| dc.description.abstract | As the importance of reducing dependency on fossil fuels increases, the use of bicyclesand electric bicycles (e-bikes) can provide a sustainable and viable alternative for single passenger commuters. The general population’s reluctance to transition to bikes and e-bikes is, in part, due to safety concerns and general mobility. However, by designing, modeling, and testing solutions aimed at reducing weaknesses inherent to cycling, perhaps these fears can be lessened. This thesis describes risks currently involved with cycling and supplies potential solutions in the form of blind-spot and road surface monitoring, as well as modeling the body forces and the power consumption of an e-bike in motion. Chapter 1 introduces the inherent dangers facing bikes and e-bikes when compared to vehicles and discusses methods for improving their safety. Additionally, it highlights the difference between and benefits of preventative safety features for use with cycling. Furthermore, this chapter introduces the use of lidar along with its further use and potential for transportation uses. In Chapter 2, e-bike blind-spot monitoring is designed and tested using a low-cost two-dimensional lidar system. It describes improvements made from previous versions along with the hardware, software, and capabilities of the design. Testing shows its ability to determine distances to objects while alerting the rider, as well as identifying potential improvements for future systems. Chapter 3 describes the hardware and software of a hand-held, portable, low-cost three-dimensional (3-D) lidar system designed for road surface monitoring. Thorough testing shows the lidar system’s ability to create a 3-D point cloud recreation of a pothole in the road. It also identifies improvements made from a previous version and what augmentations should be made in future systems. Additionally, this chapter includes a comparison to commercially available lidar systems. Chapter 4 describes the theoretical equations used to model the motion, body force, power consumption, and battery capacity of an e-bike while in use. Furthermore, by knowing road and weather conditions, e-bike design, combined weight and size of the rider and e-bike, the model can predict battery pack behavior and state of charge. Chapter 5 details experimental testing of the theoretical model described in Chapter 4. By estimating physical properties from available literature and thoroughly measuring e-bike motion, wind conditions, and route, the model can be compared to the actual battery voltage that was recorded throughout the tested routes. Additional modifications are made to the model to account for discretization of measured data and the state of health of the battery pack to better reflect the collected data. | |
