## 2 Literature Review  
As an extension of the classic vehicle routing problem which considers EVs, the EVRP, has aroused increasing interest in recent years [6; 13; 17; 18; 19; 20; 21; 22]. As a variant, the EVRPTW was first proposed by [PERSON] et al. [23]. [PERSON] and [PERSON] [24] investigated the EVRPTW considering partial recharge rather than full recharge. [PERSON] et al. [12] introduced an EVRPTW considering various types of EVs. According to different recharging forms, [PERSON] et al. [25] studied four variants of the EVRPTW to optimize the travel costs. [PERSON] and [PERSON] [26] further researched the EVRPTW with three kinds of RSs (normal, fast, and super-fast) to minimize the total recharging cost while maintaining the minimum number of EVs. RSs play an indispensable role in EV operations. [PERSON] [27] adopted both RSs and battery-swapping stations to obtain recharged EVs to optimize the EVRPTW. [PERSON] et al. [28] compared two proposed models for the EVRPTW, which are recharging process linearity and non-linearity. Based on the EVRPTW, [PERSON] et al. [29] established a model incorporating the priority of different time windows into costs, to better satisfy customer demands. The above investigations mainly focus on the transporting demands from a depot to a set of customers by a fleet of EVs which minimize the total cost. Considering the limited driving distance of EVs, effective methods must be developed to accurately monitor the energy consumption of EVs to ensure that the EVs never run aground. In practice, energy consumption is not a linear rate of travel distance and may be affected by multiple factors, as most research has assumed [6; 12; 23; 28; 29; 30; 31]. The detailed energy consumption model was first used in a pollution-routing system (PRP) by [PERSON] and [PERSON] [32], and then much research on EVRP started to track the state of charge (SoC) of EVs. Plenty of studies have introduced uncertainties into the vehicle routing problem (VRP). It is usually assumed that the travel time between two nodes, the demand, or service time of customers are uncertain [33]. Stochastic programming models are often employed for this kind of problem [34; 35]. For the situations with high uncertainty (such as unknown probability distribution) or serious risk, however, stochastic programming models will not apply. Robust optimization (RO) as a modeling methodology is usually adopted in solving the VRP under high uncertainty. According to RO theory, part of (or even all) data are uncertain and only known to belong to some uncertainty sets [36]. If there is no probability distribution, information about such uncertain data, a solution constructed by RO, will be feasible for any realization of the uncertainty in a given set [37]. There are several studies investigating VRP under uncertainty by robust optimization [33; 38]. Recently, researchers also began to focus on EVRP under uncertainty. The uncertainty in the energy consumption of EVs was examined by [PERSON] [39], and they also modeled the problem within an RO framework. [PERSON] et al. [40] researched the problem of finding the best path between an origin and destination pair, where each node has an RS with random availability. Under the uncertainty of customers, [PERSON] and [PERSON] [41] solved the problem of simultaneously routing EVs and locating RSs through RO. [PERSON] et al. [13] considered the stochastic waiting time at RSs in the EVRPTW. Although a lot of different types of uncertainties have been touched on in the existing investigations, there are few studies on energy consumption uncertainty of the EVRP. The work of [PERSON] et al. [9] is the first concerning the EVRP with energy consumption uncertainty (EVRP-ECU), in which the energy consumption is influenced by vehicle loads. They assumed that EVs need not be recharged during transportation. However, EVs usually cannot complete their mission with only the initial charge due to the uneven distribution of RSs and customers in cities. [PERSON] et al.