1. Introduction
With climate change threatening the future of our environment and society, implementing immediate and effective strategies to curb Greenhouse Gas (GHG) emissions is the need of the hour. The transportation sector is one of the guiltiest parties, accounting for 35% of the worldwide energy consumption [
1]. Since road vehicles, especially passenger cars and road freight transport vehicles, account for 86% of the global share [
2], global regulatory targets and customer demand are pushing the automotive industry to develop vehicles with improved fuel economy to reduce GHG emissions [
3].
Along with GHG emissions, road congestion is a current problem for transport policy at all levels. According to Joint Research Centre (JRC), the cost of road congestion in Europe is estimated to be over €110 billion a year [
4], not to mention the dramatic effects that traffic has on the increased air pollution [
5] and environmental noise [
6]. This particularly rings true in urban areas, where traffic lights, although being vital for allowing pedestrians and competing flows of traffic to safely cross busy intersections, may lead to increased congestion levels. Besides pollution and congestion, road safety remains a major societal issue [
7,
8]: only in Europe, more than 19,000 people died on the roads in 2021 and, despite being reduced by 31% compared to 2011 levels, these numbers should continue to fall [
9].
In this framework, integrated with the feasible technical solutions aimed at improving the efficiency of current propulsion systems [
10], the mass adoption of Connected and Automated Vehicles (CAVs) may represent an opportunity to tackle the abovementioned issues and could lead, in the next decade, to a major technological revolution in the mobility sector [
11], by improving energy utilization efficiency [
12], traffic handling [
13], and road safety [
14].
Creating systems in which information and communication technologies can be easily exchanged, namely Intelligent Transportation Systems (ITS) [
15], can be particularly beneficial in urban areas where connected vehicles, featuring Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) technologies [
16], can have access to the Signal Phase and Timing (SPaT) of traffic lights. This information can be directly transmitted to vehicles through a Dedicated Short Range Communications (DSRC) technology [
17] or may become available by the traffic control center through cellular and Wi-Fi networks, namely Cellular Vehicle-to-Everything (C-V2X) [
18]. The C-V2X potentiality could be further boosted by a possible coupling with the new 5G mobile network [
19] or joint use of DSRC and C-V2X communications [
20]. Alternatively, several studies have demonstrated that SPaT information may be inferred via on-board cameras [
20] and via crowdsourcing [
21].
Traditional approaches focused more on signal control methods to enhance traffic flow at signalized intersections, such as signal timing optimization [
22], or actuated signals application in real-world traffic [
23] that could allow smoothing traffic oscillations and decreasing vehicle waiting times at intersections. However, with the recent advances in ITS technology that empower the vehicles to share information with the surrounding environment [
24], more recent research has been focused on developing ecodriving algorithms, i.e., using the information to plan an optimal path and velocity trajectory so as to improve the efficiency in energy utilization and traffic handling [
25].
Some earlier works, like [
26], demonstrated how upcoming traffic signal information can be used by a vehicle’s adaptive cruise control system to reduce idle time at traffic lights and fuel consumption, or developed algorithms for detecting and predicting the SPaT to enable a Green Light Optimal Speed Advisory (GLOSA) system: i.e., a speed corrector to avoid unnecessary halts at traffic lights [
27]. In [
28] the performance degradation of the GLOSA system due to queuing effects and actual tracking driver errors is taken into account, while in [
29] the uncertainties of SPaT information due to varying patterns of traffic lights are introduced. In the literature, several other applications of ecodriving optimization have been shown: in [
30] an algorithm that jointly adjusts vehicle speeds at intersections and signal timings is proposed while in [
31] the benefits of incorporating near-term technologies in a predictive management strategy are assessed.
As formalized in [
32], eco-driving can be regarded as an optimal control problem where the drive commands are chosen to minimize the energy consumption for a given trip, and, among the set of methods provided by the optimal control theory, some solution techniques that are commonly employed are Model Predictive Control (MPC) and Dynamic Programming (DP). MPC can be implemented as either an optimization problem considering the nonlinearities in the powertrain efficiency characteristics [
33] or a computationally less expensive linear optimization problem that only considers the vehicle kinematics [
34]. DP [
35], as proposed by [
29] to optimize the velocity profiles for achieving automated ecodriving, can provide an optimal result even for highly nonlinear problems, such as ecodriving. However, the heavy computation burden of DP has made its use largely limited to asserting an offline performance benchmark, and suitable simplifications are needed to reduce the dimensionality of the optimization problem in real-time applications. For instance, in [
36] an algorithm derived from DP that can be real-time implementable is proposed; authors in [
37] propose an interesting pruning algorithm aimed at reducing the optimization domain by considering only the portions of the traffic light’s green phases that allow driving in compliance with the city speed limits; after defining some points of interest, such as traffic lights, road curvatures, etc.; in [
38] a variable step size is introduced that drastically reduces the computation cost; authors in [
39] propose a reduction of the search grid by considering the maximum energy recuperation or the maximum battery discharge capacity.
Quite recently, in addition to traditional optimal control theory, Reinforcement Learning (RL) algorithms have also been studied for addressing the ecodriving problem [
40]. For instance, authors in [
41] propose a hierarchical RL algorithm that decides whether the controlled vehicle should stop or pass at a traffic light and then performs the corresponding longitudinal control accordingly, in [
42] ecodriving strategies are developed that are based on RL algorithms that are suitable for cases where little data on the traffic situation are available, and in [
43] a hybrid RL-based algorithm is proposed that considers both the longitudinal acceleration/deceleration and the lateral lane changing. Moreover, since the standardization and introduction of vehicular communication is still an ongoing process, and it may take a while to reach a wide penetration rate of CAVs, a lot of research has explored the safety of mixed traffic flow, i.e., CAVs mixed with human-driven vehicles, which, as shown by [
44] can be related to platoon size and penetration rate of CAVs.
From the powertrain control point of view, the increasing adoption of connected vehicles can allow for simultaneously optimizing powertrain control and velocity profile. Several studies have explored methods for optimizing the vehicle velocity profile for Battery Electric Vehicles (BEVs) as well as for Internal Combustion Engine Vehicles (ICEVs). In [
45], Dynamic Programming (DP) is used to optimize the velocity of a BEV, while in [
46], the fuel consumption reduction of a DP-based algorithm is assessed on a heavy-duty ICEV. However, Hybrid Electric Vehicles (HEVs) and plug-in Hybrid Electric Vehicles (pHEVs) can benefit the most from embedding them in an ITS, since the information from the surrounding environment can be used to optimize their control strategies [
47,
48]. Vehicle-to-Everything (V2X) communication along with cloud computing adoption [
49] may enable a change of paradigm of the energy management problem: from an instantaneous optimization to globally minimizing it over the entire driver route [
50]. For example, in [
51], a hierarchical ecodriving control using MPC under complex driving conditions is designed for connected and automated HEVs where an intelligent driving scenario classifier is devised to identify the driving scenarios.
In summary, many studies optimized vehicle speed by using MPC, RL methods, or strategies derived from DP. All these strategies are inherently suboptimal and only DP can provide the optimal solution, but the heavy computation burden of this strategy has made its use largely limited to asserting an offline performance benchmark. This paper aims to fill these gaps by proposing a preliminary study to assess the potential of a system that could be integrated with cloud computing and interfaced with a real-time implementable energy management strategy. With the recent advances in ITS technology that could empower vehicles to share information with the surrounding environment, it seems feasible that the vehicles can have realistic information about speed limits and Expected Time of Arrival (ETA). In this context, we propose a Variable Grid Dynamic Programming (VGDP) that modifies the variable state search grid on the basis of the V2X information allowing a drastic reduction in the DP computation burden by more than 95% if compared to the standard optimization performed with a fixed grid. These achievements make this algorithm more attractive for real-world applications, and this work can represent a preliminary study that lays the basis for a controller that could realistically consider the DP for online implementation.
Figure 1 schematically describes the proposed algorithm: by relying on a cloud computing architecture in which the vehicle communicates its route and destination to a vehicle simulator offsite, the information coming from the surrounding environment, e.g., traffic lights state, speed limits, distance to travel, etc., is used to define a variable state space grid that allows a computationally efficient optimization. The DP can thus define the optimal velocity profile, that the vehicle should follow to optimize the time/energy trade-off. The major contributions of the paper are the followings:
Exploiting information coming from the surrounding environment, e.g., traffic lights state, speed limits, distance to travel, etc., to generate a variable state search grid for the DP algorithm: the DP computation burden is reduced by more than 95% if compared to the standard optimization performed with a fixed grid;
Assessing the benefits that the introduction of V2V and V2I communication, integrated with cloud computing, can have in a real-world route in terms of energy and time savings. The Reference Scenario is a predefined Real Driving Emissions (RDE) compliant route [
52], while the simulation scenarios are generated by assuming two different levels of penetration of V2X technologies. The simulations show that introducing a smart infrastructure along with optimizing the vehicle speed in a real-world urban route can potentially reduce the required energy by 54% while shortening the travel time by 38%;
Laying the basis for a cloud-based controller that could realistically consider DP for online implementation: by communicating its route and destination to an offsite vehicle simulator, a connected vehicle could be advised with the optimal velocity profile to follow in order to optimize the energy/time trade-off.
The results of the proposed analysis must be considered as a benchmark since the simulations are carried out for a simplified urban traffic network with vehicles in almost free flow, i.e., without direct constraints related to the preceding or following vehicles, but it can be conceptually extended to the case of multiple vehicles equipped with the proposed algorithm. The rest of the paper is organized as follows. In
Section 2, the virtual test rig used for the simulations is introduced along with the description of the simulation scenarios. Then, the ecodriving optimization problem is formulated in
Section 3. In
Section 4, the results of the optimization algorithm are shown for the two different scenarios. In
Section 5, conclusions are summarized and further studies are provided.
5. Conclusions
Over the next decade, Connected and Autonomous Vehicles (CAV) technologies are expected to become more commonly available on new vehicles. Although their ultimate goal is to improve safety and convenience for customers, they can provide significant information about the planned driver route and the surrounding environment. The knowledge of this information can improve the energy efficiency of a vehicle while reducing, at the same time, travel time. In this framework, this work assessed the benefits that the introduction of Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication, integrated with cloud computing, can have in a real-world route in terms of energy and time savings. The Reference Scenario is a predefined Real Driving Emissions (RDE) compliant route, while the simulation scenarios were generated by assuming two different levels of penetration of V2X technologies. The associated energy minimization problem was formulated and solved by means of a global optimization algorithm, i.e., Variable Grid Dynamic Programming (VGDP): relying on information coming from V2X, e.g., traffic lights state, speed limits, distance to travel, etc., a variable state search grid was introduced, which allows reducing the DP computation burden by more than 95%, if compared to the standard optimization performed with a fixed grid. For the route evaluated in this paper, numerical results showed that, in an urban context, introducing a smart infrastructure along with optimizing the vehicle speed can potentially reduce the required energy by 54% while shortening the travel time by 38%. A sensitivity analysis was performed on the biobjective optimization cost function to find a set of Pareto optimal solutions, between energy and travel time minimization. The results of the proposed analysis must be considered as a benchmark since the simulations were carried out for a simplified urban traffic network with vehicles in almost free flow, i.e., without direct constraints related to the preceding or following vehicles, but can be conceptually extended to the case of multiple vehicles equipped with the proposed algorithm. This work represents the first step of a broader activity aimed at developing an integrated framework that can exploit remote cloud computing and V2X information to enhance the fuel economy of HEVs. The cloud-based controller can be used to generate an optimized target speed that can be followed in real-time thanks to a low-level controller, such as an MPC or an AI-based algorithm. It should be noted that a high discretization resolution was used for the independent variable, i.e., distance: increasing the interval chosen for the distance discretization can play a key role in making this technique realistically attractive for an online implementation (a preliminary investigation showed that a 10× increase in the distance step can lead to an 11× reduction in the computational time). Further work will be aimed at testing the entire methodology in a cloud computing interface in order to evaluate its potential to recalculate the optimal velocity profile in response to real-time road traffic information. Moreover, future work will also consider platoon potentialities to address the practicality and safety of more vehicles traveling together.