Innovative Services for Electric Mobility Based on Virtual Sensors and Petri Nets

About 60%of emissions into the earth’s atmosphere are produced by the transport sector,caused by exhaust gases from conventional internal combustion engines.An effective solution to this problem is electric mobility,which significantly reduces the rate of urban pollution.The use of electric vehicles(EVs)has to be encouraged and facilitated by new information and communication technology(ICT)tools.To help achieve this goal,this paper proposes innovative services for electric vehicle users aimed at improving travel and charging experience.The goal is to provide a smart service to allow drivers to find the most appropriate charging solutions during a trip based on information such as the vehicle’s current position,battery type,state of charge,nearby charge point availability,and compatibility.In particular,the drivers are supported so that they can find and book the preferred charge option according to time availability and the final cost of the charge points(CPs).To this purpose,two virtual sensors(VSs)are designed,modeled and simulated in order to provide the users with an innovative service for smart CP searching and booking.In particular,the first VS is devoted to locate and find available CPs in a preferred area,whereas the second VS calculates the charging cost for the EV and supports the driver in the booking phase.A UML activity diagram describes VSs operations and cooperation,while a UML sequence diagram highlights data exchange between the VSs and other electromobility ecosystem actors(CP operator,EV manufacturer,etc.).Furthermore,two timed Petri Nets(TPNs)are designed to model the proposed VSs,functioning and interactions as discrete event systems.The Petri Nets are synchronized by a single larger TPN that is simulated in different use cases and scenarios to demonstrate the effectiveness of the proposed VSs.

Model-Based Quantification of Load Shift Potentials and Optimized Charging of Electric Vehicles

Managing the charging process of a large number of electric vehicles to decrease the pressure on the local electricity grid is of high interest to the utilities. Using efficient mathematical optimization techniques, the charging behavior of electric vehicles shall be optimally controlled taking into account network, vehicle, and customer requirements. We developed an efficient algorithm for calculating load shift potentials defined as the range of all charging curves meeting the customer’s requirements and respecting all individual charging and discharging constraints over time. In addition, we formulated a mixed integer linear program (MIP) applying semi-continuous variables to find cost-optimal load curves for every vehicle participating in a load shift. This problem can be solved by e.g. branch-and-bound algorithms. Results of two scenarios of Germany in 2015 and 2030 based on mobility studies show that the load shifting potential of EV is significant and contribute to a necessary relaxation of the future grid. The maximum charging and discharging power and the average battery capacity are crucial to the overall load shift potential.

Application of an aerosol electrical mobility spectrum analyzer: Charged-particle polarity ratio measurement in the Antarctic and Arctic regions

An aerosol electrical mobility spectrum analyzer(AEMSA),developed at Hanyang University,was employed to investigate the particle charge characteristics in the Antarctic and Arctic regions.The particle charge characteristics in these areas were compared with the charging state in Ansan,South Korea,located in the midlatitude,where artificial factors,such as human activity,urbanization,and traffic,might result in a higher total concentration.Furthermore,in Ansan,South Korea,the charged-particle polarity ratio was very stable and was close to 1.However,notably different particle charge characteristics were obtained in the Antarctic and Arctic regions.The imbalance between the numbers of positively and negatively charged particles was evident,resulting in more positive charges on the atmospheric particles.On average,the positively charged particle concentrations in the Antarctic and Arctic areas were 1.4 and 2.8 times higher,respectively,compared with the negatively charged particles.The developed AEMSA system and the findings of this study provide useful information on the characteristics of atmospheric aerosols in the Antarctic and Arctic regions and can be further utilized to study particle formation mechanisms.