Work

Energy-Optimal Car-Following Modeling for CAVs Based on Headway Forecasting and Optimal Velocity Difference Control

Year: 2026

Type: article

Abstract: Enhancing traffic flow stability is a critical approach for achieving energy conservation and emission reduction in road transportation. While existing cooperative car-following strategies for connected and automated vehicles (CAVs) are effective, their heavy reliance on reliable Vehicle-to-Everything (V2X) communication limits practical deployment. This study proposes an energy-optimal car-following model for CAVs, introducing a regulation term based on the predicted optimal speed difference. Rather than directly using predicted kinematic variables, this mechanism adjusts acceleration based on the difference in optimal velocity between predicted and current headways. This leverages the inherent filtering of the optimal velocity function to ensure smooth control. Linear and nonlinear stability analysis confirm the model’s effectiveness in suppressing traffic disturbances and suppression of stop-and-go wave propagation, thereby laying the theoretical foundation for smoother traffic flow and the resulting reductions in energy consumption and emissions. Simulations validate the theoretical findings. Compared to the classical Full Velocity Difference (FVD) model, the proposed model achieves significant reductions in energy consumption (38.82%), CO2 emissions (39.41%), and NOx emissions (83.46%). The model also reduces rear-end collision risks, ensuring higher safety. These findings indicate that the proposed ego-vehicle predictive framework provides a communication-independent and practically viable approach for improving the energy efficiency and stability of CAV traffic flow.

Source: Sustainability

DOI: 10.3390/su18042082

Authors: Yafan Tang, Zhipeng Li

Institution: Tongji University

Language: English

Topic: Traffic control and management

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy, Climate action, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $2,896