People would be reluctant to adopt EV technology due to longer recharge times as compared to fuel refilling for regular vehicles.

What it does

The assistant recommends drivers an ideal charging station based on not only travel time but also other factors such as waiting times, charging times, vehicle make and model, etc. It ensures that you leave on time to reach the charging station before your battery drains down to zero. The system reduces waiting times at charging stations by diverting drivers to charging stations with fewer other people in line; hence, regularly distributing workload among nearby charging stations. Once you reach the desired charging station, it recommends the amount of time to charge the vehicle based on your current trip.

How we built it

Designed and planned in Adobe XD. Prototype server uses Node.js with express for routing and MongoDB for data storage. Prototype mobile app data simulation system uses Angular.

Challenges we ran into

We struggled a lot in integrating the design with our scripts. It was hard to prioritize the implementation of the core modules from the lesser necessary ones.

Accomplishments that we're proud of

Our system has the potential to help early adopters of EV technology to ensure they don't run out of charge and in the long run, once the number of users increases significantly, it can help in the regular distribution of workload among the infrastructure. The system will generate data that would not have been generated without any digital queueing systems in place. This data can be analyzed in the future to draw inferences that can further help in the development of the infrastructure. The user interface of the assistant has been designed keeping simplicity and usability in mind, trying to ensure accessibility for all. It aims to increase the overall revenue by improving user satisfaction.

What we learned

Analyzing the problem from multiple viewpoints -- the customer, the company, the infrastructure itself, and the present scenario. Providing a viable solution to problems in the near future as well as in the long run.

What's next for Team Byte

Adding more modules to our core code to implement all the features one-by-one in the near future.

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