ANALYSING THE OPTIMISATION TACTICS FOR ELECTRICAL VEHICLE CHARGING POINTS
DOI:
https://doi.org/10.61841/ndhhc397Keywords:
Electric Vehicle Charging Probability Model, Traffic Congestion Management, Charging Strategy Development, Electric Vehicle Charging Networks.Abstract
In spite of the restricted range of electric vehicles (EVs) and the limited availability of charging stations, drivers of EVs who are contemplating long-distance journeys continue to experience range anxiety. As a result, it is essential to ascertain whether or not the chosen route is feasible and to pinpoint the most effective method of charging. A mixed integer linear programming (MILP) method is proposed in this paper for the electric vehicle charging strategy problem (EVCSP). This approach incorporates a piecewise linear approximation technique to handle the issues that are provided by nonlinear charging durations. This approach is an essential component of decision support for electric vehicle drivers. The suggested optimisation model, which is known as CSPM, is responsible for determining where, when, and how much to charge an electric vehicle for a certain route in order to reduce the amount of time and money spent travelling. The resilience and dependability of the CSPM reveals itself via the solution time of large-scale test issues as well as through a case study on Turkey. In addition, the case study is subjected to the application of two kinds of multi-objective optimisation techniques, namely the weighted sum method and the lexicographic approach, and the outcomes are analysed. There is a range of 46.09 percent across all of the charging techniques that were applied, which indicates that the trip cost is more sensitive to the chosen charging approach. On the other hand, journey time is more durable, with a maximum variation of 19.77 percent. The CSPM cut travel time by 60.1% and improved cost efficiency by 105.72%, according to a comparison study with a complete charging approach. This is the result of the CSPM.
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