Comparison of locomotive energy storage systems for heavy-haul operation

Spiryagin, Maksym; Wu, Qing; Wolfs, Peter; Sun, Yan Quan; Cole, Colin

doi:10.1080/23248378.2017.1325719

Cited by 20 publications

(22 citation statements)

References 26 publications

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“…For example, wheel-rail friction that has implications for train energy consumptions cannot be accurately simulated in LTD. However, the single DOF simplification in LTD is widely acknowledged and well accepted for train energy simulations in academia and industry [11][12][13][14][15][16][17].…”

Section: Ltd Basicsmentioning

confidence: 99%

“…The first type considers the train as a whole, in which inter-vehicle movements are neglected [2][3][4][5][6][7][8][9][10]. The second type uses models that are commonly used for longitudinal train dynamics (LTD) [11,12]; specifically, each vehicle of the train is modelled as a rigid body with a single degree of freedom (DOF) [13][14][15][16][17][18][19]. Both types of models use the same or similar procedures to calculate train energy consumptions.…”

Section: Introductionmentioning

confidence: 99%

“…Mines are usually located in mountainous areas with relatively higher altitudes, whilst ports are all located seaside with almost zero altitude. Considering that most heavy haul trains return to mines empty, theoretically, there is a large amount of energy that can be recovered during the loaded trip via technologies such as dynamic brake (DB) and energy storage systems (ESSs) [3,7,9,11,12]. Studies that focus on heavy haul trains therefore have the potential to be of great significance.…”

Section: Introductionmentioning

confidence: 99%

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Train energy simulation with locomotive adhesion model

Spiryagin

Cole

2020

Rail. Eng. Science

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Railway train energy simulation is an important and popular research topic. Locomotive traction force simulations are a fundamental part of such research. Conventional energy calculation models are not able to consider locomotive wheel-rail adhesions, traction adhesion control, and locomotive dynamics. This paper has developed two models to fill this research gap. The first model uses a 2D locomotive model with 27 degrees of freedom and a simplified wheel-rail contact model. The second model uses a 3D locomotive model with 54 degrees of freedom and a fully detailed wheel-rail contact model. Both models were integrated into a longitudinal train dynamics model with the consideration of locomotive adhesion control. Energy consumption simulations using a conventional model (1D model) and the two new models (2D and 3D models) were conducted and compared. The results show that, due to the consideration of wheel-rail adhesion model and traction control in the 3D model, it reports less energy consumption than the 1D model. The maximum difference in energy consumption rate between the 3D model and the 1D model was 12.5%. Due to the consideration of multiple wheel-rail contact points in the 3D model, it reports higher energy consumption than the 2D model. An 8.6% maximum difference in energy consumption rate between the 3D model and the 1D model was reported during curve negotiation.

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Section: Ltd Basicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Train energy simulation with locomotive adhesion model

Spiryagin

Cole

2020

Rail. Eng. Science

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“…To derive the energy-conservation-oriented operation profiles and calculate consumed tractive energy, the BFO-enabled procedures can be described in Figure 3. It has to be noticed that a simplified tractive energy calculation model is adopted in the presented solution compared to those consider detailed locomotive traction and train dynamics as [46,47]. Utilization of a detailed modeling solution will enhance the resolution of evaluation by integrating more realistic behaviors of the trains.…”

Section: Calculation Of Energy-efficient Driving Control Solutionmentioning

confidence: 99%

Boundary Identification for Traction Energy Conservation Capability of Urban Rail Timetables: A Case Study of the Beijing Batong Line

Liu

Cai

et al. 2020

Energies

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Energy conservation is attracting more attention to achieve a reduced lifecycle system cost level while enabling environmentally friendly characteristics. Conventional research mainly concentrates on energy-saving speed profiles, where the energy level evaluation of the timetable is usually considered separately. This paper integrates the train driving control optimization and the timetable characteristics by analyzing the achievable tractive energy conservation performance and the corresponding boundaries. A calculation method for energy efficient driving control solution is proposed based on the Bacterial Foraging Optimization (BFO) strategy, which is utilized to carry out batch processing with timetable. A boundary identification solution is proposed to detect the range of energy conservation capability by considering the relationships with average interstation speed and the passenger volume condition. A case study is presented using practical data of Beijing Metro Batong Line and two timetable schemes. The results illustrate that the proposed optimized energy efficient driving control approach is capable of saving tractive energy in comparison with the conventional traction calculation-based train operation solution. With the proposed boundary identification method, the capability space of the energy conservation profiles with respect to the energy reduction and energy saving rate is revealed. Moreover, analyses and discussions on effects from different passenger load conditions are given to both the weekday and weekend timetables. Results of this paper may assist the decision making of rail operators and engineers by enhancing the cost effectiveness and energy efficiency.

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“…Energy storage systems (ESSs) 23 such as battery storage systems and flywheel storage systems have seen ever increasing applications on locomotives, i.e. hybrid locomotives.…”

Section: Introductionmentioning

confidence: 99%

Traction modelling in train dynamics

Spiryagin

Wolfs

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part F:

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This paper presents five locomotive traction models for the purpose of train dynamics simulations, such as longitudinal train dynamics simulations. Model 1 is a look-up table model with a constant force limit to represent the adhesion limit without modelling the wheel-rail contact. Model 2 is improved from Model 1 by empirically simulating locomotive sanding systems, variable track conditions and traction force reduction due to curving. Model 3 and Model 4 have included modelling of the wheel-rail contact and traction control. Model 3 uses a two-dimensional locomotive model while Model 4 uses a three-dimensional locomotive. Model 5 is based on Model 2 and developed to simulate hybrid locomotives. Demonstrative simulations are presented for the case of longitudinal train dynamics. The results show that the consideration of locomotive sanding systems, variable track conditions and traction force reduction have evident implications on the simulated traction forces. There can be up to 30% difference in the simulated traction forces. Simulated traction forces by models that consider the wheel-rail contact are about 10-15% lower than those simulated by models without consideration of the wheel-rail contact. This is mainly due to the variable friction in the wheel-rail contact and conservative traction control schemes.

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Comparison of locomotive energy storage systems for heavy-haul operation

Cited by 20 publications

References 26 publications

Train energy simulation with locomotive adhesion model

Train energy simulation with locomotive adhesion model

Boundary Identification for Traction Energy Conservation Capability of Urban Rail Timetables: A Case Study of the Beijing Batong Line

Traction modelling in train dynamics

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