EXAMINING THE OPTIMISATION STRATEGIES FOR ELECTRIC VEHICLE CHARGING STATIONS
DOI:
https://doi.org/10.61841/21c1r893Keywords:
Electric vehicles, scheduling techniques, optimal scheduling, network integration.Abstract
Despite the restricted range of electric vehicles (EVs) and the limited availability of charging stations, owners of EVs who are contemplating long-distance journeys continue to experience range anxiety from their vehicles. Therefore, it is essential to ascertain whether or not the chosen route is feasible and to figure out the most effective method of charging at the same time. This paper provides a mixed integer linear programming (MILP) solution for the electric vehicle charging strategy problem (EVCSP). The MILP approach incorporates a piecewise linear approximation technique to solve the issues provided by nonlinear charging durations. This approach is an essential component of decision assistance for electric vehicle drivers. For a given route, the suggested optimisation model, which is referred to as CSPM, decides where, when, and how much to charge an electric vehicle in order to save the amount of time and money spent travelling. It is possible to determine the resilience and dependability of the CSPM by analysing the solution time of large-scale test issues and doing a case study on Turkey. In addition, the case study is subjected to the application of two multi-objective optimisation approaches, namely the weighted sum method and the lexicographic method, and the outcomes are analysed. With a range of 46.09 percent across all of the applied charging methods, the findings suggest that the trip cost is more sensitive to the chosen charging strategy. On the other hand, journey time remains more robust, with a maximum variation of 19.77 percent throughout the range of the applied charging strategies. According to the findings of a comparison study with a complete charging approach, the CSPM increases the cost efficiency by 105.72 percent while simultaneously decreasing the amount of time spent travelling by 60.1%.
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