SFFEV 2016 Abstracts

Short Papers

Paper Nr:	1
Title:	MAMUTE – Monitoring AutoMotive Unit Transit Emissions - Development and Application for Flex-fuel and Diesel Vehicles
Authors:	Demostenis Ramos Cassiano, Julie Anne Holanda Azevedo, Helry Luvilany Fontenele Dias, Rinaldo dos Santos Araújo, Francisco Sales Ávila Cavalcante, Bruno Vieira Bertoncini, Nara Angelica Policarpo and Mona Lisa Moura de Oliveira
Abstract:	Vehicle emissions are substantially contributing for air pollution in urban areas. It is estimated that around 23% of global carbon dioxide (CO2) emissions just comes from the transportation sector. Particularly, carbon monoxide and hydrocarbons pollutants from light vehicles emissions (i.e. Otto cycle) are the most significant. However, heavy duty vehicles such as Diesel cycle are responsible for most emissions of nitrogen oxides and particulate matter. Considering the negative impacts of atmospheric pollution, several efforts have been made by researchers in the development and determination for automotive vehicles. The use of emission factors as input data for vehicle emissions modeling are an useful tool to support policy development, evaluation and optimization of urban mobility. Thus, this paper aims to explain an alternative method for emissions monitoring developed by authors for an on-board system that can be used in light or heavy duty vehicles (i.e. flex-fuel and diesel engine), named MAMUTE – Monitoring AutoMotive Unit Transit Emissions. This paper also describes the application of the developed equipment and emission factors obtained for the main pollutants from combustion for both Otto and Diesel engines, as well as the analysis of effect of operating modes vehicle. In general, emission factors for Diesel vehicle showed higher values than flex-fuel vehicle. It was observed that the acceleration mode had a more significant influence than others operating modes for the tested vehicles (i.e. flex-fuel and diesel), generating significant emission rates. Also, it was obtained emission factors similar to those described in the literature.

Paper Nr:	2
Title:	Experimental Analysis and Modeling of NOx Emissions in Compression Ignition Engines Fueled with Blends of Diesel and Palm Oil Biodiesel
Authors:	Adriana Patricia Villegas Quiceno, Ramón Fernando Colmenares Quintero, Simona Silvia Merola, Adrian Irimescu and Gerardo Valentino
Abstract:	In this work, theoretical and experimental studies about the effects of the blends of diesel and palm oil biodiesel on NOx formation in compression-ignition engines were developed. Experiments were conducted using commercial diesel, palm oil biodiesel and blends at 5% (B5), 20% (B20) and 50% (B50) as fuels. A phenomenological semi-empirical model that uses the information obtained from thermodynamic diagnostics was used for determining the theoretical NOx formation. The model shows the high sensitivity of NOx formation to the temperature and the operating conditions. Effects associated to the operating conditions of the engine were evaluated and the results indicate that high engine loads and low speeds lead to the NOx formation. However, it was not possible to determine with precision, the effect of the type of fuel, because of the high sensitivity of the NOx formation with respect to the operating conditions of the engine.
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Paper Nr:	4
Title:	Real-Time Schedule Optimization in Shared Electric Vehicle Fleets
Authors:	Falko Koetter and Julien Ostermann
Abstract:	Use of electric vehicles in corporate carsharing has become a promising option. However, to make the use of electric vehicles economically feasible, a high degree of utilization is necessary. In the Shared E-Fleet project, solutions for shared car fleets are being researched, increasing utilization by sharing cars among different companies. In this work, we present a process and algorithms for real-time vehicle schedule optimization, aiming to minimize manual scheduling work, to optimize the schedule towards a goal function (e.g. minimizing emissions) and to compensate disruptions in real-time. We evaluate the approach using synthetic data and model trials, showing that schedule optimization increases utilization as well as quality-of-service.
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