Electric Vehicles (EV) are environment-friendly with lower CO2 emissions, and financial affordability (in term of battery based refuel) benefits. Here, when and where to recharge are sensitive factors significantly impacting the environmental and financial gains, these are still challenges to be tackled. In this paper, we propose a sustainable and smart EV charging scheme enables the preemptive charging functions for heterogeneous EVs equipped with various charging capabilities and brands. Our scheme intents to address the problems when EVs are with various ownerships and priority, in related to the services agreed with charging infrastructure operators. Particularly, the anticipated EVs' charging reservations information with heterogeneity (are multiscale) including their EV type, expected arrival time and charging waiting time at the charging stations (CSs), have been considered for design, planning and optimal decision making on the selection (i.e., where to charge) among the candidature CSs. We have conducted extensive simulation studies, by taking the realistic Helsinki city geographical and traffic scenarios as an example. The numerical results have confirmed that our proposed preemptive approach is better than the First-Come-First-Serve (FCFS) based system, associated with its significant improvement on the reservation feature in EV charging.

This paper aims to design an intelligent mobile charging control mechanism for Electric Vehicles (EVs), by promoting charging reservations (including service start time, expected charging time, and charging location, etc.). EV mobile charging could be implemented as an alternative recharging solution, wherein charge replenishment is provided by economically mobile plug-in chargers, capable of providing on-site charging services. With intelligent charging management, readily available mobile chargers are predictable and could be efficiently scheduled towards EVs with charging demand, based on updated context collected from across the charging network. The context can include critical information relating to charging sessions as well as charging demand, etc. Further with reservations introduced, accurate estimations on charging demand for a future moment are achievable, and correspondingly, optimal mobile chargersselection can be obtained. Therefore, charging demands across the network can be efficiently and effectively satisfied, with the support of intelligent system-level decisions. In order to evaluate critical performance attributes, we further carry out extensive simulation experiments with practical concerns to verify our insights observed from the theoretical analysis. Results show great performance gains by promoting the reservation-based mobile charger-selection, especially for mobile chargers equipped with suffice power capacity.Note to Practitioners-The convenience of charging service is one major concern for EVs, especially when an urgent charging is required while none charging points are reachable. Recently, a Chinese EV company (NIO) is promoting its mobile charger (ES8 model) to Tesla. Driven by such market trend, this paper proposes an efficient approach toward intelligent scheduling of mobile chargers toward parked EVs. Different from fixed charging stations focusing on the problem of long waiting times, the proposed solution is applicable to chargingon-demand with pre-charging appointment at mobile chargers. Preliminary experiments show great charging efficiency achieved by concerning the issue of where to reserve, i.e., the consideration of optimal selection on mobile chargers. Such mobile charging services can coexist with the governmental or pilots' initiated charging station deployment. However, future research will need to evaluate the holistic service platform. Index Terms-Electric vehicles (EVs), battery charging, queuing theorem, mobile charging services.

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