Eco-Driving for Metro Trains: A Computationally Efficient Approach Using Convex Programming

Xiao, Zhuang; Murgovski, Nikolce; Chen, Mo; Feng, Xiaoyun; Wang, Qingyuan; Sun, Pengfei

doi:10.1109/tvt.2023.3262345

Cited by 9 publications

(2 citation statements)

References 44 publications

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“…The least amount of remaining energy of the battery system is at the final instance. Consequently, if an optimal value of 𝜆 * e can be found to satisfy the constraint (25h) at the final instance, the dynamics (24) and constraint (25h) can be removed from the optimization problem by adding the product of 𝜆 * e and the battery force 22), ( 23), (25c ), (25d ), (25e), (25 f ), (25g), (28b)…”

Section: Analysis Based On Pmp Principlementioning

confidence: 99%

“…To improve computational efficiency while obtaining optimal solutions, an alternative way is to reformulate the problem as a convex problem by convex relaxation and approximation techniques [21,22]. An approximation of energy consumption model is used to reformulate the original problem as a convex program [23,24]. In addition, an exact convex relaxation is developed in [25] to minimize train operational energy consumption, which can obtain globally optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal driving strategies for emergency operation of high‐speed trains using on‐board energy storage systems

Zhu

Zhang

Chen

et al. 2023

IET Intelligent Trans Sys

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A power outage occurs when there is an interruption to traction power system. In such emergency situations, trains are expected to achieve autonomy operation powered by on‐board energy storage systems (OESS). This paper presents optimization models and methods to find optimal driving strategies for train emergency operation. Specifically, a nonlinear and non‐convex program is first formulated in space‐domain to minimize trip time under the limits of power and energy capacity of OESS. To improve computational efficiency, the time dynamics is removed from constraints and adjoined to the cost function. Furthermore, convex modelling steps are proposed to reformulate the problem as a convex program that can be solved efficiently. A bi‐level algorithm is designed to obtain optimal driving strategies that can arrive at destinations as soon as possible. Compared to energy‐optimal driving strategies, the proposed methods can effectively reduce the trip time.

show abstract

Section: Analysis Based On Pmp Principlementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal driving strategies for emergency operation of high‐speed trains using on‐board energy storage systems

Zhu

Zhang

Chen

et al. 2023

IET Intelligent Trans Sys

View full text Add to dashboard Cite

show abstract

Multi-objective online driving strategy optimization for energy storage tram considering battery life

Wang,

Wu,

Zhang

et al. 2024

Journal of Energy Storage

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Two‐objective train operation optimization based on eco‐driving and timetabling

Wang,

Feng,

Sun

et al. 2023

IET Intelligent Trans Sys

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In urban railway systems, the timetable guides the section operation of the single train and the arrangement of the train group to meet the dual needs of cost and passengers. This paper proposes a two‐objective train operation optimization based on eco‐driving and timetabling to restore a more realistic scene, including a method level and an objective level. For the method level, the speed curve optimization of the single train and the timetable optimization of the train group are adopted jointly. For the objective level, both the total energy consumption of the train group and the consuming time of passengers are considered. A hybrid solution strategy based on quadratic programming and improved artificial bee colony algorithm is proposed. A hardware‐in‐the‐loop platform is built to carry out validation experiments. Both the cases in general hours and special hours are verified based on the actual data from Beijing Metro Line 15. The results show that both the energy consumption and the passenger consuming time are reduced simultaneously. Correspondingly, the speed curve and the time distribution of the timetable are individually optimized based on the fluctuating passenger flow.

show abstract

Eco-Driving for Metro Trains: A Computationally Efficient Approach Using Convex Programming

Cited by 9 publications

References 44 publications

Optimal driving strategies for emergency operation of high‐speed trains using on‐board energy storage systems

Optimal driving strategies for emergency operation of high‐speed trains using on‐board energy storage systems

Multi-objective online driving strategy optimization for energy storage tram considering battery life

Two‐objective train operation optimization based on eco‐driving and timetabling

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