## 2 Literature Review
Related efforts are stated in [35] that focuses on linearizing the nonlinear function of the EV charging operation, considering piece-wise linear approximations, applied for partial recharge and different charging technologies. From other perspectives, the user satisfaction is also considered in the EV charging stations deployment, which is analyzed in [36] via three indicators: charging convenience, charging cost, and charging time. The same authors of [9] published in [8] a linear approach for the EVRP; furthermore, the three-index formulation for the CVRP is not used, particularly for the parcel flows through the arcs of routes. In this sense, the drawback comes with the charging stations' installation limited to the customers location, which does not reflect the complete reality. Contributions corresponding to 2020 are framed within topics on nonlinear partial recharge, time windows, multiple depots, bin packing problem, battery swapping, and battery efficiency. Likewise, some efforts are made around the analysis of influencing factors for EV market penetration. This is the case for the work reported in [37], which proposes the technological progress assessment of EVs for logistics purposes, considering physical and performance features. The work performed in [38] is towards the same direction, making a comparison between the conventional EV and other low-emission freight transportation means technologies, such as biodiesel, natural gas, and fuel-cells. In [39], the EVRP with time windows and multiple recharging options is investigated, with partial recharge and battery swapping. The formulation represents a cost-efficient routing strategy, since the driver can recharge the battery according to the energy requirement for traveling completion or choose battery swapping for purposes of saving routing time. In the charging process context, the authors in [40] introduce the EVRP with load-dependent battery discharge and nonlinearities in the charging process, fitted within a linearized mathematical model. In the same streamline, the proposal in [41] works with a fuzzy optimization model taking into consideration the uncertain behavior of some parameters, such as travel time, service time, and battery energy consumption. Like other contributions mentioned above, partial recharge is allowed with the purpose of making the model more practical from a realistic point of view. In order to avoid the installation of charging stations, in [42], the option to open multiple depots is shown to comply with the demand of customers using a fixed number of EVs that are fairly distributed on each corresponding depot. A more advanced approach is proposed in [43] where the EVRP with multiple depots is proposed, incorporating two-dimensional weighted items for each customer demand, as part of the bin packing problem that is also considered as an NP-hard problem. Given the computational complexity, the formulation is solved using variable neighborhood search and space saving algorithms for the EV routing and bin packing problems, respectively. Similar to the contributions in [30], in [44], the routing problem for PHEVs and power management optimization is integrated. First, PHEVs tours are computed, then the driving cycle profile of each tour is obtained as a function of the customers assigned to that tour. This information is the data input to find optimal power distribution from the two energy sources installed in a PHEV. In contrast with traditional approaches, a prominent focus to overcome the waiting times of recharge and the so called range anxiety is posed in [5], combining the EVRP with mobile battery swapping vans, both synchronized at a designated time and space. In the context of battery efficiency, the research in [45] implements a simulator for an electric truck to achieve the least-energy delivery route, taking into consideration the nonlinear model of the battery and the change of parcel weight over the course of the route.