1.Battery Electric Vehicle Charging Scheduling  

Battery Electric Vehicle Charging Scheduling In recent years, scholars have conducted various research studies in the field of battery electric vehicles (BEVs), such as BEV energy consumption analysis [13,14], sitting problems of BEV charging stations [15–17], and the optimization of BEB charging scheduling [18]. Ahn et al (2011) [19] developed an optimal decentralized charging control algorithm for BEVs connected to smart grids, using the flexible characteristics of charging interruption to reduce the power load of the grid. Sundstrom et al (2012) [20] explored the flexible charging strategy of BEVs, considering the grid power constraints, and proposed a new method for the BEV charging schedule by combining the impact of voltage and power on the grid. The strategy mitigates congestion in the distribution network while meeting the changing needs of BEVs. Flath et al (2014) [21] pointed out that the charging demand for large-scale BEVs might greatly increase the power load of the grid, and an appropriate coordinated charging strategy could effectively reduce the impact of high power demand on the grid. Although BEV scheduling and BEB scheduling have many similarities, there are fundamental differences in operation management: (1) the former have irregular driving routes and flexible charging locations, while the latter have fixed itineraries and charging stations; and (2) the former make charging decisions based on different origins and destinations of the BEVs, while BEBs follow their fixed travel routes when making charging schedule decisions. Therefore, compared with BEVs, the charging scheduling of BEBs has stronger constraints and a higher level of homogeneity in charging demands.

2. Optimization of BEB Charging Station Locations

The optimization of BEB charging station locations has a large impact on the daily charging schedules of BEBs. There are many studies on the charging station locations and infrastructure planning of bus transit networks [22–24]. Xylia et al (2017) [25] developed an optimization model for Stockholm’s bus network in order to ensure the availability and efficiency of the charging infrastructure. Wei et al (2018) [26] optimized the deployment of the BEB system from the perspective of spacetime characteristics, minimizing the total costs of BEB procurement and charging station allocation. Lin et al (2019) [27] studied the planning problems associated with large-scale BEB charging stations, considering multi-level networks and grid power constraints; the objective was to minimize the total construction costs. An (2020) [28] employed the infrastructure optimization of BEBs under uncertain demands, and considered the impacts of battery capacity, weather factors, and road traffic conditions on the optimization solutions. Wang et al (2022) [29] proposed a combinatorial optimization model for the charging pile configuration and fleet scheduling of BEBs, aiming to determine the optimal battery capacity and fleet sizes of BEBs. Ferro et al (2023) [30] studied the joint optimization problem associated with the location and capacity of BEB charging stations with the objective to minimize the total operating costs. The above studies on the planning and deployment of BEB charging infrastructure focus on decision-making on a strategic level. The results can provide useful references for the construction of urban BEB infrastructure.

3.  BEB Charging Scheduling

In the daily operation of BEBs, there are three basic types of electrical energy replenishment: overnight centralized charging, daytime opportunity charging, and battery exchange [31,32]. Overnight centralized charging means that BEB charging activities occur at charging stations during non-operating hours at night. With battery swapping, a BEB can replace its low-charge battery with a fully charged one at a battery swap station in minutes. Opportunity charging usually happens in the daytime between trips of a BEB; it often takes a shorter time but comes with a higher charging cost compared to overnight charging. A few authors considered the scheduling problem associated with overnight centralized charging. Houbbadi et al (2019) [33] proposed an overnight charging schedule that takes into account the impact of battery degradation costs on the charging scheme. Zheng et al (2022a) [34] developed a set of optimal charging schedules for the overnight centralized charging of BEBs while considering peak–valley electricity prices and battery degradation costs. The proposed schedule effectively mitigates charging activities in peak hours and saves charging costs. In their study, the authors neither considered the time-varying power of charging piles nor daytime charging. Zheng et al (2022b) [35] considered the uncertain charging times of BEBs in the overnight centralized charging scheduling problem; their objective was to minimize the expected total charging costs. They proved the NP-hardness of the problem and developed a scenario reduction-based enhanced sample average approximation (eSAA) approach, as well as an improved genetic algorithm, to solve large-scale instances. The authors focused on overnight charging and did not consider opportunity charging and the impact of the peak–valley price. Jahic et al (2019) [36] optimized the BEB centralized charging schedule to balance the high electricity costs in the daytime peak period and load peak constraints.

Some studies focus on the opportunity charging of BEBs. Abdelwahed et al (2020) [32] investigated the opportunity charging scheduling of BEBs, and developed two optimization frameworks based on discrete time and discrete events. The authors assumed that all BEBs were initially fully charged and that the power of all charging piles remained constant. The work did not consider the impact of grid power constraints on charging schedules. He et al (2020) [37] considered the impacts of electricity demand costs on the opportunity charging schedule, and proposed a network modeling framework for optimizing the BEB charging schedule. Casella et al (2021) [38] proposed an optimal BEB charging schedule based on the bus demand response plan under the grid load constraint. They did not consider the impact of the peak–valley electricity price and the variable charging power of the charging schedule. Zhang et al (2021) [39] studied a BEB opportunity charging scheduling problem that considered battery loss costs and nonlinear charging. The problem was described as a nonlinear programming model, which was solved by the branch and bound algorithm. The authors assumed that once a BEB was charged, it must be charged to its initial state of charge. Liu et al (2021) [12] considered the BEB charging scheduling problem with real-time power considerations of charging piles, and proposed a column generation algorithm to solve the problem. They assumed that all BEBs had a fixed charging duration; however, BEBs may require different charging times in the real-world based on their remaining electricity power levels.

This entry is adapted from the peer-reviewed paper 10.3390/su151310728