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Title: Electric train energy consumption modeling

For this paper we develop an electric train energy consumption modeling framework considering instantaneous regenerative braking efficiency in support of a rail simulation system. The model is calibrated with data from Portland, Oregon using an unconstrained non-linear optimization procedure, and validated using data from Chicago, Illinois by comparing model predictions against the National Transit Database (NTD) estimates. The results demonstrate that regenerative braking efficiency varies as an exponential function of the deceleration level, rather than an average constant as assumed in previous studies. The model predictions are demonstrated to be consistent with the NTD estimates, producing a predicted error of 1.87% and -2.31%. The paper demonstrates that energy recovery reduces the overall power consumption by 20% for the tested Chicago route. Furthermore, the paper demonstrates that the proposed modeling approach is able to capture energy consumption differences associated with train, route and operational parameters, and thus is applicable for project-level analysis. The model can be easily implemented in traffic simulation software, used in smartphone applications and eco-transit programs given its fast execution time and easy integration in complex frameworks.
Authors:
 [1] ;  [1]
  1. Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States). Transportation Inst. and Center for Sustainable Mobility
Publication Date:
Grant/Contract Number:
AR0000612
Type:
Accepted Manuscript
Journal Name:
Applied Energy
Additional Journal Information:
Journal Volume: 193; Journal Issue: C; Journal ID: ISSN 0306-2619
Publisher:
Elsevier
Research Org:
PARC, Palo Alto, CA (United States)
Sponsoring Org:
USDOE Advanced Research Projects Agency - Energy (ARPA-E); Transportation for Livability by Integrating Vehicles and the Environment (TranLIVE); Georgia Inst. of Technology, Atlanta, GA (United States)
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; 42 ENGINEERING; 97 MATHEMATICS AND COMPUTING; Electric train; Energy consumption model; Regenerative braking efficiency; Rail transit simulation
OSTI Identifier:
1427870
Alternate Identifier(s):
OSTI ID: 1414493

Wang, Jinghui, and Rakha, Hesham A. Electric train energy consumption modeling. United States: N. p., Web. doi:10.1016/j.apenergy.2017.02.058.
Wang, Jinghui, & Rakha, Hesham A. Electric train energy consumption modeling. United States. doi:10.1016/j.apenergy.2017.02.058.
Wang, Jinghui, and Rakha, Hesham A. 2017. "Electric train energy consumption modeling". United States. doi:10.1016/j.apenergy.2017.02.058. https://www.osti.gov/servlets/purl/1427870.
@article{osti_1427870,
title = {Electric train energy consumption modeling},
author = {Wang, Jinghui and Rakha, Hesham A.},
abstractNote = {For this paper we develop an electric train energy consumption modeling framework considering instantaneous regenerative braking efficiency in support of a rail simulation system. The model is calibrated with data from Portland, Oregon using an unconstrained non-linear optimization procedure, and validated using data from Chicago, Illinois by comparing model predictions against the National Transit Database (NTD) estimates. The results demonstrate that regenerative braking efficiency varies as an exponential function of the deceleration level, rather than an average constant as assumed in previous studies. The model predictions are demonstrated to be consistent with the NTD estimates, producing a predicted error of 1.87% and -2.31%. The paper demonstrates that energy recovery reduces the overall power consumption by 20% for the tested Chicago route. Furthermore, the paper demonstrates that the proposed modeling approach is able to capture energy consumption differences associated with train, route and operational parameters, and thus is applicable for project-level analysis. The model can be easily implemented in traffic simulation software, used in smartphone applications and eco-transit programs given its fast execution time and easy integration in complex frameworks.},
doi = {10.1016/j.apenergy.2017.02.058},
journal = {Applied Energy},
number = C,
volume = 193,
place = {United States},
year = {2017},
month = {5}
}