Energy Consumption Prediction of a Vehicle along a User-Specified Real-World Trip

Standard cycles provide an easy way to evaluate the energy consumption of vehicles, but it is the energy consumption that occurs on real-world trips that really matters to the driver and, to a larger extent, society. This study shows how digital maps and vehicle simulation tools can be used to estimate energy consumption on a real-world trip. The user (1) selects a trip in the mapping service ADAS-RP (Advanced Driver Assistance Systems Research Platform), (2) defines a vehicle model in the vehicle powertrain simulation tool Autonomie, and (3) runs and analyzes the simulation in that same tool. For each section of the trip, ADAS-RP provides various information that can include speed limits, historic data on traffic pattern speeds, the slopes of the routes, and the positions of stop signs and traffic lights. The first stage of processing this information is to schedule the stops and to create an intermediate speed target that takes those stops into account. The final driver demand speed includes transitions – accelerations and decelerations – between sections with different intermediate speed targets, or around stops. The ADAS-RP/Autonomie process is then used to compute the energy consumption of a hybrid electric vehicle and a conventional vehicle on 10 trips defined across the United States and Germany. The hybrid vehicle is more fuel efficient, especially on congested routes and routes with downhill slopes, the effect of which is analyzed in further detail. Document type: Articl

Energy Consumption Prediction of a Vehicle along a User-Specified Real-World Trip

Standard cycles provide an easy way to evaluate the energy consumption of vehicles, but it is the energy consumption that occurs on real-world trips that really matters to the driver and, to a larger extent, society. This study shows how digital maps and vehicle simulation tools can be used to estimate energy consumption on a real-world trip. The user (1) selects a trip in the mapping service ADAS-RP (Advanced Driver Assistance Systems Research Platform), (2) defines a vehicle model in the vehicle powertrain simulation tool Autonomie, and (3) runs and analyzes the simulation in that same tool. For each section of the trip, ADAS-RP provides various information that can include speed limits, historic data on traffic pattern speeds, the slopes of the routes, and the positions of stop signs and traffic lights. The first stage of processing this information is to schedule the stops and to create an intermediate speed target that takes those stops into account. The final driver demand speed includes transitions – accelerations and decelerations – between sections with different intermediate speed targets, or around stops. The ADAS-RP/Autonomie process is then used to compute the energy consumption of a hybrid electric vehicle and a conventional vehicle on 10 trips defined across the United States and Germany. The hybrid vehicle is more fuel efficient, especially on congested routes and routes with downhill slopes, the effect of which is analyzed in further detail

Energy Consumption Prediction of a Vehicle along a User-Specified Real-World Trip

Standard cycles provide an easy way to evaluate the energy consumption of vehicles, but it is the energy consumption that occurs on real-world trips that really matters to the driver and, to a larger extent, society. This study shows how digital maps and vehicle simulation tools can be used to estimate energy consumption on a real-world trip. The user (1) selects a trip in the mapping service ADAS-RP (Advanced Driver Assistance Systems Research Platform), (2) defines a vehicle model in the vehicle powertrain simulation tool Autonomie, and (3) runs and analyzes the simulation in that same tool. For each section of the trip, ADAS-RP provides various information that can include speed limits, historic data on traffic pattern speeds, the slopes of the routes, and the positions of stop signs and traffic lights. The first stage of processing this information is to schedule the stops and to create an intermediate speed target that takes those stops into account. The final driver demand speed includes transitions – accelerations and decelerations – between sections with different intermediate speed targets, or around stops. The ADAS-RP/Autonomie process is then used to compute the energy consumption of a hybrid electric vehicle and a conventional vehicle on 10 trips defined across the United States and Germany. The hybrid vehicle is more fuel efficient, especially on congested routes and routes with downhill slopes, the effect of which is analyzed in further detail. Document type: Articl

IEEE VTS Motor Vehicles Challenge 2018 – Energy Management of a Range Extender Electric Vehicle

International audienceIn October 2016, a first international challenge devoted to the energy management of a fuel cell/battery vehicle was launched during the 2016 IEEE Vehicle Power and Propulsion Conference (VPPC), in Hangzhou, China. Following the success of this first initiative, this paper describes the technical framework of a second challenge focused on the energy management of a Range Extender Electric Vehicle, the Chevrolet Volt. Both Academic and Professional teams are welcomed to participate in this challenge. The aim is to develop a robust Energy Management Strategy to minimize the fuel consumption and the battery charging cost. In this way, a validated vehicle model and control will be provided to the challenge participants by the use of the Autonomie Matlab Simulink & Stateflow based software, developed by the Argonne National Laboratory. The top scoring participants will be distinguished and invited to present their results in a special session at the 2018 IEEE VPPC

Corrosion of magnesium and aluminum in palm biodiesel: A comparative evaluation

10.1016/j.energy.2013.04.067Energy57478-483ENEY

Modeling Fuel Consumption of Hybrid Electric Buses: Model Development and Comparison with Conventional Buses

Electric hybridization technologies appear to be one of the most promising approaches to improving the energy efficiency of buses; however, this improvement has not been systematically quantified. A fuel consumption model is essential for capturing fuel consumption behavior accurately and quantifying the fuel benefits of hybrid buses. Consequently, the objective of this study was to develop a fuel consumption model for hybrid buses on the basis of the framework of the Virginia Tech Transportation Institute’s comprehensive power-based fuel consumption model and then to quantify the benefits associated with hybridization technologies relative to conventional diesel bus operations. The model estimates were demonstrated to be consistent with in-field measurements, and the optimum fuel economy cruise speed was demonstrated to be approximately 50 km/h. The results demonstrate that hybrid buses consumed less fuel overall, while heavier buses and higher passenger loads may have reduced the fuel savings. The results also reveal that more fuel savings could be achieved for cruise and stop-and-go activity compared with idling behavior and that stop-and-go operation generated the highest level of fuel efficiency benefits. The conclusions of this paper can support bus planning applications to achieve fleet fuel savings