## 1 Introduction
Therefore, to accomplish the dual objectives of the optimal charging and passing benefits, a combination of signalised intersections located upstream and downstream of wireless charging facilities allows vehicles to pass through intersections at reduced speeds without stopping and with power replenishment, increasing the traffic efficiency and mileage. Subsequently, various energy-consumption models for electric vehicles (EVs) and EFVs, wireless charging, and eco-driving control have been explored to satisfy the requirements of vehicles travelling in complex environments. To improve the existing energy-consumption models for EVs and EFVs, [PERSON] et al. [30] integrated the energy-consumption functions of individual EVs for different phases into an aggregate energy-consumption model by simplifying the driving action of EVs at signalised intersections. [PERSON] et al. [31] used the minimum principle theory to derive new car-following models of EVs with zero and non-zero initial states from the optimal energy-consumption model. Numerical experiments demonstrated the effectiveness of this model in terms of the vehicle position, velocity, and acceleration distributions. [PERSON] et al. [32] studied the efficiency of regenerative braking energy and impact of auxiliary systems on the vehicle energy consumption and developed an electric vehicle energy model that utilised different deceleration levels to determine the instantaneous regenerative braking energy and estimate the electric vehicle energy consumption using the vehicle speed, acceleration, and roadway grade updated every second as input variables. On this basis, [PERSON] and [PERSON] [33] proposed and validated a microscopic backward highly resolved power-based EFV energy-consumption model (EFV-ECM) using the vehicle speed, actual weight, roadway grade, and vehicle characteristics updated every second as input features combined with the actual EFV driving data. Later, [PERSON] et al. [34] extended and validated the EFV-ECMusing global and regionalised sensitivity analysis techniques to identify model inputs that most strongly influenced the variability of simulated energy consumption, which led to average or extreme model scenarios. The obtained results revealed that the EFV-ECM effectively reproduced the inherent uncertainty of energy-consumption measurements during real-world EFV driving. Therefore, this study employs the EFV-ECM as the energy-consumption model to calculate the instantaneous power consumption. To increase the wireless charging efficiency, [PERSON] and [PERSON] [35] proposed a method for evaluating the performance of a dynamic charging power system for EFVs that integrated traffic and energy dynamics using a freight delivery service operating with medium-sized EFVs in an urban area as a reference scenario. This method evaluated the impact of wireless charging delays and vehicle speed on charging while driving as well as the effect of the EFV charging demand on energy consumption. [PERSON] et al. [36] used a car-following model and lane-change rules to investigate the movement of EVs in a WCL, driving behaviour of each EV in a two-lane system with a WCL, and effect of WCL on the movement of EVs. [PERSON] et al. [37] proposed a new method for comparing different energy-consumption models and calibrations to explore the effect of WCL on the travel time and energy consumption of EVs in a two-lane system. [PERSON] et al. [38] evaluated the longitudinal safety of EVs equipped with a partial WCL on freeways using time-exposed time-to-collision and time-integrated time-to-collision as the safety evaluation indicators. The obtained results revealed that a low state of charge (SOC) produced a significant negative impact on longitudinal safety when an EV was driven in the partial WCL. [PERSON] et al. [39] proposed a cooperative control method for mixed fleets including electric and fuel vehicles based on the following four objectives: driving safety, traffic efficiency, passenger comfort, and energy management.