基于微行程识别的优化能量管理策略研究

Zelong Zhang, Y. Huangfu, Liangcai Xu, Jun Zhao, Wenzhuo Shi, Tianying Yu
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引用次数: 1

摘要

燃料电池电动汽车的能量管理策略对汽车的性能有很大的影响,因此研究人员在这一领域进行了大量的研究。为了提高传统策略对复杂工况的适应性,将基于智能算法的工况识别方法引入到能量管理策略中。然而,现有的识别方法存在着识别精度低、通用性低等缺点。针对这些问题,本文提出了一种基于微行程识别的模糊优化能量管理策略。该策略首先将准备识别的工况划分为若干微行程,以提高识别精度;然后,在本文构建的简化电力系统上对该策略进行了仿真,并与一般基于规则的策略的仿真结果进行了比较。所提策略的较好性能证明了该方法的有效性和最优性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Optimized Energy Management Strategy Based on Micro-trip Recognition
The energy management strategy of fuel cell electric vehicle can greatly influence the performance of vehicle, so a lot of research in this field were done by researchers. In order to improve the adaptability to complex work conditions of traditional strategies, the work condition recognition methods based on intelligence algorithms were introduced to energy management strategies. However, there are a lot of disadvantages of present recognition methods, such as low recognition accuracy and low generality. Aiming at solving these problems, an optimized fuzzy energy management strategy based on micro-trip recognition is proposed in this paper. In this strategy, firstly the work conditions prepared for recognition are divided into several micro-trips to improve the accuracy of recognition. Then, the strategy is simulated on the simplified power system built in this paper and compared with the simulation result of a general rule-based strategy. The better performance of the strategy proposed proves the effectiveness and optimality of this method.
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