A real-time energy management control strategy for battery and supercapacitor hybrid energy storage systems of pure electric vehicles
发表时间:2021-02-22     阅读次数:     字体:【


摘要

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.


部分图片:

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.

引文信息

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

其他相关论文:

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



上一篇:Design and experimental verification of a fuel cell/supercapacitor passive configuration for a light vehicle
下一篇:Drive Cycle Energy Efficiency of Fuel Cell/Supercapacitor Passive Hybrid Vehicle System
0
联系地址:北京市海淀区中关村南大街5号北京理工大学   Copyright  ©  2020-   先进储能科学与应用课题组  All Rights Reserved.网站地图
友情链接: 北京理工大学    ICEIV2022会议    机械与车辆学院    机械工程学报    Applied Energy期刊