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Intelligent energy management strategy of hybrid energy storage system for electric vehicle based on driving pattern recognition

机译：基于驾驶模式识别的电动汽车混合动力储能系统智能能源管理策略

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摘要

To achieve optimal power distribution of hybrid energy storage system composed of batteries and supercapacitors in electric vehicles, an adaptive wavelet transform-fuzzy logic control energy management strategy based on driving pattern recognition (DPR) is proposed in view of the fact that driving cycle greatly affects the performance of EMS. The DPR uses cluster analysis to classify driving cycles into different patterns according to the features extracted from the historical driving data sampling window and utilizes pattern recognition to identify real-time driving patterns. After recognition results are obtained, an adaptive wavelet transform is employed to allocate the high frequency components of power demand to supercapacitor which contains transient power and rapid variations, while the low frequency components are distributed to battery accordingly. The use of fuzzy logic control is to maintain the SOC of supercapacitor within desired level. The simulation results indicate that the proposed control strategy can effectively decrease the maximum charge/discharge current of battery by 58.2%, and improve the battery lifetime by 6.16% and the vehicle endurance range by 11.06% compared with conventional control strategies. Further demonstrate the advantage of hybrid energy storage system and the presented energy management strategy.
机译：为实现电动汽车中由电池和超级电容器组成的混合动力储能系统的最优功率分配，针对行驶周期影响较大的问题，提出了一种基于驾驶模式识别（DPR）的自适应小波变换-模糊逻辑控制能量管理策略。 EMS的性能。 DPR根据从历史驾驶数据采样窗口提取的特征，使用聚类分析将驾驶周期分类为不同的模式，并利用模式识别来识别实时驾驶模式。获得识别结果后，采用自适应小波变换将功率需求的高频分量分配给包含瞬态功率和快速变化的超级电容器，而低频分量则相应地分配给电池。模糊逻辑控制的使用是为了将超级电容器的SOC保持在期望的水平内。仿真结果表明，与传统控制策略相比，该控制策略可有效降低电池的最大充放电电流58.2％，将电池寿命延长6.16％，将车辆续航里程提高11.06％。进一步展示了混合储能系统的优势和提出的能源管理策略。

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  • 来源
    《Energy》 |2020年第may1期|117298.1-117298.17|共17页
  • 作者

    Jie Hu; Di Liu; Changqing Du; Fuwu Yan; Chen Lv;

  • 作者单位

    Hubei Key Laboratory of Advanced Technology for Automotive Components Wuhan University of Technology Wuhan 430070 China Hubei Collaborative Innovation Center for Automotive Components Technology Wuhan University of Technology Wuhan 430070 China Hubei Research Center for New Energy & Intelligent Connected Vehicle Wuhan University of Technology Wuhan 430070 China;

    School of Mechanical and Aerospace Engineering Nanyang Technological University 639798 Singapore;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Hybrid energy storage system; Driving patterns recognition; Transient power; Adaptive wavelet transform; Fuzzy logic control;

    机译：混合储能系统;驾驶模式识别;瞬态功率自适应小波变换模糊逻辑控制;

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