SmartDrive: Traction Energy Optimization and Applications in Rail Systems

Tian, Zhongbei; Zhao, Ning; Hillmansen, Stuart; Roberts, Clive; Dowens, Trevor; Kerr, Colin

doi:10.1109/tits.2019.2897279

Cited by 86 publications

(34 citation statements)

References 30 publications

(41 reference statements)

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“…In order to calculate the accessible RBE, the train motion simulated based on the vehicle characteristic and route data. [26][27][28][29] Figure 3 depicts the simulation process of the RBE calculation. The composition of forces imposed on the train determines the movement of it, regarding Newton's mechanical laws.…”

Section: Modeling Train Motionmentioning

confidence: 99%

Optimal operation of a smart railway station based on a multi‐energy hub structure considering environmental perspective and regenerative braking utilization

Akbari

Fazel

Jadid

2021

Energy Science & Engineering

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Section: Modeling Train Motionmentioning

confidence: 99%

Optimal operation of a smart railway station based on a multi‐energy hub structure considering environmental perspective and regenerative braking utilization

Akbari

Fazel

Jadid

2021

Energy Science & Engineering

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“…Reducing energy consumption in railways directly leads to reduction in CO 2 emissions, and previous research has looked at driver interventions, which can reduce the total energy consumption by up to 20% [9,10]. Operational energy consumption (the energy consumption of a vehicle in operation rather than its embodied energy) of a hydrogen-hybrid powered train has been studied in [11], and Ref.…”

Section: Introductionmentioning

confidence: 99%

An investigation into intermittent electrification strategies and an analysis of resulting CO2 emissions using a high-fidelity train model

et al. 2021

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A near-term strategy to reduce emissions from rail vehicles, as a path to full electrification for maximal decarbonisation, is to partially electrify a route, with the remainder of the route requiring an additional self-powered traction option. These rail vehicles are usually powered by a diesel engine when not operating on electrified track and are referred to as bi-mode vehicles. This paper analyses the benefits of discontinuous electrification compared to continuous electrification using the CO2 estimates from a validated high-fidelity bi-mode (diesel-electric) rail vehicle model. This analysis shows that 50% discontinuous electrification provides a maximum of 54% reduction in operational CO2 emissions when compared to the same length of continuously electrified track. The highest emissions savings occurred when leaving train stations where vehicles must accelerate quickly to line speed. These results were used to develop a linear regression model for fast estimation of CO2 emissions from diesel running and electrification benefits. This model was able to estimate the CO2 emissions from a route to within 10% of that given by the high-fidelity model. Finally, additional considerations such as cost and the embodied CO2 in electrification infrastructure were analysed to provide a comparison between continuous and discontinuous electrification. Discontinuous electrification can cost up to 56% less per reduction in lifetime emissions than continuous electrification and can save up to 2.3 times more lifetime CO2 per distance electrified.

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“…Pan et al [12] discussed the influence of many factors on train energy consumption, but relevant factors were not used to provide a more economy driving strategy. Tian et al [13] optimized energy consumption through optimal parameter design of cruising plus coasting strategy, and validated the method through driver practice training system (DPTS). Xiao et al [14] used multi-phase dynamic programming (DP) algorithm to optimize the eco-driving running strategy with the consideration of signal constraints, and the method was validated through numerical simulation.…”

Section: Introductionmentioning

confidence: 99%

Integrated Optimal Design of Speed Profile and Fuzzy PID Controller for Train With Multifactor Consideration

Zhu

Liu³

et al. 2020

IEEE Access

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Speed profile design and tracking control are two important issues to ensure the performance of the automatic train operation system. Aiming to realize the maximum optimization probability of train running, this paper constructs an integrated optimal method through speed profile design and fuzzy PID controller design. In the recommended speed profile design, three typical running strategies are included in the proposed comprehensive scheme to extend solution space, and the constructed train performance simulation is combined with NSGA-II algorithm to solve such multiobjective problem. As for recommended speed profile tracking, in order to improve the tracking performance of the train, which is a nonlinear system, a fuzzy PID controller is designed to adaptively adjust PID gains. Note that multiple indicators of punctually, energy consumption, stopping accuracy and comfort are all considered in the integrated design. Taking Beijing Subway Line 8 as the numerical example, the results show that the design of speed profile and fuzzy PID controller are both effective, and energy consumption saves about 10.40% with other better indicators compared with the original data.

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SmartDrive: Traction Energy Optimization and Applications in Rail Systems

Cited by 86 publications

References 30 publications

Optimal operation of a smart railway station based on a multi‐energy hub structure considering environmental perspective and regenerative braking utilization

Optimal operation of a smart railway station based on a multi‐energy hub structure considering environmental perspective and regenerative braking utilization

An investigation into intermittent electrification strategies and an analysis of resulting CO2 emissions using a high-fidelity train model

Integrated Optimal Design of Speed Profile and Fuzzy PID Controller for Train With Multifactor Consideration

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