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Electric train energy consumption modeling

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  • Wang, Jinghui
  • Rakha, Hesham A.

Abstract

The paper develops 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.

Suggested Citation

  • Wang, Jinghui & Rakha, Hesham A., 2017. "Electric train energy consumption modeling," Applied Energy, Elsevier, vol. 193(C), pages 346-355.
    • Handle: RePEc:eee:appene:v:193:y:2017:i:c:p:346-355
    DOI: 10.1016/j.apenergy.2017.02.058
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    7. Oke, Jimi B. & Akkinepally, Arun Prakash & Chen, Siyu & Xie, Yifei & Aboutaleb, Youssef M. & Azevedo, Carlos Lima & Zegras, P. Christopher & Ferreira, Joseph & Ben-Akiva, Moshe, 2020. "Evaluating the systemic effects of automated mobility-on-demand services via large-scale agent-based simulation of auto-dependent prototype cities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 140(C), pages 98-126.
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    9. Pan, Deng & Zhao, Liting & Luo, Qing & Zhang, Chuansheng & Chen, Zejun, 2018. "Study on the performance improvement of urban rail transit system," Energy, Elsevier, vol. 161(C), pages 1154-1171.

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