## 1 Introduction
EAD has been tested on internal combustion engine vehicles (ICEV), leading to savings that can range from 5% to 10% for high entry speed and 7% to 26% for low entry speed [20]. EAD on CVs relies on signal phase and timing (SPaT) information. SPaT is a series of messages based on vehicle-to-everything (V2X) communication systems as established by SAE International J2735 standard [21]. It is intended to be a guideline for transportation authorities and the automotive industry to control and communicate within traffic systems [22]. SPaT provides details on the states of the traffic lights, timing data, intersection location, and road layout. Data are transmitted at a 10 Hz rate. SPaT can provide information to driver assistance systems to prevent accidents and to avoid nonessential throttle or braking [23]. Most EAD approaches rely on communications between vehicles and their surrounding environment: pedestrians, vehicles, infrastructure, network, and devices [24]. However, in realistic urban scenarios, not all the information may be available during navigation [25]. Developments of EAD for EVs assume full connectivity and access to SPaT information and consider vehicle queues and battery discharge prediction. The vehicle models used to validate EAD strategies are typically kinematic or simplified longitudinal dynamic models of ICEVs [26]. However, a powertrain model of the vehicle provides deeper insight on how power consumption and regeneration relate to vehicle dynamics, road characteristics [27], battery charging, discharging, and aging [28], and vehicle motion [29]. Power-efficient longitudinal control for AVs in urban traffic can be achieved when an EAD system merges SPaT data with a valid model of the vehicle, facilitating its migration into a physical platform [30]. Through electric drives, EVs are capable of restoring electrical power to their battery by means of regenerative braking systems (RBS) [31]. These systems retrieve mechanical power from the vehicle and convert it into electricity while reducing the speed of the vehicle during navigation [32]. Power savings and regeneration depend on speed, traveled distance, vehicle [33], and motor features [34]. Algorithms developed to generate target velocity profiles for EAD are known as green light optimal speed advisory (GLOSA) systems [35]. They determine ideal longitudinal speed setpoints to optimize power, travel time, and comfort [36]. Implementations of these systems are based on several approaches. For instance, discontinuous functions are employed to follow recommended speed profiles that would reduce fuel consumption and travel time [19]. Other approaches use rule-based systems. They have been deployed on physical platforms using separate strategies for green, yellow, and red lights [20]. This approach has also been extended to other scenarios where a vehicle can have access to V2X, and whether it finds other vehicles in the environment [24]. On the other hand, fuzzy control systems have been coupled with sliding control to manage energy in EVs and use an RBS [31]. Correspondingly, a dynamic programming approach considers passenger preferences regarding time efficiency, comfort, and fuel usage based on a dataset. It is then deployed in a simulator to validate its performance [37]. Another example is the use of evolutionary methods, where a genetic algorithm is employed to optimize the braking energy recovery efficiency. The study demonstrates its better performance compared with inexperienced and expert drivers [34]. The use of human-like reasoning has led into the use of neural networks. Fuel economy and reduced emissions were achieved by a neural network that uses SPaT data, inter-vehicle communications, and onboard sensing [25]. In hybrid approaches, dynamic programming is used to optimize the longitudinal speed of the vehicle considering energy efficiency. Model predictive control is used to compute the short horizon reactive behavior [26].