Estimation of energy consumption, route visualisation and traffic avoidance in electric vehicles
With the increase in the amount of CO2 released into the atmosphere and global warming taking its toll on people all over the globe, the need for greener alternatives in at its peak. The burning of fossil fuels contributes to 82% of Greenhouse gases released into the environment. Switching to electric vehicles will go a long way in reducing the consumption of fossil fuels and the consequent emission of greenhouse gases into the atmosphere. However, there are many reasons why people are reluctant to use electric vehicles. Our main purpose was to look for ways in which we can promote the use of electric vehicles.
The talk will involve estimating the energy that will be consumed by the electric vehicle during the journey from source to destination and additionally we will also estimate the amount of time the journey will take to complete while factoring in the time required to recharge the electric vehicle. We also predicted the amount of CO2 that will be saved from being emitted into the environment thus increasing the awareness about electric vehicles and CO2 emission.
This Route option module focuses on leveraging human knowledge to get the best route. At a given time, due to certain circumstances (like rush hour) it is possible that a certain route may be faster than a route shown on the map. People who travel at those times or people who are residents of those areas have more knowledge about which route should be taken at what time. If every person entered which path to be taken based on their experience we could create a database that will help us to predict which path should be taken.
Wouldn't you like to see a map of all the places that you have explored in your electric vehicle so far? That is what the visualisation module is about. Using data sampling and machine learning the path taken by the vehicle has been visualised.
The route prediction module helps to predict the route that the car will take based on the previous history. Wouldn't you like your map to automatically predict the path with least traffic? Wouldn't it be awesome if your map showed you the path to be taken after checking where other cars are going to go? This is what module is about. Based on the initial few points and the previous history, it will predict the route that the car will take, thus helping to determine the predicted congestion and helping in routing the electric cars better.
Basic knowledge of Machine Learning
Awareness about electric vehicles
A desire to save planet Earth by going green!
Mashrin is passionate about data science, algorithms and graphs and is presently in the final year pursuing Computer Science and Engineering from Vellore Institute of Technology. I have interned as a data scientist at Wingify, Cerelabs, Sales and Quotes and Knolskape and have been a contributor to the Stanford Scholar Initiative and processing.py
Saumya is a data science enthusiast currently working as Google Summer of Code student @GreenNavigation. She is presently a final year student pursuing Computer Science and Engineering from Vellore Institute of Technology and have previously interned with Cisco, Knolskape and Cerelabs along with contributing to Stanford Scholar initiative.
Sanjay is presently a final year student of Computer Science and Engineering at VIT University. He is extremely proficient in algorithms and web programming. He has a new found interest in Machine Learning and is planning to pursue it for higher education.
P.S.- Special thanks to Andrew Ng from all three of us (and many others from the student community)
GSoC link: https://github.com/Greennav/machine-learning