With climate change becoming more and more of a concern, the need to transition to green energy is now more than ever. To ensure access to affordable, reliable, sustainable and modern energy for all, it is crucial to understand our current situation to significantly reduce our carbon consumption by 2030 and accelerate action on modern renewable energy. That is where eVe comes in place.
What it does
In the shift towards a green economy, the government of Canada will require all new car and truck sales to come from zero-emission vehicles by 2035. To support this transition, Canada will need to increase its EV infrastructure in order to improve the accessibility of charging stations for communities across the nation. Our project aims to support this transition by highlighting the need for increased EV infrastructure.
Our website identifies the gas stations and charging stations across the country. It creates a visual representation of the 11,405 conventional gas stations and 6687 EV stations that can be located across the country.
How we built it
The front end of eVe was built using React.js, and Tailwind.css. Its backend was built in Flask, making use of its amazing libraries such as SQLAlchemy to manage databases. eVe also uses different public APIs, such as the deck.gl API, and googleapis. eVe is deployed on Google Cloud Platform and can be accessed at e-v-e.tech, domain name purchased on domain.com
Challenges we ran into
We had issues with our API component where we got rate limited for the number of API calls we made. We were not sure why we got rate limited when we did not make so many requests. We had issues with deployment on Google Cloud for hosting our project.
Accomplishments that we're proud of
Our team's biggest accomplishment was to deploy our web application on Google Cloud services for the first time and have it running. We are also proud of the diversity of the tech stack used in our project.
What we learned
We learned new frameworks like Tailwind and Flask for the first time and it was a pretty good experience using them for a project like this. We also learned how to deploy our website on a Google Cloud platform.
What's next for eVe
In the future, eVe would provide AI predictions thanks to a machine learning model using all the data it would have collected in the long run. This will allow eVe to recommend clients on when is the best time to head to the closet EV station. That incentive will be to promote EV stations and encourage municipalities on making clean energy more affordable.