ALBERT

Description: Mobility as a Service (MaaS) is a concept that aligns with both current and future mobility demands of users, namely intermodal, personalized, on-demand and seamless. Although the number of shared mobility, electric mobility and multimodal passenger transport users is rapidly growing, until now, the list of MaaS and electric Mobility as Service (eMaaS) providers is quite short. This could partly be explained by the lack of a common architecture that facilitates the complex integration of all actors involved in the (e)MaaS ecosystem. The goal of this publication is to give an overview of the state of the art regarding (e)MaaS’ ecosystems and architectures. Moreover, it aims to support the further development of eMaaS by proposing a definition and a novel system architecture for eMaaS. Firstly, the state of the art of the MaaS ecosystem is reviewed. Secondly, the eMaaS ecosystem that builds upon our definition of eMaaS is described and the MaaS system- and technical- architectures found in literature are reviewed. Finally, an eMaaS architecture that focuses on the integration of MaaS and electric mobility systems is presented. With the definition, ecosystem and system architecture presented in this work, the aim is to support the further development of the eMaaS concept.

Description: Current electric vehicle (EV) charging systems have limited smart functionality, and most research focuses on load-balancing the national or regional grid. In this article, we focus on supporting the early design of a smart charging system that can effectively and efficiently charge a company’s EV fleet, maximizing the use of self-generated Photo-Voltaic energy. The support takes place in the form of the Vehicle Charging Simulation (VeCS) model. System performance is determined by operational costs, CO2 emissions and employee satisfaction. Two impactful smart charging functions concern adaptive charging speeds and charging point management. Simulation algorithms for these functions are developed. The VeCS model is developed to simulate implementation of a smart charging system incorporating both charging infrastructure and local Photo-Voltaics input, using a company’s travel and energy data, prior to having the EVs in place. The model takes into account travel behaviour, energy input and energy consumption on a daily basis. The model shows the number of charged vehicles, whether incomplete charges occur, and energy flow during the day. The model also facilitates simulation of an entire year to determine overall cost and emission benefits. It also estimates charging costs and CO2 emissions that can be compared to the non-EV situation. With the VeCS model, the impact of various system design and implementation choices can be explored before EVs are used. Two system designs are proposed for the case company; a short-term version with current technology and a future version with various smart functionalities. Overall, the model can contribute to substantiated advice for a company regarding implementation of charging infrastructure.