Coordinated Planning of Fixed and Mobile Charging Facilities for Electric Vehicles on Highways

This paper presents a bilevel planning framework to coordinate truck mobile chargers (TMCs) and fixed chargers (FCs) on highways to promote charging flexibility and provide more choices for electric vehicle (EV) users. A collaborative location optimization (CLO) approach is developed at the upper level to optimize the location of charging stations. Furthermore, a collaborative capacity optimization (CCO) approach is formulated at the lower level to optimize the capacity of TMC and FC at candidate stations. In the proposed framework, origin-destination (OD) analysis, Floyd algorithm and Monte Carlo simulation (MCS) are employed to generate the spatial-temporal distribution of charging demand based on historical data. An improved income approach (IIA) is then developed to well capture the heterogeneity of EV users' charging behavior. The waiting cost of EV users is estimated by their value of time (VOT), which helps them to make a better choice between TMC and FC. To solve the optimal model easily, the big-M method is applied to linearize and convert the nonlinear problem into a mixed-integer linear programming (MILP) model. Meanwhile, the analytical target cascading (ATC) technique is employed to realize the data exchange process between the upper and lower layers. Finally, numerical study demonstrates the effectiveness of the proposed framework and method.

Automated summary

This paper proposes a bilevel planning framework for coordinating truck mobile chargers (TMCs) and fixed chargers (FCs) on highways to enhance charging flexibility for electric vehicle (EV) users. A collaborative location optimization (CLO) approach is developed to determine optimal charging station locations, while a collaborative capacity optimization (CCO) approach optimizes the capacity of TMCs and FCs. The framework employs various techniques, such as origin-destination analysis, Floyd algorithm, and Monte Carlo simulation, to generate charging demand distribution, and utilizes the improved income approach (IIA) to capture heterogeneity in EV users' charging behavior. The proposed framework and method are demonstrated to be effective through numerical study.