ABOUT THE PROJECT
Customer / Partner Country
- Mobile Application Development
- Blockchain development
- Machine Learning Development
- Backend Development
- Frontend development
The Smart Management Platform for Electric Vehicles Charging Stations aims to build a software platform for the management of EV charging stations on top of the latest OCPP version. Some of the most important features of this platform include an AI module for smart scheduling, a Blockchain system for peer-to-peer charging, and a wrapper over the OCPP protocol. The backend of the SmartEVC Platform is currently being developed using the Ruby on Rails framework.
The general goal of the Smart EVC Platform project is to increase the competitiveness of the economic environment by assimilating RDI results in conceiving, designing, implementing, testing and demonstrating an intelligent charging station management platform based on Blockchain and artificial intelligence (TRL6), along with the transfer of this software to the market. The Smart EVC Platform will be a software platform for the intelligent management of charging stations that will run in the cloud.
- Development of a platform meant to unify the different methods of connecting the electric vehicles to the charging stations.
- Unification of payment methods for charging electric vehicles by using Blockchain technology.
- Development of an intelligent reservation module for charging stations, using machine learning algorithms.
- Development of a mobile application that facilitates the user's interaction with the charging stations and that generates intelligent alerts on the upcoming charging options, using various parameters such as driving style or technical parameters of the vehicle.
- Strengthen charging station networks by supporting and encouraging private station owners to allow reservations and recharging for a fee.
ASSIST Software is responsible for the development of the online charging platform, mobile applications, intelligent reservation module for charging stations, using machine learning algorithms.
|Co-funded by the nationally-funded program PN-III-P2-2.1-PTE-2019|
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