Fuel Estimation Model for ECO-Driving and ECO-Routing Imed Ben Dhaou College of Engineering Al Jouf University P.O.Box 2014, Sakakah Kingdom of Saudi Arabia Email: imed.bendhaou@ju.edu.sa Abstract— This paper elaborates a macroscopic, non-iterative algorithm to estimate the fuel consumption of vehicles. The algorithm uses the Willan’s internal combustion engine model and needs no instantaneous values of speed and acceleration. The efficiency of the proposed algorithm has been compared with measurement results for the following three cycles: mo- tor vehicle expert group (MVEG-95), European driving cycle (ECE), and extra-urban driving cycle (EUDC). For eco-routing, experiments show that there are tradeoffs between fuel savings and travel time. Results reported in this paper show that up to 33% of fuel savings have been achieved at the expense of 3% increase in trip-time. Finally, for manual gearing, the paper reports that proper gear shifting strategies can have substantial fuel savings. The reported experiments show that shifting from the 3rd to the 4th gear can save fuel consumption by 19%. These savings can reach up to 25% when shifting from the 4th to the 5th gear. I. I NTRODUCTION Carbon dioxide is believed to be the largest contributor to the global warming phenomena. Man-made carbon dioxide emissions are due to the usage of fossil fuels in manu- facturing and construction, electricity generation, and road transportation [1]. The latter accounts for 16% of the total emitted CO2[2]. In Europe, road transportation accounts for 20% of the total air pollution. There is strong evidence that global warming has caused various climate changes such as flooding, increasing sea- levels, degrading eco-system, typhoons and tornados, and climate refugee. Hurricane Katarina, for instance, was one of the five deadliest natural disasters the US has ever experienced [3]. Reducing road CO2 can be addressed in an integrated ap- proach (vehicle technology, alternative fuels, driver behavior, infrastructure measure, and CO2 taxation). The European Union has set a maximum limit of 120g/km of CO2 for new cars. This limit can be translated into an average fuel consumption of 4.5 L/100km for diesel cars and 5 L/100km for petrol ones [4]. In recent years, there has been a great awareness among drivers and traffic policy makers to use on-board navigation equipments[5] [6]. Route-guided driving can save fuel, avoid road congestion and reduce travel time. Latest surveys show that the navigation equipments can lower the consumed fuel by 12% [7]. According to Associated General Contractors (AGC) of America, traffic congestion is responsible for an extra 40 billion gallons of fuel in USA market [9]. Consequently, solving congestion through effective road-design and traffic management tools can greatly save fuel and decrease traffic CO2 [8]. According to Japan Manufacturers Associations (JAMA) 28.3 millions tones of CO2 can be reduced by improving road-infrastructure and traffic management tools [10] . Contemporary studies show that the fuel-consumption depends on the driving habits. Several recommendations for eco-driving have been published. Some of the eco-driving tips are (1) drive sensibly, (2) run at a fuel saving speed , (3) do not carry unnecessary weight, (4) shift to the highest gear as soon as possible, (5) avoid acceleration and deceleration, (6) check the wheel pressure (7) prevent traffic congestion, (8) lower the number of stops, (9) keep the engine warm, and (10) consolidate trips [11]. High-level fuel estimation algorithms are vitals for eco- driving and eco-routing. Accurate fuel estimation algorithms need detailed knowledge of, among others, road frictions, the grade, tire pressure, air-drag coefficients, driver behavior, instantaneous accelerations and speeds, and idle time. Those parameters are not a priori known. To address this problem, a fair macroscopic fuel estimation algorithm should be developed that can select the fuel-efficient route and optimize other driving parameters such as the speed and gear-shifting strategies. This paper elaborates a vehicle independent macroscopic fuel estimation algorithm. Section II defines the problem, reports our fuel estimation algorithm and compares it with existing algorithms. Section III presents the impact of gear- shifting strategies on the fuel consumption, compares the efficiency of the proposed algorithm with the measured fuel consumption, and details the application of the algorithm for eco-routing. Finally, Section IV concludes the paper and gives further directions for this research work. II. FUEL ESTIMATION ALGORITHM A. Models to estimate fuel consumption The equations and models to derive the macroscopic fuel estimation algorithm are detailed in [12] and [13]. The forces exerted on an accelerated vehicle assumed by the model are shown in Fig.1. 2011 IEEE Intelligent Vehicles Symposium (IV) Baden-Baden, Germany, June 5-9, 2011 978-1-4577-0891-6/11/$26.00 ©2011 IEEE 37