Adaptive model predictive control for hybrid energy storage energy management in all-electric ship microgrids. (15th October 2019) - Hou, Jun; Song, Ziyou; Hofmann, Heath; Sun, Jing
Highlights: An adaptive model predictive control is developed to address parameter uncertainties. A sensitivity analysis is studied to provide insights into the impact of uncertainties. The proposed method is validated and discussed in both simulation and experiments. In the experiment, the improvement of power losses can be as high as 15%. Abstract: Hybrid energy storage systems have been widely used in transportation, microgrid and renewable energy applications to improve system efficiency and enhance reliability. However, parameter uncertainty can significantly affect system performance. In order to address this issue, an adaptive model predictive control is developed in this paper. Online parameter identification is used to mitigate parameter uncertainty, and model predictive control is used to optimally split power, deal with constraints, and achieve desired dynamic responses. A sensitivity analysis is conducted to identify major impact factors. In order to validate the proposed method, both simulation and experiments are performed to show the effectiveness of the proposed adaptive model predictive control. Compared to the model predictive control without online parameter identification, the power loss reduction can be as high as 15% in the experiments. This study focuses on all-electric ship energy management to mitigate load fluctuations and improve system efficiency and reliability. The proposed method could also be used in other applications.