基于循环识别的插电式混合动力汽车优化能量管理策略研究

Yong Ren, Guanlong Yang, W. Liang, Jie Liu, Xueyong Tian
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引用次数: 0

摘要

为了使插电式混合动力汽车获得最优的能耗经济性,适应更复杂的工作环境,提出了基于行驶工况识别的插电式混合动力汽车优化能量管理策略。首先,选取6种循环作为标准工况,分别代表城市拥堵、城市近郊和高速公路,采用复合均匀法计算街区分割的特征参数;其次,应用极限学习机对工作条件进行训练和识别。第三,应用优化算法计算6个标准循环的能量分布规律,并存储控制参数库以供调用。在MATLAB/SIMULINK平台上建立了优化模型,对工况识别和非工况识别的能量管理策略进行了仿真。仿真结果表明,当SOC初始值为0.95、0.75、0.55和0.35时,基于行驶工况识别的控制策略的能耗经济性分别提高了13.8%、16.4%、14.8%和11.1%。
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 Optimization Energy Management Strategies Based on Driving Cycle Recognition for Plug-in Hybrid Electric Vehicle
In order to make the plug-in hybrid electric vehicle obtain optimal energy consumption economy and adapt to more complex working environment, the optimization energy management strategies based on driving cycle recognition were made. First, six kinds of cycle as standard working were selected to represent urban congestion, city suburban and highway, and the characteristic parameters of block segmentation were calculated by use of composite uniform method. Second, the extreme learning machine was applied to train and identity working conditions. Third, the optimum algorithm was applied to calculate the energy distribution rules of six standard cycles, which was stored control parameter library in order to call. On the MATLAB/SIMULINK platform, the optimization mode was built and the energy management strategy of conditions recognition and conditions without recognition were simulated. Simulation results indicate that the energy consumption economy of control strategy based on driving cycle recognition have improved 13.8%,16.4%, 14.8%, 11.1%,when the initial value of SOC is 0.95,0.75,0.55 and 0.35.
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