A Nash optimal distributed model predictive control for heavy-duty truck platoon to enhance operation and fuel-saving under freeway cut-in conditions

Efficient energy management and stability optimization represent critical challenges for connected automated heavy-duty truck platoons, particularly in scenarios involving external disturbances such as cut-in vehicles. To optimize the speed fluctuations and increased fuel consumption caused by the cut-in vehicles, this paper proposes a control strategy that coordinates both platoon and cut-in vehicles. First, this study introduces a Nash optimal distributed model predictive control algorithm that integrates fuel efficiency optimization to mitigate the adverse effects of cut-in vehicles. Secondly, based on the proposed control algorithm, an optimal insertion position–speed model for the cut-in vehicles is established. This model provides a reference for cutting into a stable platoon, thereby reducing the overall speed fluctuation. The results of numerical simulation experiments show that the combined use of the proposed control algorithm and model can reduce fuel consumption by 1.93 %-5.49 %, 6.43 %-12.29 %, and 7.22 %-9.88 % in low-speed, medium-speed, and high-speed environments when dealing with cut-in.