PATH PLANNING CONSIDERING DRIVER PREFERENCES USING ANALYTIC HIERARCHY PROCESS FOR ELECTRIC VEHICLES
Öz
Sustainable transportation and green logistics are becoming increasingly important, and the efficient use of electric vehicles (EVs) plays a critical role. However, efficient path planning for EVs remains a major challenge due to limited driving range and the need for optimised charging strategies. Usually, path recommendations are made based on a single criterion. However, drivers may want to consider multiple criteria for path selection. This study focuses on building a multi-criteria path planning algorithm that incorporates driver preferences by considering total travel time, energy consumption and travelling distance. To obtain the appropriate recommendation, these three criteria are evaluated using the Analytic Hierarchy Process (AHP) and Dijkstra algorithm is used to identify roads that take into account driver preferences. Johnson technique was used to remove negative energy weights due to energy recovery and solved the incompatibility problem of the Dijkstra algorithm with negative edge weights. The results have shown the proposed algorithm can efficiently generate solutions designed based on driver preferences and is suitable for EV routing applications. This study presents a method to increase user satisfaction by aiming at the widespread adoption of EVs and emphasizes the importance of multi-criteria decision making in addressing the unique challenges of EVs.
Anahtar Kelimeler
Electric vehicle, Path planning, Driver preference, Analytical Hierarchy Process, Dijkstra algorithm
Destekleyen Kurum
Key Digital Technologies Joint Undertaking (KDT JU) from the European Union’s Horizon Europe Programme and the National Authorities
Proje Numarası
101097267, 222N269
Teşekkür
This paper is supported by the OPEVA project that has received funding within the Key Digital Technologies Joint Undertaking (KDT JU) from the European Union’s Horizon Europe Programme and the National Authorities (France, Belgium, Czechia, Italy, Portugal, Turkey, Switzerland), under grant agreement 101097267. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or KDT JU. Neither the European Union nor the granting authority can be held responsible for them. This work is supported by the Scientific and Technical Research Council of Turkey (TUBITAK), Contract No 222N269, project title: “OPtimization of Electric Vehicle Autonomy (OPEVA)".
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