Electric vehicles (EVs) have enjoyed increasing adoption because of the global concerns about the petroleum dependence and greenhouse gas emissions. However, their limited driving range fosters the occurrence of charging requests deriving from EV drivers in urban road networks, which have significant uncertain characteristic from time dimension in the real-world situation. To tackle the challenge brought by the dynamic charging requests, this study is devoted to proposing optimal strategies to provide guidance for EV charging. The time-varying characteristic of road network is further involved in the problem formulation. Based on the charging request information, we propose two charging guidance strategies from different perspectives. One of the strategies considers the travel demands of EV drivers and uses the driving distance as the optimization criterion. In contrast, the other strategy focuses on the impacts of EV number on the charging station operation and service satisfaction. The reachable charging stations with minimum vehicle number are selected as the optimal ones. More importantly, both the strategies have the ability to ensure the reachability of selected charging stations in a time-varying road network. In addition, we conduct simulation examples to investigate the performance of the proposed charging guidance strategies. Besides, the insights and recommendations on application scenarios of the strategies are introduced according to the simulation results under various parameter scenarios.
The dependence of human society on petroleum has contributed to serious environmental and energy problems. The transportation sector is one of the major economic industries that contribute to energy consumption and greenhouse gas emissions. According to the investigation conducted by the International Energy Agency, the energy consumption of the transportation sector accounts for 28% of the global energy consumption and is responsible for 23% of the global greenhouse gas emissions (International Energy Agency, 2017). Given the public concern on climate change and advances in battery technologies, electric vehicles (EVs) have been introduced as a promising solution for the problem of dependency on fossil fuels and increasing greenhouse gas emissions (Rezvani et al., 2015).
However, unlike conventional internal combustion engine vehicles, EVs have the relatively short driving range due to the limited capacity of batteries. The drivers often need to recharge their vehicles during trips to successfully reach the destinations. Moreover, the charging stations for EVs are considerably less popular than gas stations. These disadvantages increase driver range anxiety, that is, the fear of depleting battery energy en route (Melliger et al., 2018). In order to help drivers to select suitable charging stations and alleviate their range anxiety, a smart charging service would be developed to provide guidance for EV charging. Through such a service, EV drivers send charging requests to the charging operating centre when the battery energy of their vehicles is insufficient to reach the destinations, and the centre provides feedback to the drivers, which is the optimal charging station selection according to the information from the drivers’ charging requests (Wang et al., 2018b).
To realize the smart charging service, the charging guidance strategies based on charging request information need to be developed. Furthermore, in the real-world travel situation, the traffic condition on a road network often has the time-varying characteristics, which would influence the route and charging station selection for EVs (Gendreau et al., 2015). Thus, besides the charging requests, the time-varying characteristics of road network should be considered in the charging guidance strategies.
More importantly, the dynamic characteristic intrinsic to the charging requests has substantial impacts on the strategies, which would further increase the difficulties to deal with the charging requests. Note that, large-scale charging behaviours with dynamic characteristic would exert significant impacts on the operation efficiency of charging stations. Therefore, given the widespread adoption of EVs in the current and future global transportation system, special attention must be given to solve the dynamic charging requests under the real-world complicated situation.
EVs are taking shape as the potential solution for the environmental and energy problems.
However, since the limited driving range and insufficient charging infrastructure cause trouble for the EV drivers’ travel, it calls for the effective methods to guide EV drivers to select suitable charging stations and routes. For this reason, EVs have received increased interest from the scientific community.
In consideration of the limited driving range, several studies have attempted to find the optimal routes for EVs based on the framework of constrained shortest path problem (Artmeier et al., 2010;Storandt, 2012;Neaimeh et al., 2013). However, the charging behaviour was not involved in the models. Kobayashi et al. (2011) further considered the impacts of charging behaviour and established a route search method for EVs. In this method, the location of charging station is an influencing factor to select the travel routes, besides the driving range. Wang et al. (2018b) designed a geometry-based algorithm for charging guidance based on the charging request information. The algorithm considered the consistency of direction trend between charging routes and destination. Sweda et al. (2017) proposed two heuristic methods to find the adaptive routing and recharging decisions for EVs. The charging costs were involved in the solution. Besides charging processes, Qin and Zhang (2011) and Said et al. (2013) considered the impacts of queuing time on the charging station selection. The queuing theory was used to optimize the charging guidance. Several studies combined the driving time, charging time and queuing time to discuss the charging and route optimization for EVs (Yang et al., 2013;De Weerdt et al., 2016;Zhang et al., 2018). Furthermore, Wang et al. (2014) incorporated the energy constraints in the travel and proposed the energy-aware routing model for EVs. Cao et al. (2012) and Liu et al. (2014) considered the impacts of charging costs on the charging station selection to investigate the EV charging problems. Yagcitekin and Uzunoglu (2016) developed a smart charging guidance strategy based o
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