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首页> 外文期刊>Journal of Energy Storage >A real-time energy management control strategy for battery and supercapacitor hybrid energy storage systems of pure electric vehicles
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A real-time energy management control strategy for battery and supercapacitor hybrid energy storage systems of pure electric vehicles

机译：纯电动汽车电池和超级电容器混合储能系统的实时能源管理控制策略

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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.
机译：由于系统效率和电池寿命的唯一能源相比，混合能量存储系统引起了越来越多的兴趣。本研究旨在提出实现这些目标的实时能源管理控制策略。该策略基于小波变换，神经网络和模糊逻辑的组合。小波变换是提取负载电源需求的不同频率分量的有效工具，以匹配电池和超级电容器的特性。但是，很难直接应用于实时系统。为此，开发了使用从小波变换分解的数据集进行脱机训练的神经网络模型，以在线预测电池的低频电力需求。因此，高频电力需求在线计算并分配给超级电容器。另外，进一步开发了一种基于模糊的基于逻辑的监控控制器，用于控制一定合适的范围内的超级电容器电压。最后，已经开发出72 V电池和96 V超级电容器混合能量存储系统实时硬件平台以验证所提出的能源管理控制策略的有效性。

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