We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
A Markov Model for Subway Composite Energy Prediction.
- Authors
Xiaokan Wang; Qiong Wang; Liang Shuang; Chao Chen
- 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.
- Subjects
ELECTRIC vehicles; MARKOV processes; ELECTRIC power; ENERGY storage; DYNAMIC programming
- Publication
Computer Systems Science & Engineering, 2021, Vol 39, Issue 2, p237
- ISSN
0267-6192
- Publication type
Article
- DOI
10.32604/csse.2021.015945