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

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Trajectory optimization of train cooperative energy-saving operation using a safe deep reinforcement learning approach
Original language description
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'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.
Czech name
—
Czech description
—

Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)