An Eco-Route Planner for Heavy Duty Vehicles Maria Pia Fanti, Fellow, IEEE, Agostino Marcello Mangini, Member, IEEE, Alfredo Favenza, and Gianvito Difilippo Abstract—Driving style, traffic and weather conditions have a significant impact on vehicle fuel consumption and in particular, the road freight traffic significantly contributes to the CO 2 increase in atmosphere. This paper proposes an Eco-Route Planner devoted to determine and communicate to the drivers of Heavy-Duty Vehicles (HDVs) the eco-route that guarantees the minimum fuel consumption by respecting the travel time established by the freight companies. The proposed eco-route is the optimal route from origin to destination and includes the optimized speed and gear profiles. To this aim, the Cloud Computing System architecture is composed of two main components: the Data Management System that collects, fuses and integrates the raw external sources data and the Cloud Optimizer that builds the route network, selects the eco-route and determines the optimal speed and gear profiles. Finally, a real case study is discussed by showing the benefit of the proposed Eco-Route planner.   I. Introduction CO 2 H EAVY-DUTY Vehicles (HDVs) and freight traffic are one of the recognized causes of emissions and energy consumption. The automotive industry has put significant effort in finding new powertrain with less energy consumption. A proposed solution is for instance the hybrid electric vehicles (HEVs) that inspired some studies focused on energy management [1]–[2]. A second approach for reducing energy consumption is studying the driving velocity profile and route optimization. In this context, Ahn and Rakha [1] investigate the impacts of route choice on vehicle energy consumption and emission rates for different vehicle types. They demonstrate that the faster highway route choice is not always the best from an environmental and energy consumption perspective. For this reason, in the recent years some studies are developing a new environmentally navigation concept called eco-routing. The eco-routing is the identification of the most energy-efficient route for a vehicle to travel between two points and is offered as a way in which drivers can reduce fuel consumption [3]. In the simulation results presented in [2], the authors establish that eco-routing systems can reduce the fuel consumption in most cases and quantifies the fuel savings in the range between 3.3% and 9.3% with respect to typical travel fastest routes on two large metropolitan networks. Recently, the eco-routing strategies can take advantage by information technology solutions, mainly in the area of advanced traveler information systems. Now, many navigation systems can transmit to the drivers a route selection within a roadway network (shortest and fastest routes, toll routes, etc.) that considers traffic data, accident occurrence, congestion [4]. In this research area, [5] and [6] show by simulation studies that there is a big potential to save energy using eco-routing concepts and traffic data. However, these studies do not present approaches to find optimal routes but only test some routes measuring fuel consumption. Moreover, the authors in [7] determine for the pair origin-destination a Pareto frontier containing optimum solution paths. However, the optimum solution paths are derived by a bi-objective problem minimizing distance and time. Moreover, [8] and mainly [9] propose an energy optimal real time navigation system for electric vehicle focused on energy optimal route calculation. In particular, [9] uses Internet and smart devices to take data about road slope, traffic conditions and speed limits. In addition, the longitudinal model of the electric vehicle is determined to optimize speeds by a look-ahead concept. However, the authors do not consider weather conditions and the route network used to determine the optimal eco-route is assumed given. Furthermore, the traffic and geographical information of the road networks require large storage units, and the search algorithms for global optimization may require high computation power, which is not available on current vehicle computing units [4], [6]. Street slope is an important factor that should be considered in an eco-routing strategy. The authors of [10] use a 3D routing model to determine eco-friendly routes for transportation and distribution of goods in urban areas: the street gradient is factored in the estimation of the fuel consumption, which is performed using an Emission Estimation Model based on COPERT III, a computer software able to determine routes between two points and emissions [11]. In addition, [12] solves the eco-routing problem for HEVs in urban areas, at the same time by optimizing the power train control. Driving conditions are considered in [12] and [10], but all computations are performed in a static scenario, without using real-time data, and with fixed origin and destination. Moreover, a personalized energy Manuscript received July 21, 2020; accepted August 13, 2020. Recommended by Associate Editor MengChu Zhou. (Corresponding author: Maria Pia Fanti.) Citation: M. P. Fanti, A. M. Mangini, A. Favenza, and G. Difilippo, “An eco-route planner for heavy duty vehicles,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 1, pp. 37–51, Jan. 2021. M. P. Fanti and A. M. Mangini are with the Department of Electric and Information Engineering, University Polytechnic of Bari, Bari, Italy (e-mail: {mariapia.fanti, agostinomarcello.magini}@poliba.it). A. Favenza is with Links Foundation, Turin, Italy (e-mail: alfredo.favenza@ linksfoundation.com). Gianvito Difilippo is with the company AutoLogS, Bari, Italy (e-mail: gianvito.difilippo@autologs.eu). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JAS.2020.1003456 IEEE/CAA JOURNAL OF AUTOMATICA SINICA, VOL. 8, NO. 1, JANUARY 2021 37