An eco-driving algorithm for trains through distributing energy: A Q-Learning approach.
ISA Trans
; 122: 24-37, 2022 Mar.
Article
en En
| MEDLINE
| ID: mdl-33972080
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.
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1
Base de datos:
MEDLINE
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
ISA Trans
Año:
2022
Tipo del documento:
Article
País de afiliación:
China