## Inspiration

Urban areas are increasingly polluted by traffic, and although people make an effort to ride share, cars still produce a large portion of urban carbon dioxide emissions. App-based directions offer an option between distance-optimized or time-optimized routes, but never present the possibility of an eco-friendly route. In addition, many people want to be more green, but don't know what factors most impact the carbon footprint of their vehicle.

## What it does

Our interface provides an information-based solution. By synthesizing millions of very precise data points from Ford's OpenXC platform, we can isolate factors like idling, tailgating, aggressive driving, and analyze their impacts on fuel efficiency. We strip the noise in raw data to find the important trends and present them in clear visualizations.

## How we built it

The bulk of data analysis is handled by python which processes the raw json data files. Then, we use pandas to streamline data into tables, which are easier to handle and filter. Given the extreme precision of the data points (records in fractions of a second), the data was initially very difficult to interpret. With the help of numpy, we were able to efficiently calculate MPG figures and overlay additional trends on several visuals.

## Challenges we ran into

Data points for specific vehicle attributes are taken very irregularly and do not match up at the same timestamps. The user's interaction with their car's usage - negative fuel usage figures when tanks were filled. Column names in the data were inconsistent across sets (e.g. Odometer vs Fine Odometer Since Restart). Plenty of files had missing data for certain attributes, resulting in a scattering of NaNs across the dataset. Given this, we had to be clever with data filtering and condense the data so important metrics could be compared.

## Accomplishments that we're proud of

Beautiful visuals indicating clear trends in data. Clean filtering of extremely noisy raw data. A fun frontend that's visually appealing to the user.

## What we learned

Big data is not as easy as running a few functions on data that's simply downloaded from a database. Much of analytics is the filtration and data handling, and trends may often be surprising.

## What's next for MPGreen

We could integrate Maps and Directions APIs to find more eco friendly routes in order to directly provide the user with ways to reduce their carbon footprint. As it stands, our system is a strong tool to view and share information, but has potential to actually impact the environment.
## 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.
## pushing peer to use clean energy

this application is fairly simple , base on certain input such a make , model, year, mileage driven, and motor type I was able to generate the money someone would have save by driving an electric vehicle rather than gasoline. At this rate if we don ' t do something for this planet we are living on , who will?.

## feature

the application can also generate a map base on the user location with all the charging station nearby with their info as the user touch the markers, this will display ifo such as phone number, address, email, schedule etc....

## future

i plan to add new things to the app such as self awareness of driving pattern, a graph of the saving from potential weeks to months.

## what did i learn

It was my first time dealing with excessive amount of data, i litteraly had to do data mining in order to gather all the information necesarry to do the computation of the actual saving, and that based on data that are constantly changing such as gas prices. I am using a total of 7 API in this project, some of them giving me only xml data.