Su et al. [9] proposed a two-stage multi-period policy to decide which charging station the EV should be assigned to and then recommends the best driving route for each EV taking into account traffic congestion.  Boyacı et al. [10] developed a multi-objective MILP programming considering EV relocation and charging requests in ECSS.  Boyacı et al. [11] presented a discrete simulation model to make operational decisions of vehicle and staff relocation in a CSS and determines the feasibility of vehicle assignment based on available battery levels. Xue et al. [12] developed a comprehensive evaluation framework that shows that vehicle availability and relocation management are the most important factors affecting the performance of ECSS in China. Bruglieri et al. [13] presented the vehicle relocation problem in a station-based one-way ECSS. Movement between stations is performed using bicycles, and then vehicle relocation is performed among stations. The predicted trip requests were considered, and all of them were assumed to be met. Liu et al. [14] proposed a bi-level optimization model that maximizes profit and minimizes operating cost by considering demand-side management for EVs’ distribution and station capacity and location. Liu et al. [15] first determined the optimal physical charging power of the EV fleet and then constructed a decision model to describe whether users accept the scheduling. Based on the optimal scheme of the time-sharing subsidy, the EV fleet size considering users’ subjective decisions was obtained. Ref. [16] addressed the imbalance of vehicles by using a simulation to make prices change dynamically to influence trip demands, while Chow and Yu [17] provided a bidding-based vehicle-sharing model. The fleet-size and trip-pricing problems were combined in [18], taking into account the dynamic changes of trips and the relocation of people and vehicles.

In order to solve the problems arising from demand fluctuations and the time-varying state of charge of EVs, Huang et al. [19] presented two decision problems at the strategic and operational levels and proposed a MINLP model to maximize the profit of operators in one-way ECSS. Lu et al. [20] proposed a bi-level nonlinear model to study the pricing and relocation problem. Combinations of different pricing strategies and relocation schemes were analyzed through case studies. Xu et al. [21] used reinforcement learning technology to study the order-scheduling problem, and the goal was to maximize instant rewards and future profit. Lyu et al. [22] studied the multi-period multi-objective online ride-matching problem. They developed an efficient online matching policy to balance the trade-offs among multiple goals. Özkan and Ward [23] proposed a linear-programming-based matching policy that especially accounts for passenger patience, with the goal to maximize the total number of total passengers being served. Hu and Zhou [24] studied the dynamic matching where, if the driver or passenger waits for too long, either of them may leave the platform.

The matching time interval and matching distance are two important factors affecting results in an online matching system. More orders will be fulfilled if the platform extends the matching time interval, but some users may leave the platform if the matching time interval exceeds their patience. Meanwhile, the pick-up distance will be reduced with a shorter match radius, but the matching rate may decrease as well. Bian and Liu [25] considered the individual requirements of users for different inconvenient factors, including waiting times, to determine the optimal matching between passengers and vehicles. Yang et al. [26] proposed to segment the various stages of the online matching process in the ride-sharing market. Abdolmaleki et al. [27] proposed an optimization problem with consistency constraints to synchronize the schedules in the transportation network so that the total waiting time for users’ transferring is minimized.

User flexibility was also considered in our one-way ECSS. Correia et al. [28] proposed a model measuring the impact of user flexibility on the vehicle-assignment problem in a one-way CSS. Stiglic et al. [29] replaced the original target point with meeting points where the user can get on and off the vehicle within a certain distance from the original target point. A combination of spatial and temporal flexibility was considered to optimize the vehicle assignment at the same time [30]. A time window was set at the beginning and end of the trip, while locations within a certain radius of the original target point were considered as acceptable. The effect of flexibility on the car-sharing system can be summarized in some parts: increasing the number of matched users, improving vehicle utilization and the number of requests fulfilled, and reducing costs and the fleet size [8].

Taking the key factor that an EV’s trip mileage is limited by battery level, Zhang et al. [31] enabled users to finish longer trips through driving two vehicles in sequence. A new time–space–battery network flow model was proposed to find the best vehicle assignment and relaying decisions so as to achieve higher vehicle utilization.