Back to Search
Start Over
An eco-driving algorithm for trains through distributing energy: A Q-Learning approach.
- Source :
- ISA Transactions; Mar2022, Vol. 122, p24-37, 14p
- Publication Year :
- 2022
-
Abstract
- The energy-efficient train operation methodology is the focus of this paper, and a Q-Learning-based eco-driving approach is proposed. Firstly, the core idea of energy-distribution-based method (EDBM) that converts the eco-driving problem to the finite Markov decision process is presented. Secondly, Q-Learning approach is proposed to determine the optimal energy distribution policy. Specifically, two different state definitions, i.e., trip-time-relevant (TT) and energy-distribution-relevant (ED) state definitions, are introduced. Finally, the effectiveness of the proposed approach is verified in a deterministic and a stochastic environment. It is also illustrated that TT-state approach takes about 20 times more computation time compared with ED-state approach while the space complexity of TT-state approach is nearly constant. The hyperparameter sensitivity analysis demonstrates the robustness of the proposed approach. • The inverse problem of energy-efficient train control problem is formulated. • A data-driven method based on Q-Learning approach is proposed. • Two state definitions based on trip time and energy distribution are introduced. [ABSTRACT FROM AUTHOR]
- Subjects :
- MARKOV processes
INVERSE problems
ALGORITHMS
SENSITIVITY analysis
Subjects
Details
- Language :
- English
- ISSN :
- 00190578
- Volume :
- 122
- Database :
- Supplemental Index
- Journal :
- ISA Transactions
- Publication Type :
- Academic Journal
- Accession number :
- 155843877
- Full Text :
- https://doi.org/10.1016/j.isatra.2021.04.036