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A Markov Model for Subway Composite Energy Prediction

Xiaokan Wang1,2,*, Qiong Wang1, Liang Shuang3, Chao Chen4
1 Henan Mechanical and Electrical Vocational College, Xinzheng, 451191, China
2 School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China
3 University of Florence, Firenze, 50041, Italy
4 Henan Mechanical and Electrical Vocational College, Xinzheng, 451191, China
* Corresponding Author: Xiaokan Wang. Email:

Computer Systems Science and Engineering 2021, 39(2), 237-250. https://doi.org/10.32604/csse.2021.015945

Received 15 December 2020; Accepted 15 March 2021; Issue published 20 July 2021

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.

Keywords

Markov model; predictive control; composite energy storage; urban rail train

Cite This Article

X. Wang, Q. Wang, L. Shuang and C. Chen, "A markov model for subway composite energy prediction," Computer Systems Science and Engineering, vol. 39, no.2, pp. 237–250, 2021.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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