1875 Introduction In this Anthropocene period, Global warming, urbaniza- tion, and growing consumption of fossil fuels disrupting the ecological balance hastily. Bring back the balance between human made infrastructures and ecology required to be ad- dressed as a significant component to renovate the energy entree. From the literature, it can be seen that, air pollution has the biggest impact on the environment 1a . The fossil fuel driven vehicles are mostly responsible for the air pollution and to reduce it, deliberately, people are tending towards the electric vehicles (EV), because of its zero emission features 1b . However, the main anxiety of EV drivers are “WHEN” and “WHERE” to charge the EV. Due to the poor charging infra- structure and planning, most of the charging stations (CS) are suffering from long queues and sudden breakdown at middle of the road. Besides these, they are suffering from long queues in CS, as they are unknown about the slot avail- ability. In literature 1c , the authors were focused on the de- sign of EVs. In 1d , the authors were focused on Electric ve- hicles Supply equipment (EVSE) to improve the charging WEES-2020 Special Issue J. Indian Chem. Soc., Vol. 97, No. 10b, October 2020, pp. 1875-1891 Allocation of appropriate charging station and intelligent charging scheduling for on-road electric vehicles Sourav Das*, Parimal Acharjee and Aniruddha Bhattacharya Department of Electrical Engineering, National Institute of Technology Durgapur, Durgapur-713 209, West Bengal, India E- mail: svdas111@gmail.com, parimal.acharjee@gmail.com, bhatta.aniruddha@gmail.com Manuscript received online 21 April 2020, revised and accepted 21 August 2020 Electrification of vehicles is an upgraded solution to deal with global warming. Although, anxiety related with Electric Vehicle (EV) usage is a great challenge to deal with. To avoid anxiety, selection of “appropriate” charging station at right time is nec- essary. Concomitantly, charging of EVs in uncoordinated manner can be stressful for the grid causing power loss, and insta- bility issues. Hence, in this paper, a new intelligent “Strategy for Appropriate Charging Station Assignment and Intelligent Sched- uling” (SACAIS) algorithm has been proposed. In the first layer of algorithm, the relevant charging station and the shortest path to reach the assigned Charging station for individual EVs has been done. After that, a combined scheduling of the ve- hicles has been done to minimize the total daily charging cost incurred by CSO considering grid to vehicle (G2V) and Ve- hicle to grid (V2G) mode simultaneously in the second layer of algorithm. Later to validate the robustness of the optimization techniques, Wilcoxon Signed Ranked Test and Quade test has been performed. Keywords: Charging scheduling, Dijkstra’s algorithm, grid to vehicles (G2V), linear programming, optimization techniques, ve- hicles to grid (V2G). infrastructure, but very few researchers dealt with the anxi- eties of EV owners. Therefore, more exploration is required to get rid of such anxieties as mentioned earlier. Smart strat- egies are needed to find out “appropriate” charging stations for charging EVs at apt time. In 1o , though relevant CS has been identified, but the shortest path to reach that CS has not been determined. In the other hand, charging of EVs in uncoordinated is another major concern, since it may create stresses on the utility, which may also cause increase of ac- tive power loss, instability, voltage sag and so many adverse effects 1e . Many authors have taken various strategies to deal with this issue. In 1f , Vehicle to Grid (V2G) concept has been adopted as a remedy to deliver surplus power of the battery to the grid and can act as a spinning reserve 1g . But here, the battery degradation has not been considered during V2G technique and simultaneous operation G2V and V2G mode of operation also missing. In the literatures 1h,1i , the authors have shown that, uncoordinated EV charging can cause more active power losses and therefore coordination of charging using dual mode of operation (G2V and V2G) can minimize