Energy-Efficient Train Control With Onboard Energy Storage Systems Considering Stochastic Regenerative Braking Energy

Wu, Chaoxian, Lu, Shaofeng, Tian, Zhongbei, Xue, Fei and Jiang, Lin ORCID: 0000-0001-6531-2791 (2025) Energy-Efficient Train Control With Onboard Energy Storage Systems Considering Stochastic Regenerative Braking Energy IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION, 11 (1). pp. 257-274. ISSN 2332-7782, 2332-7782

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Abstract

With the rapid development of energy storage technology, onboard energy storage systems (OESS) have been applied in modern railway systems to help reduce energy consumption. In addition, regenerative braking energy utilization is becoming increasingly important to avoid energy waste in the railway systems, undermining the sustainability of urban railway transportation. However, the intelligent energy management of the trains equipped with OESSs considering regenerative braking energy utilization is still rare in the field. This article considers the stochastic characteristics of the regenerative braking power distributed in railway power networks. It concurrently optimizes the train trajectory with OESS and regenerative braking energy utilization. The expected regenerative braking power distribution can be obtained based on the Monte-Carlo simulation of the train timetable. Then, the integrated optimization using mixed integer linear programming (MILP) can be conducted and combined with the expected available regenerative braking energy. A generic four-station railway system powered by one traction substation is modeled and simulated for the study. The results show that by applying the proposed method, 68.8% of the expected regenerative braking energy in the environment will be further utilized. The expected amount of energy from the traction substation is reduced by 22.0% using the proposed train control method to recover more regenerative braking energy from improved energy interactions between trains and OESSs.

Item Type:	Article
Uncontrolled Keywords:	Rail transportation, Transportation, Motors, Trajectory, Energy storage, Energy consumption, Substations, Intelligent energy management, Monte-Carlo simulation, onboard energy storage system (OESS), regenerative braking energy, train trajectory
Divisions:	Faculty of Science & Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	24 Apr 2024 11:25
Last Modified:	16 Jun 2026 17:02
DOI:	10.1109/TTE.2024.3389960
Related Websites:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3180563
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