Nonlane-Discipline-Based Car-Following Model for Electric Vehicles in Transportation- Cyber-Physical Systems

This paper proposes a new car-following (CF) model incorporating the effects of lateral gap and roadside device communication to capture the characteristics of electric vehicle (EV) traffic stream in transportation-cyber-physical systems. Stability of the proposed CF model is analyzed using the perturbation method. Furthermore, the energy consumption of the EV traffic stream is investigated based on the drive cycles produced by the proposed model. Numerical experiments analyze three scenarios: start, stop, and evolution processes for the scenarios of no lateral gap, lateral gap, and lateral gap with roadside device, respectively. Results demonstrate that: 1) the nonlane-discipline-based model is more responsive than the lane-discipline-based model; 2) the nonlane-discipline-based model for the EV traffic stream consumes more energy in the acceleration phase and recuperates more energy in the deceleration phase compared with the lane-discipline-based model; and 3) the nonlane-discipline-based model with roadside device communication for EV traffic stream consumes more energy in the acceleration phase and recuperates more energy in the deceleration phase than the model without roadside devices.