Optimization of the cruising speed for high-speed trains to reduce energy consumed by motion resistances

Fang-Ru Zhou, Kai Zhou, Duo Zhang and Qi-Yuan Peng

Applied Energy, 2024, vol. 374, issue C, No S0306261924014223

Abstract: As one of the effective measures to reduce energy consumption of high-speed railway, train operation control has gained much attention because of its cost-effectiveness. Conventional approaches to eco-driving generally rely on single-mass train models and lack a thorough investigation into the vehicle cruising process, which can overlook real-world operation conditions and hinder further improvements in energy efficiency. To address these issues, this paper proposes a comprehensive framework for optimizing the cruising speed of high-speed trains (HSTs) based on track conditions to reduce energy consumed by motion resistances (ECMR). Firstly, a multibody dynamics simulation model of the HST is developed and validated to accurately evaluate ECMR under a range of operation scenarios. Simulation results show that the impact of speed on ECMR varies considerably according to track characteristics. Subsequently, a neural network is constructed to derive the regressive relationship between operation conditions and ECMR. On this basis, an optimization model for the cruising speed under different track conditions is formulated to minimize the ECMR of a railway segment with a given operation time. Finally, the effectiveness of the model is validated through case studies, which demonstrate that ECMR can be reduced by up to 27.85% compared to traditional control strategies. The proposed optimization method can be integrated with current train speed profiles to further enhance energy efficiency. This framework has broad applicability for addressing engineering problems related to train operation optimization.

Keywords: High-speed trains; Energy-efficient operation; Cruising speed optimization; Energy consumed by motion resistances; Multibody dynamic simulation; Neural network regression (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.apenergy.2024.124039

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