Dynamic-Area-Based Shortest-Path Algorithm for Intelligent Charging Guidance of Electric Vehicles

Abstract

With the increasing popularization and competition of electric vehicles (EVs), EV users often have anxiety on their trip to find better charging stations with less travel distance. An intelligent charging guidance strategy and two algorithms were proposed to alleviate this problem. First, based on the next destination of EV users’ trip, the strategy established a dynamic-area model to match charging stations with users’ travel demand intelligently. In the dynamic area, the Dijkstra algorithm is used to find the charging station with the shortest trip. Then, the area extension algorithm and the charging station attribution algorithm were developed to improve the robustness of the dynamic area. The two algorithms can automatically adjust the area size according to the number of charging stations in the dynamic area to reduce the number of nodes traversed by the Dijkstra algorithm. Finally, simulation examples were used to verify the effectiveness of the proposed model and algorithms. The results showed that the proposed intelligent charging guidance strategy can meet the travel demand of users. It is a promising technique in smart cities to find better travel trips with less travel distance and less computed time.

AFARM: Anxiety-Free Autonomous Routing Model for Electric Vehicles with Dynamic Route Preferences

“…Different from the benchmark model [10], which considers the charging request point as the initial reference for the search area, our proposal involves finding the routing path R ev i with respect to the trip's starting point. Moreover, our model reads the following information (1) EV make m, m ∈ M, (2) the EV model e, e ∈ E, (3) its battery charge β ev i in kWh, SoH, and (4) the EV's power consumption profile ρ ev i in kW/kWh, along with (5) the source s ev i and destination d ev i points of the desired trip.…”

“…For the problem of routing and path selection for EVs, in [10], the authors proposed a routing model that claims to solve the issue of range anxiety. They proposed creating a polygon-shaped area as a navigation space towards the vehicle's trip destination.…”

“…In the same domain of research, the work of [11] proposed an autonomous adaptive routing model that aims to enhance the path selection process outlined in [10], and introduces a cognitive model that takes into account the type of EV in the routing process. The proposal in [11] is also adaptive, as it adjusts the path selection process to any changes or updates that occur in the vehicle's or road's status in real-time.…”

“…The proposal in [11] is also adaptive, as it adjusts the path selection process to any changes or updates that occur in the vehicle's or road's status in real-time. Compared to the navigation paths chosen in [10] the model presented in [11] is cognitive, adaptive, and shorter. However, restricting the search domain for a charging station to a relatively small area, as suggested in [11], may not guarantee the selected navigation paths are the shortest.…”

Electric vehicle charging path planning considering couped faults in distribution-transportation network

“…If a charging station is directly connected to a fault node, it may lose its charging function. Charging stations not directly connected to the fault node may also be affected by dispatching strategies, which can cause them to stop working (Cai et al, 2020). In such cases, EVs at the faulty charging station will need to replan their charging itinerary.…”