ChargeVerte

Inspiration

One charge of the average EV's battery uses as much electricity as a house uses every 2.5 days. This puts a huge strain on the electrical grid: people usually plug in their car as soon as they get home, during what is already peak demand hours. At this time, not only is electricity the most expensive, but it is also the most carbon-intensive; as much as 20% generated by fossil fuels, even in Ontario, which is not a primarily fossil-fuel dependent region. We can change this: by charging according to our calculated optimal time, not only will our users save money, but save the environment.

What it does

Given an interval in which the user can charge their car (ex., from when they get home to when they have to leave in the morning), ChargeVerte analyses live and historical data of electricity generation to calculate an interval in which electricity generation is the cleanest. The user can then instruct their car to begin charging at our recommended time, and charge with peace of mind knowing they are using sustainable energy.

How we built it

ChargeVerte was made using a purely Python-based tech stack. We leveraged various libraries, including requests to make API requests, pandas for data processing, and Taipy for front-end design. Our project pulls data about the electrical grid from the Electricity Maps API in real-time.

Challenges we ran into

Our biggest challenges were primarily learning how to handle all the different libraries we used within this project, many of which we had never used before, but were eager to try our hand at. One notable challenge we faced was trying to use the Flask API and React to create a Python/JS full-stack app, which we found was difficult to make API GET requests with due to the different data types supported by the respective languages. We made the decision to pivot to Taipy in order to overcome this hurdle.

Accomplishments that we're proud of

We built a functioning predictive algorithm, which, given a range of time, finds the timespan of electricity with the lowest carbon intensity.

What we learned

We learned how to design critical processes related to full-stack development, including how to make API requests, design a front-end, and connect a front-end and backend together. We also learned how to program in a team setting, and the many strategies and habits we had to change in order to make it happen.

What's next for ChargeVerte

A potential partner for ChargeVerte is power-generating companies themselves. Generating companies could package ChargeVerte and a charging timer, such that when a driver plugs in for the night, ChargeVerte will automatically begin charging at off-peak times, without any needed driver oversight. This would reduce costs significantly for the power-generating companies, as they can maintain a flatter demand line and thus reduce the amount of expensive, polluting fossil fuels needed.