TY - EJOU AU - Wang, Xiaokan AU - Wang, Qiong AU - Shuang, Liang AU - Chen, Chao TI - A Markov Model for Subway Composite Energy Prediction T2 - Computer Systems Science and Engineering PY - 2021 VL - 39 IS - 2 SN - AB - 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. KW - Markov model; predictive control; composite energy storage; urban rail train DO - 10.32604/csse.2021.015945