@Article{csse.2021.015945, AUTHOR = {Xiaokan Wang, Qiong Wang, Liang Shuang, Chao Chen}, TITLE = {A Markov Model for Subway Composite Energy Prediction}, JOURNAL = {Computer Systems Science and Engineering}, VOLUME = {39}, YEAR = {2021}, NUMBER = {2}, PAGES = {237--250}, URL = {http://www.techscience.com/csse/v39n2/43831}, ISSN = {}, 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.}, DOI = {10.32604/csse.2021.015945} }