A MOVABLE CHARGING UNIT FOR GREEN MOBILITY

Abstract. Battery swapping of electric vehicles (EVs) matter appears to be the swiftest and most convenient to users. The existence of swapping stations increases the feasibility of distributed energy storage via the electric grid. However, it is a cost-prohibitive way of charging. Early adaptors’ preferences of /perceptions about EV system in general, has its inflectional effects on potential users hence the market penetration level. Yet, the charging matter of electric batteries worries the users and puts more pressure on them with the more rigorous planning-ahead they have to make prior to any trip. This paper presents a distinctive way of charging. It aims at making the overall charging process at ease. From a closer look into the literature, most of EVs’ populations depend on domestic charge. Domestic charging gives them more confidence and increases the usability factor of the EV system. Nevertheless, they still need to count on the publically available charging points to reach their destination(s). And when it comes to multifamily residences, it becomes a thorny problem as these apartments do not have a room for charging outlets. Having said the irritating charging time needed to fatten the batteries over the day and the minimal average mileage drove daily, hypothetically, home delivery charging (Movable Charging Unit-MCU) would be a stupendous solution. The paper discusses the integration of shortest path algorithm problem with the information about EV users within a metropolitan area, developing an optimal route for a charging unit. This MCU delivers charging till homes whether by swapping batteries or by fast charging facility. Information about users is to be provided by the service provider of the neighbourhood, which includes charging patterns (timing, power capacity). This problem lies under the shortest path algorithms problem. It provides optimal route of charging that in return shall add more reliability and usability values and alleviate the charging/ limited range / daily planning anxieties. The model is in a very preliminary stage of development, future work is needed to elaborate on the model and developing a complete feasibility study.