## 5 Co-Optimization of Eco-Driving Strategy and EMSs for HEV/PHEVs
### Single-Vehicle Scenarios  
In the past decade, most research literature in the cooperative optimization of eco-driving and Energy Management Systems (EMS) for HEV/PHEVs has focused on the single-vehicle scenario, where the most valued target is to optimize the power split considering traffic or road information and progressively taking safety constraints into account, but rarely considering other vehicle interactions, such as overtaking and lane-changing. * Cooperative Optimization for HEVs: In most situations, the cooperative optimization framework for HEVs has been designed to minimize energy consumption by first optimizing the speed profile. Next, the power distribution is optimized by following the ideal speed suggested by the eco-velocity planning system. As Figure 8 shows, this concept relies on a two-level optimization framework, which includes both the vehicle and powertrain levels. However, the complex nature of driving in real-world scenarios makes it impractical to optimize the entire velocity trajectory. Therefore, the analysis boundary is typically restricted near to the intersection, thus allowing better integration of EMS with the traffic and road conditions, such as SPAT information and speed limits. Numerous methods have been proposed to tackle the two-layer problem. For instance, for only considering one signalized intersection, [PERSON]. [67] decomposed the hybrid optimal problem into two subproblems. First, the optimal speed profile was computed by solving a nonlinear time-varying optimal problem. Then, the Krylov subspace method was employed to improve computational efficiency. After that, the optimal torque split ratio and gear shift schedule were determined by combining PMP and numerical methods in the bi-level MPC framework. For considering continuous intersections, [PERSON]. [68] introduced a novel cooperative optimization framework for HEVs, which was similarly designed to minimize energy consumption by optimizing velocity trajectories first, then optimizing the power split based on a genetic algorithm to solve the complex fuel consumption model of HEVs. The simulation results of the proposed optimal speed algorithm were compared to the results of a real driving test and a single-intersection optimization algorithm. These comparisons showed that the proposed strategy was more effective in reducing fuel consumption and intersection passing time. Finally, as an example for considering as many real traffic scenarios as possible, Ref. [69] developed an MPC-based strategy that fully considered the three main objectives: safe driving, energy management, and exhaust emission reduction. To address these objectives, the study designed a driving scenario classifier to determine the corresponding vehicle mode. Furthermore, the simulation was conducted in a realistic urban traffic environment using Simulation of Urban MObility (SUMO), and the results demonstrated that the proposed strategy guaranteed safe driving throughout the entire trip, reduced fuel consumption and exhaust emissions, and kept the battery in a healthy SOC range. The study showed the effectiveness and robustness of the proposed strategy for potential online applications.