.png){kind=icon}
.png){kind=icon}
Return
Energy-efficient train control considering on-board energy storage based on convex optimization
.png){kind=icon}
PRE
AI
DOI：10.1080/23248378.2025.2529403.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
As energy consumption in rail transit becomes increasingly prominent, the efficient utilization of regenerative braking energy by on-board energy storage devices is crucial. However, the complex coupling between energy storage devices and train operation systems, as well as the modelling of state-of-charge (SOC) estimation, requires further improvement. This paper proposes a co-optimization method for train with on-board energy storage driving strategies and energy management based on time-domain modelling. By employing convex relaxation and convex approximation techniques, non-convex problems are transformed into convex optimization problems that can be efficiently solved. A correction framework is designed to reduce the mapping errors between time-domain modelling and actual route gradient and speed limit information. Experimental results demonstrate that the co-optimization method outperforms sequential optimization in energy efficiency, achieving energy efficient rates of 20.86%, 10.34%, and 11.97% for three route scenarios. Additionally, the maximum and average relaxation errors are below 0.002%, and the calculation time is within 1 s, indicating its potential for online optimization applications.
Keywords:
Train with on-board energy storage
co-optimization
convex optimization
time-domain modelling
correction framework
Journal
IF:
3.6
Papers:
1.3K
Citations:
1.2K
Organization
View more organizations