Trajectory optimization of train cooperative energy-saving operation using a safe deep reinforcement learning approach

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F25%3A50022611" target="_blank" >RIV/62690094:18470/25:50022611 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s10489-025-06542-x" target="_blank" >https://link.springer.com/article/10.1007/s10489-025-06542-x</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10489-025-06542-x" target="_blank" >10.1007/s10489-025-06542-x</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Trajectory optimization of train cooperative energy-saving operation using a safe deep reinforcement learning approach

Popis výsledku v původním jazyce

Energy-efficient optimization of train speed profiles can effectively reduce the traction energy consumption of urban rail transit systems. Existing reinforcement learning (RL) optimization models for optimizing train operation profiles do not proactively handle the utilization constraints of regenerative braking energy (RBE). For this reason, this paper proposes an optimization model of train energy-saving profiles under multi-train cooperative operations. A novel safe deep reinforcement learning algorithm, guided by heuristic rules, is developed to optimize energy-saving train driving strategies in various scenarios. To ensure safety during the agent&apos;s learning processes, a two-layer protection mechanism with soft constraint and truncation penalties is employed. Dynamic energy constraints are also introduced to enable the RBE utilization between trains. The simulation experiments using a real metro line data show that the proposed model and algorithm not only generate safe and energy-efficient profiles that meet metro operational constraints but also maximize the RBE utilization between trains, significantly reducing traction energy consumption.

Název v anglickém jazyce

Trajectory optimization of train cooperative energy-saving operation using a safe deep reinforcement learning approach

Popis výsledku anglicky

Energy-efficient optimization of train speed profiles can effectively reduce the traction energy consumption of urban rail transit systems. Existing reinforcement learning (RL) optimization models for optimizing train operation profiles do not proactively handle the utilization constraints of regenerative braking energy (RBE). For this reason, this paper proposes an optimization model of train energy-saving profiles under multi-train cooperative operations. A novel safe deep reinforcement learning algorithm, guided by heuristic rules, is developed to optimize energy-saving train driving strategies in various scenarios. To ensure safety during the agent&apos;s learning processes, a two-layer protection mechanism with soft constraint and truncation penalties is employed. Dynamic energy constraints are also introduced to enable the RBE utilization between trains. The simulation experiments using a real metro line data show that the proposed model and algorithm not only generate safe and energy-efficient profiles that meet metro operational constraints but also maximize the RBE utilization between trains, significantly reducing traction energy consumption.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Applied Intelligence

ISSN

0924-669X

e-ISSN

1573-7497

Svazek periodika

55

Číslo periodika v rámci svazku

7

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

17

Strana od-do

"Article Number: 651"

Kód UT WoS článku

001463762900002

EID výsledku v databázi Scopus

2-s2.0-105002969745