## 5 Co-Optimization of Eco-Driving Strategy and EMSs for HEV/PHEVs
### Double/Multi-Vehicle Scenarios
[98] integrated a complete ACC and EMS through a proposed Deep Deterministic Policy Gradient-Based ECOlogical driving strategy (DDPG-ECO) that was based on deep reinforcement learning. The weights of multiple objectives were analyzed to optimize the training results. The simulation results showed that the DDPG-ECO approach achieved over 90% of the performance of DP-based methods while also ensuring good car-following performance. In conclusion, the integration of ACC and EMS aims to reduce fuel consumption by using specific driving cycles to approximate the velocity of the preceding vehicle without taking into account dynamic driving conditions. On the other hand, the integration of PCC and EMS focus more on predicting a preceding vehicles' state through dynamic traffic information, thereby further improving fuel economy. Both approaches prioritize safety and fuel economy when optimizing controls. * Multi-Vehicle Scenarios:  
Figure 9: The scenario of double-vehicle considering the safety constraints with preceding vehicle. The multi-vehicle scenario has the potential to reduce air resistance for each vehicle, which can, in turn, increase road capacity, reduce fuel consumption, and improve road safety [99]. Therefore, there has been increased attention on the cooperative optimization of EMSs and eco-driving for HEV/PHEV platoons, and great efforts are being made towards proposing holistic approaches in this area. The most classic framework, as shown in Figure 10 and presented in [76; 77], is a hierarchical energy management control strategy for a group of connected HEVs. At the higher level, MPC is used to incorporate SPaT information to predict the optimal velocity profile over a finite time horizon. At the lower level, the adaptive ECMS and DP are used separately as controllers to achieve power distribution by tracking the optimal speed of each HEV's higher-level controller. The effectiveness of the proposed control strategy has been validated through simulation results. However, it should be noted that the propulsion and recuperation efficiencies of HEVs were considered to be constant in this work. To reflect operating characteristics precisely, [PERSON]. [78] considered the efficiency feedback of the two characters based on the above hierarchical energy management control strategy. The fuel economy of the system could be improved, and additional benefits could be achieved by synergizing the reduction of red light stopping, collision avoidance, and cooperative platoon information. Around the same time, [PERSON]. [100] proposed a real-time MPC scheme for connected HEVs that relied on look-ahead traffic information, as well as a chain GP-based predictor to obtain the preceding vehicle's speed, assuming that the vehicle aimed to maintain an average speed that was reflected through the traffic density. The simulation results showed that the proposed method could avoid violations of the spacing corridor to ensure traffic safety and reduce energy consumption without requiring significant emergency acceleration or braking behavior. Moreover, several related works, such as [79; 101; 102], have confirmed the fuel-saving potential in platoons of HEV/PHEVs. However, optimizing the control strategy for a platoon and the EMS of each HEV simultaneously can be challenging due to the highly coupled nature of the nonlinear augmented system. Most of the current literature assumes a perfectly homogeneous traffic flow, which overlooks human-related factors. As heterogeneous traffic flow becomes more prevalent in multi-vehicle scenarios in the near future, it will further increase the complexity of traffic conditions, thus posing a more significant challenge to improving both the mobility of the traffic system and fuel economy. Figure 10: The hierarchical control framework of multi-vehicle scenario. Finally, by integrating EMS and eco-driving strategies techniques, we can better address the challenges posed by human behavior and technological advancements in the context of ITS technology.