Open Access

ARTICLE

A Markov Model for Subway Composite Energy Prediction

Xiaokan Wang^1,2,*, 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.

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Keywords

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