### Related Works  
Vehicle platooning has been a subject of interest for several years in the transportation research community. As stated above, the focus has shifted towards improving the energy efficiency of platooning systems, which has led to a surge in research on this topic. In this section, some of the relevant work that has been carried out in the area of our discussion is reviewed. A number of studies have investigated the benefits of platooning in terms of fuel consumption and greenhouse gas emissions. For instance, in [9] it is demonstrated that platooning can reduce fuel consumption by up to 13.6% and travel time in connected and automated vehicles. Additionally, in [10], the authors conduct experiments using computational fluid dynamics (CFD) to explore the issue for different platoon formulations and they conclude that for specific formulations, energy savings can reach up to 10.3%, and at the same time aerodynamic reductions can be reduced up to 24%. To achieve these benefits, several techniques have been proposed for optimizing platooning systems. One common approach is to use adaptive cruise control. In [11], the authors investigate the effect of that control method in terms of stability, safety, and traffic flow behaviour. A pioneering approach of that method, cooperative adaptive cruise control (CACC) [12], which is a variant of adaptive cruise control, allows vehicles to communicate with each other to coordinate their movements. The ecological approach of CACC has been shown to improve fuel efficiency and reduce emissions by minimizing the distance between vehicles while maintaining a safe following distance. Another approach is to use predictive control algorithms to optimize platoon performance. In [13], a model predictive control (MPC) is implemented and compared with other current methods. The algorithm uses a model of the platoon dynamics to predict the future behaviour of the vehicles and adjust the control inputs accordingly. Another MPC method that considers energy reduction utilizing the air-drag coefficient, similar to the approach of this study, is proposed in [14]. Despite the fact that these strategies show great potential, both ACC and MPC methods require knowledge of the dynamics of the system. In recent years, several studies have focused on using dynamic programming and machine learning techniques to optimize platooning systems. Dynamic programming in [15] helps the control system to explore the optimal trajectory that reduces fuel consumption based on road topography maps, and the final scheme is stated as a look-ahead control strategy. In [16], a reinforcement learning-based algorithm for vehicle platooning is proposed that provides a solution to the longitudinal control problem considering efficiency policies. The algorithm uses a deep neural network to learn the optimal control policy for the platoon, which is then used to adjust the control inputs in real time. These methods are quite promising; however, they require high-end process systems and several restraints have been made throughout their experimental implementations. A different approach is provided in [17], where a control method is developed to provide a solution for the platoon rooting problem in a real-world metropolitan network using small predefined inter-vehicular distances to minimize fuel consumption. Concerning the extremum-seeking approach in [18], the authors developed an ESC method to reduce aerodynamic drag on a single truck by utilizing the parameters of a wind deflector installed on the roof of the tractor cab. Although this study is not focused on platooning, the patent can be adopted for the case in future works. Overall, these studies demonstrate the potential benefits of platooning in reducing energy consumption and greenhouse gas emissions and highlight the importance of developing efficient control strategies for platooning systems.