## 5 Co-Optimization of Eco-Driving Strategy and EMSs for HEV/PHEVs
### Double/Multi-Vehicle Scenarios  
The previous subsections covered the integration of ITS information and EMS for single-vehicle scenarios. These ideas can also be applied to double/multi HEV/PHEVs to further enhance overall performance or for a fleet with regards to fuel economy and traffic efficiency. * Double-Vehicle Scenarios: The advent of ITS technology has made it more convenient to acquire V2I/V2V information about the surrounding traffic. In the double-vehicle scenario, cooperative optimization is typically carried out using a car-following model that takes into account the interaction between the two vehicles in order to balance fuel economy and safety. According to [3], two categories of strategies can be distinguished for double-vehicle scenario optimization. The first category is mainly based on Adaptive Cruise Control (ACC) systems, which aim to reduce traffic accidents, increase driving comfort, and improve traffic flow throughput [90]. Instead of the safety and traffic efficiency objectives, the integration of ACC and EMS means the optimization of them simultaneously, thereby adding another target that aims to improve fuel economy. In the car-following scenarios, the velocity of the preceding vehicle has a great impact on the following vehicle as an input for devising the following vehicle's EMS. A typical example is [91]; the authors developed an ACC system based on a nonlinear MPC for intelligent HEVs. This system took into account traffic safety, fuel economy, and ride comfort, therby ultimately improving energy efficiency and the integration of the control system. However, this integration of the ACC and EMS is usually carried out by predefining the preceding vehicle's velocity, which can be done by following certain rules. A similar work is [92]; an adaptive tube-based nonlinear model predictive control (AT-NMPC) approach was introduced for designing autonomous cruise control systems, which guaranteed robust satisfaction of the specified constraints, even in uncertain conditions, and enhanced the system's performance by adapting to changesin the vehicle control-oriented model. Using a different approach in [93], an ecological ACC based on motion-dependent heuristic dynamic programming was developed to achieve multi-objective optimization. As Figure 9 illustrates, the aforementioned point about the pre-defining velocity of the preceding vehicle based on traffic light time when a signalized intersection is taken into account and the second category are based on Predictive Cruise Control (PCC). EMSs utilizing PCC are designed to make the most of future information by predicting the velocity of the preceding vehicle and using a predictive control algorithm to determine the optimal velocity and power split for the subject vehicle while considering the traffic disturbances [94]. Specifically, PCC-based EMSs are designed with the goal of maximizing the use of future information, which is achieved through the prediction of the preceding vehicle's velocity. An example is [95]; the work suggested an estimation method based on actual and past inter-vehicle distance data, as well as information on traffic and upcoming traffic lights, by employing a set of nonlinear, autoregressive (NARX) models to predict traffic behavior using a cooperative adaptive cruise controller (CAC) to achieve better fuel economy because of the advantages of information prediction. Moreover, some machine learning methods were introduced to the part of the preceding vehicle's prediction due to the complexity and stochastic nature of dynamic traffic, such as the Bayes network model used in [96], which showed a better prediction performance than the \"constant acceleration\" and \"constant velocity\" methods. The Gaussian process model was used in [97], which predicts leading vehicle velocity based on time series data and mean traffic flow speed drawn from cloud data. By the way, these machine-learning methods can also be leveraged to optimize complex systems with inconsistent objectives and stringent constraints. For example, Ref.