Simulation-Based Analytical Framework for M2M Autonomous Charging Infrastructure

Abstract

While a computerized simulation can be useful to gather user inputs during the new service development (NSD) process, it requires the active involvement of users/customers and can be costly and time-consuming compared to other practices such as surveys, focus groups, and interviews. However, when the service is rendered in the machine-to-machine (M2M) environment, the NSD process can exploit a simulation platform to assess service alternatives without customer involvement. This article contributes to the NSD research by identifying the services in the M2M environment as the suitable application area where a simulation platform can be effectively and efficiently implemented for data collection and analysis. We consider the autonomous charging service to demonstrate how a simulation-based analytical framework can help developing a new service in the M2M environment. As the electric vehicle (EV) industry has been evolving tremendously over the past decade, it becomes necessary to undertake significant modernization and autonomy adoption in the future charging infrastructure. Since the currently deployed charging infrastructure has not been designed to service autonomous EVs (AEVs) in an unsupervised manner, service plans to upgrade this infrastructure need to be developed to serve AEVs without any human intervention. Numerical experiments demonstrate how the simulation platform helps analyze autonomous charging station alternatives, provides recommendations for operational decisions, and creates a potential value for stakeholders and businesses during the NSD process.