Network equilibrium models with battery electric vehicles

Multiple Types of Plug-In Charging Facilities’ Location-Routing Problem with Time Windows for Mobile Charging Vehicles

“…The subset F 0 = {γ + βα + 1, · · · , γ + (β + 1)α } of F is the dummy nodes of depot. In Figure 2, β = 4 and α = 2, so F = {11, 12,13,14,15,16,17,18,19, 20}, F = {11, 13, 15, 17, 0}, and F 0 = {19, 20}, which is not marked in Figure 2.…”

“…To increase the market share of the BEVs, many governments are planning to deploy charging stations in their regions, and a number of other strategies tackling their range have emerged [16]. But, it seems unrealistic to expect that range anxiety can be eliminated in the near future.…”

“…Until now, some alternative methods have been proposed, such as plug-in charging [16], 15 battery-swapping [19][20][21], dynamic wireless recharging [22], and charging lanes [23]. The most common charging method for BEVs is plug-in charging, which is static conductive charging via a cable and a vehicle connector when the BEV is parked.…”

“…S1 (11) S4 (17) C10 (10) C4 (4) C7 (7) C8 (8) C9 (9) S3 (16) C6 (6) C1 (1 For the convenience of model building, the definition of some sets is stated here. For the depot, we set both 0 and + 1 to denote it, which is the same node.…”

“…Constraint (15), Constraint (16), and Constraint (17) keep track of the state of charge of the battery for the customer nodes, charging the dummy nodes and deport, respectively, and ensure that the battery state of charge never falls below the electric demand of the next customer node. Because, in the early assumptions, all of the vehicles are full when they leave the depot {0}, therefore Constraint (16) and Constraint (17) are different from the power when leaving the node where the electricity leaving node is equal to the sum of the remaining charge level, y i , and the recharging amount, F i , in Constraint (16), while equaling the capacity of battery Q in Constraint (17).…”