate model of the test platform developed by the first phase. In doing so, future groups will have access to an interchangeable design tool for the vehicle.

• Modeling
  • Battery
  • DC Motor
  • Controller
  • Vehicle Dynamics
  • Loads
    • A/C
    • Lighting
    • Heat
• Verify and Optimize Model
  • Perform data acquisition and compare with experimental results of current platforms
  • Adjust model until desired performance is achieved.
  • Compare experimental and simulated outputs of subsystems and modify Simulink© blocks as necessary
  • Optimize Simulink© blocks