A 100% Solar Electric Vehicle: Applying high efficiency solar modules in sustainable transport
As our planet suffers the effects of climate change, it is only a matter of time before society will have to centre all aspects of development around sustainability. In the past, clean solutions for transportation have been dismissed due to the higher cost, and lower efficiency than fossil fuels. However, in the past few decades, there has been a steep decline in solar module cost, and and a steady climb towards higher efficiency. From my findings in this project, I have concluded that we are now at a point where we can embrace the clean, renewable potential which our sun offers. I have created and tested a proof-of concept electric vehicle (Solar EV), which can run indefinitely during daylight hours, provided sunny conditions. There are several mechanical features of my project which highlight the potential that renewable energy in transportation can have. Firstly the vehicle’s 500W motor is powered by 3 100W solar modules, and 3 50W modules, for a total of 450W or power generation. This means that when driving at anything less than 90% throttle, the Solar EV can run continuously without needing to stop to charge or refuel. Another design mechanism installed in the vehicle are three 12V lead acid batteries. These batteries allow the Solar EV to be powered for over 1.5 hours, which is useful during cloudy conditions, night, and most importantly, when driving through areas of shade. A unique efficiency component designed into my vehicle is the linear actuator I installed into the module racking system. This design element allows the tilt of the modules to be altered, to maximize the efficiency of the solar module array. At early or late hours of the day, it can be heavily tilted with the press toggle switch, or kept at a relatively flat level when the sun is the highest in the sky. I ran a series of trials to figure out whether or not the theoretical data matches up with the experimental results. After my series of trials, the bike was yet to run out of power. The solar vehicle reaches speeds up to 32 km/h, however comfortably glides at around 25 km/h. The linear actuator I installed allows the solar modules’ tilt to change . During different times of day or year, the sun is at different heights in the sky, however it is very important to maximize the solar potential. With the press of a switch, the module can be actuated to account for this. Lastly, regenerative braking captures the energy from braking. Using the reversible nature of a DC motor with a specialized motor controller responding to feedback from the brake actuators allows the vehicle to reuse energy that would otherwise be wasted as heat.
Satellite Modeling of Wildfire Susceptibility in California Using Artificial Neural Networking
Wildfires have become increasingly frequent and severe due to global climatic change, demanding improved methodologies for wildfire modeling. Traditionally, wildfire severities are assessed through post-event, in-situ measurements. However, developing a reliable wildfire susceptibility model has been difficult due to failures in accounting for the dynamic components of wildfires (e.g. excessive winds). This study examined the feasibility of employing satellite observation technology in conjunction with artificial neural networking to devise a wildfire susceptibility modeling technique for two regions in California. Timeframes of investigation were July 16 to August 24, 2017, and June 25 to December 8, 2017, for the Detwiler and Salmon August Complex wildfires, respectively. NASA’s MODIS imagery was utilized to compute NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), land surface temperature, net evapotranspiration, and elevation values. Neural network and linear regression modeling were then conducted between these variables and ∆NBR (Normalized Burn Ratio), a measure of wildfire burn severity. The neural network model generated from the Detwiler wildfire region was subsequently applied to the Salmon August Complex wildfire. Results suggest that a significant degree of variability in ∆NBR can be attributed to variation in the tested environmental factors. Neural networking also proved to be significantly superior in modeling accuracy as compared to the linear regression. Furthermore, the neural network model generated from the Detwiler data predicted ∆NBR for the Salmon August Complex with high accuracy, suggesting that if fires share similar environmental conditions, one fire’s model can be applied to others without the need for localized training.