Fleet EV | Devpost

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Inspiration

This project was inspired by the growing popularity of electric vehicles (EVs) and the increasing need for mass adoption of sustainable transportation. As team members had experience in the EV industry and faced the challenges of charging an electric vehicle, they decided to tackle the challenge of route planning for a fleet of EVs with the goal of minimizing energy consumption, CO2 emissions, and travel time.

What it does

The project aims to develop a fleet management tool to optimize routes for multiple EVs, reducing the load on grids and optimizing the charging logistics of an EV fleet. The approach focuses on two main fronts: algorithm development and app development.

How we built it

The algorithm development focuses on solving the vehicle routing with battery constraint problem, using a mix of mixed integer linear programming and heuristics methods. On the app development front, the team attempted to gather data from a Swedish company's API but had to find alternative free sources of data for the app, such as the OpenChargeMap API and ElectricityMaps API. The app also uses Streamlit to display charging information for multiple EVs in a user-friendly interface.

Challenges we ran into

The team faced challenges with data/algorithm integration and user interface design. The challenge of gaining access to the Swedish company's API led to the need to find alternative data sources, and the team had to design a user-friendly interface to display charging information effectively.

Accomplishments that we're proud of

The team is proud of developing an efficient and effective optimization model that balances different objectives in a practical and realistic way. They also believe that the app has the potential to play a significant role in advancing the adoption of electric transportation and contributing to a more sustainable future.

What we learned

The project allowed the team to gain a deeper understanding of the complex challenges and potential solutions involved in optimizing routes for a fleet of electric vehicles. The team learned about the nuances of developing efficient and effective optimization models and the importance of balancing different objectives in a practical and realistic way.

What's next for Fleet EV

The next step for Fleet EV is to continue refining the algorithm and app, gathering more data and feedback to further improve its performance and usability. The team is also exploring potential collaborations with companies and organizations in the EV industry to help advance the adoption of sustainable transportation.

Built With

electricitymapsapi
googlecloudapi
love
openchargemapapi
pulp
python
streamlit

Submitted to

Hacklytics 2023: A Starry Night

Created by

I worked on the app development using python and streamlit, as well as API integration. I also came up with the idea of tackling the EV charging space that originally evolved into EV fleet route planning.

Gabe G
As the project manager, I was responsible for overseeing every aspect of the project, including ideation, app development, and the mathematical algorithms. I also played a hands-on role in the development process, working on the integration of design and development and utilizing datasets to create visualizations using GCP's deck.gl tool. These visualizations were essential to the success of our mission.

Shubhankar Tripathy
Computer Science , Computer Engineering and Mathematics major at UMass Amherst. I am an active undergrad researcher.
I worked on the presentation as a whole designing graphics for the UI of the Drivers app and Data visualisation using google cloud for the fleet in the main logistics app.

Krish Sharma
I came up with the original idea and developed our mixed integer linear program for optimizing our multi-vehicle fleet trajectories. The optimization framework allows us to actually compute these fleet trajectories and is really useful for planning purposes.

Aksh Garg