A method of calculating running resistance using monitoring devices for energy simulators

Manabe, Shinichi; Ogawa, Tomoko; Imamura, Yoichi; Minobe, Shingo; Kawamura, Jyunya; Kageyama, Masahisa

doi:10.1109/esars.2015.7101432

Cited by 4 publications

(3 citation statements)

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“…In [48], the maximum tractive effort and running resistance parameters are estimated from pulling and coasting tests, respectively. Regarding operational data, running resistance estimation under various circumstances is explored in [57], including tunnels and different weather conditions monitoring data. The results obtained from data are found to be consistent with coasting tests in [60].…”

Section: A Offline Train Motion Model Calibrationmentioning

confidence: 99%

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A Literature Review on Train Motion Model Calibration

Cunillera

Bešinović

Lentink

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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The dynamics of a moving train are usually described by means of a motion model based on Newton's second law. This model uses as input track geometry data and train characteristics like mass, the parameters that model the running resistance, the maximum tractive effort and power, and the brake rates to be applied. It can reproduce and predict train dynamics accurately if the mentioned train characteristics are carefully calibrated. The model constitutes the core element of a broad variety of railway applications, from timetabling tools to Driver Advisory Systems and Automatic Train Operation. Among the existing train motion model calibration techniques, those that use operational data are of particular interest, as they benefit from on-board recorded data, capturing the train dynamics during operation. In this literature review article we provide an overview of the train motion model calibration techniques that have been published in the scientific literature between January 2000 and December 2021 and either use operational data or can be minimally adapted to use it. To this end, we present a critical overview of the existing train motion model calibration approaches, distinguishing online calibration that analyzes data on-the-go and offline calibration that analyzes historical data batchwise. We propose a research agenda and highlight some potential goals to be tackled in the near future: from devising accurate online calibrators for eco-driving applications to quantitizing the physical sources of parameter variation. Last, we discuss practical recommendations for practitioners and scholars inferred from the current state of the art.

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Section: A Offline Train Motion Model Calibrationmentioning

confidence: 99%

“…Moreover, weather may also influence train motion model parameter variations. Public weather data sources could be used for analyzing seasonal and weather-induced variations [57]. The impact of wind speed and direction could also be assessed, as it may also influence the train dynamics.…”

Section: Research Agenda and Recommendationsmentioning

confidence: 99%

A Literature Review on Train Motion Model Calibration

Cunillera

Bešinović

Lentink

et al. 2023

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…This research should emphasize on the impact of tunnels in the running resistance parameters, as may boost the accuracy of energy consumption calculations and ecodriving-based applications [12], [37]. The impact of weather conditions, namely wind strength and direction, precipitations and temperature could also be gauged thanks to the availability of public weather data sources [37]. Moreover, the wind-dependent running resistance equation proposed in [16] could also be verified.…”

Section: A Research Agendamentioning

confidence: 99%