Paper Info
Title
A Markov Model For Subway Composite Energy Prediction
Abstract
Electric vehicles such as trains must match their electric power supply and demand, such as by using a composite energy storage system composed of lithium batteries and supercapacitors. In this paper, a predictive control strategy based on a Markov model is proposed for a composite energy storage system in an urban rail train. The model predicts the state of the train and a dynamic programming algorithm is employed to solve the optimization problem in a forecast time domain. Real-time online control of power allocation in the composite energy storage system can be achieved. Using standard train operating conditions for simulation, we found that the proposed control strategy achieves a suitable match between power supply and demand when the train is running. Compared with traditional predictive control systems, energy efficiency 10.5% higher. This system provides good stability and robustness, satisfactory speed tracking performance and control comfort, and significant suppression of disturbances, making it feasible for practical applications.
Year
DOI
Venue
2021
10.32604/csse.2021.015945
COMPUTER SYSTEMS SCIENCE AND ENGINEERING
Keywords
DocType
Volume
Markov model, predictive control, composite energy storage, urban&nbsp, rail train
Journal
39
Issue
ISSN
Citations 
2
0267-6192
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Xiaokan Wang111.38
Qiong Wang201.35
Shuang Liang378.41
Chao Chen400.34