A Machine to Machine framework for the charging of Electric Autonomous Vehicles

TL;DR

This paper introduces Inno-EAV, an open-source M2M framework that automates charging for Electric Autonomous Vehicles, enhancing efficiency and data collection for better energy management.

Contribution

The paper presents a novel distributed M2M charging framework for EAVs, including its software design, development process, and backend database architecture.

Findings

Automates EAV charging process

Enables data collection for decision making

Supports distributed network architecture

Abstract

Electric Autonomous Vehicles (EAVs) have gained increasing attention of industry, governments and scientific communities concerned about issues related to classic transportation including accidents and casualties, gas emissions and air pollution, intensive traffic and city viability. One of the aspects, however, that prevent a broader adoption of this technology is the need for human interference to charge EAVs, which is still mostly manual and time-consuming. This study approaches such a problem by introducing the Inno-EAV, an open-source charging framework for EAVs that employs machine-to-machine (M2M) distributed communication. The idea behind M2M is to have networked devices that can interact, exchange information and perform actions without any manual assistance of humans. The advantages of the Inno-EAV include the automation of charging processes and the collection of relevant data that can support better decision making in the spheres of energy distribution. In this paper, we present the software design of the framework, the development process, the emphasis on the distributed architecture and the networked communication, and we discuss the back-end database that is used to store information about car owners, cars, and charging stations.