ECO-DRIVE

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  Home Page

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  User Interface

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  Route Information

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  Available Charging Stations' Information

ECO-DRIVE tackles an EV-specific challenge like battery limits, learning to optimize routes intelligently and efficiently.

ECO-DRIVE makes use of Reinforcement Learning model implemented on a double DQN to find optimal paths for EVs. The project also incorporates a machine learning model for predicting the maximum distance an EV can go based on its vehicle parameters

Geocoding to fetch the co-ordinates of the start and end locations Fetch routes and charging station based on users current location Predict range of EV using a RandomForest regression model Use the RL model to find the most optimal route or suggest stopping at a charging station Store the route and charging station

Challenges: Data unavailability API Inaccuracy

Built With

• flask
• javascript
• python
• react
• tensorflow