Quantification of Energy Saving Potential for A Passenger Train Based on Inter-Run Variability in Speed Trajectories

Abstract

Passenger train energy consumption is dependent on speed trajectories. The variability of passenger train energy consumption owing to the variability in speed trajectories can help identify ways to reduce train energy use via improved operations. Empirical fuel use data from a portable measurement emission measurement system (PEMS) and empirical speed trajectories measured using a global positioning system (GPS) receiver were used to verify and quantify real-world energy consumption variability and the variability in empirical speed trajectories, respectively. To identify potential realistic speed trajectories that can lead to energy saving (i.e., eco-driving), a Markov chain based speed trajectory simulator was used to simulate inter-run variability in speed trajectories. An energy index model (EIM) was used to compare energy consumption among different speed trajectories. The results show inter-run variability in fuel use associated with inter-run variability in the empirical speed trajectories. There is also inter-segment variability in fuel use related to the segment length and grade. The Markov chain based speed trajectory simulator can simulate realistic inter-run variability in speed trajectories based on calibration using empirical speed trajectories. The number of empirical speed trajectories used for simulator calibration affects the range of simulated inter-run variability. The EIM provides an accurate estimation of the empirical fuel use. Eco-driving, such as reducing the peak speed, can reduce energy consumption without compromising travel time. The methodology shown in this study is not system-specific and can be applied to other passenger train systems.

Characterizing Fuel Use and Emission Hotspots for a Diesel-Operated Passenger Rail Service

“…This work demonstrates that PEMS-based measurements are useful for quantifying spatial variability in FUERs and associated factors for a given route and operation. Train activity and infrastructure variables can be easily inferred from low-cost GPS devices. , The methods and example case study demonstrate that spatial variability in FUERs contributes to high emission rates in populated areas. Intuitively, hotspots are likely to be located on segments with positive grades or positive acceleration.…”

“…Train activity and infrastructure variables can be easily inferred from low-cost GPS devices. 34,63 The methods and example case study demonstrate that spatial variability in FUERs contributes to high emission rates in populated areas. Intuitively, hotspots are likely to be located on segments with positive grades or positive acceleration.…”

“…Several studies demonstrated methods to modify a 1 Hz train activity and were focused on reductions in energy use for a given trip. − Frey et al ,,, and Vojtisek-Lom et al measured real-world 1 Hz FUERs for passenger rail service . To quantify spatial variability in FUERs, Gould and Niemeier apportioned annual aggregated fuel use data to local track segments .…”