Hybrid energy storage systems have attracted more and more interests due to their improved performances compared with sole energy source in system efficiency and battery lifetime. This study aims to propose a real-time energy management control strategy for achieving these goals. The strategy is based on a combination of wavelet transform, neural network and fuzzy logic. Wavelet transform is an effective tool in extracting different frequency components of load power demand to match the characteristics of battery and supercapacitor. However, it is hard to be directly applied in a real-time system. For this, a neural network model, which is offline trained using the dataset obtained from the wavelet transform decomposition, is developed to online predict the low frequency power demand for the battery. Accordingly, the high frequency power demand is online calculated and distributed to the supercapacitor. In addition, a fuzzy logic based supervisory controller is further developed for controlling the supercapacitor voltage within a certain suitable range. Finally, a 72 V battery and 96 V supercapacitor hybrid energy storage system real-time hardware platform has been developed to validate the effectiveness of the proposed energy management control strategy.
图1 Detailed control framework of hybrid energy storage system in this study.
图2 A dedicated scaled-down experimental platform, which includes: (1) battery pack, (2) supercapacitor pack, (3) RapidECU, (4) DC/DC converter, (5) load simulator, (6) PC.
Q. Zhang, L. Wang, G. Li, Y. Liu, A real-time energy management control strategy for battery and supercapacitor hybrid energy storage systems of pure electric vehicles, Journal of Energy Storage. 31 (2020) 101721. https://doi.org/10.1016/j.est.2020.101721.（下载链接）
1. C. Wang, R. Xiong, H. He, Y. Zhang, W. Shen, Comparison of decomposition levels for wavelet transform based energy management in a plug-in hybrid electric vehicle. J. Clean. Prod. (2019) (2019), pp. 1085-1097.（下载链接）
2. R. Xiong, J. Cao, Q. Yu, Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle. Appl. Energy, 211 (2018), pp. 538-548.（下载链接）