Center for Sustainable Mobility

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  • Comparative analysis of alternative powertrain technologies in freight trains: A numerical examination towards sustainable rail transport
    Aredah, Ahmed; Du, Jianhe; Hegazi, Mohamed; List, George; Rakha, Hesham A. (Elsevier, 2024-02-15)
    This study assesses the energy efficiency and environmental implications of six powertrain technologies in the U.S. freight rail network: diesel, biodiesel, diesel-hybrid, biodiesel-hybrid, hydrogen fuel cell, and electric. Utilizing a simulation model, energy consumption at the tank across different demand scenarios and geographical regions is conducted. The study revealed electric powertrains as the standout, slashing energy consumption at the tank by 56% compared to traditional diesel, with the potential for zero CO2 emissions when powered by green energy sources. Biodiesel and biodiesel-hybrid also outperformed conventional diesel, cutting CO2 tank emissions by 6% and 21%, respectively. Diesel-hybrid registered a 16% reduction in both tank energy and diesel consumption, while hydrogen fuel cells demonstrated a 15% energy consumption drop at the tank and zero emissions. Implementing these advanced technologies requires considerable infrastructure investment and adaptation, which is beyond the scope of our analysis. While centered on the U.S. rail network, our findings offer valuable insights for global freight rail systems, underpinning the push for sustainable powertrain transitions.
  • NeTrainSim: A Network Freight Train Simulator for Estimating Energy/Fuel Consumption
    Aredah, Ahmed; Fadhloun, Karim; Rakha, Hesham A.; List, George (2023-01-10)
    Although train simulation research is vast, most available network simulators do not track the instantaneous movements and interactions of multiple trains for the computation of energy/fuel consumption. In this paper, we introduce the NeTrainSim simulator for heavy long-haul freight trains on a network of multiple intersecting tracks. Trains are modeled as a series of moving mass points (each car/locomotive is modeled as a point mass) while ensuring safe following distances between them. The simulator considers the motion of the train as a whole and neglects the relative movements between the train cars/locomotives. Furthermore, the powers of the different locomotives are transferred to the first locomotive as such a simplification result in a reduced simulation time without impacting the accuracy of energy consumption estimates. While the different tractive forces are combined, the resistive forces are calculated at their corresponding locations. The output files of the simulator contain pertaining information to the train trajectories and the instantaneous energy consumption levels. A summary file is also provided with the total energy consumed for the full trip and the entire network of trains. Two case studies are conducted to demonstrate the performance of the simulator. The first case study validates the model by comparing the output of NeTrainSim to empirical trajectory data using a basic single-train network. The results confirm that the simulated trajectory is precise enough to estimate the electric energy consumption of the train. The second case study demonstrates the train-following model considering six trains following each other. The results showcase the model’s ability in relation to maintaining safe-following distances between successive trains. Finally, the NeTrainSim is demonstrated to be scalable with computational times of O(n) for less than 50 trains (n) and O(n2) for higher number of trains.