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发动机管理策略对增程式电动车燃油经济性的影响研究 被引量:2

Research on Influence of Engine Management Strategy on Fuel Economy of Extended-Range Electric Vehicle
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摘要 为了提高增程式电动车的燃油经济性,在传统定点控制式发动机管理策略的基础上,提出了模糊控制式管理策略,对不同的发动机管理策略进行了仿真研究。在CRUISE软件中建立了增程式电动车的驱动系统模型,并在Simulink软件中搭建了发动机管理策略模型。在模糊逻辑控制器中,使用T-S模糊系统和Mamdani模糊系统,建立了模糊控制规则。在全球统一轻型车测试规程(worldwide light-duty test cycle,WLTC)循环工况下进行了联合仿真,将模糊控制式管理策略与定点控制式管理策略的结果进行了对比分析。仿真结果显示,与定点控制式管理策略相比,基于T-S模糊系统和Mamdani模糊系统的发动机管理策略分别使增程式电动车的燃油经济性提高了12.52%和10.60%。其中,基于T-S模糊系统的发动机管理策略效果更好,燃油经济性更优。 In order to improve the fuel economy of extended-range electric vehicles,a fuzzy control management strategy based on the traditional fixed-point engine management strategy was proposed,and both management strategies were simulated.The drive system model of an extended-range electric vehicle was established with the CRUISE software,and the engine management strategy model was built with the Simulink software.In fuzzy logic controller,the T-S fuzzy system and Mamdani fuzzy system were used,and fuzzy control rules were developed.Co-simulation was carried out under the worldwide light-duty test cycle(WLTC)conditions,and the results of the fuzzy control management strategy and fixed-point management strategy were analyzed.The simulation results show that compared with the fixed-point management strategy,the engine management strategies based on the T-S fuzzy system and Mamdani fuzzy system improve the fuel economy of the extended-range electric vehicle by 12.52%and 10.60%respectively.The engine management strategy based on the T-S fuzzy system is better in terms of management effect and fuel economy.
作者 骆光炬 邓晓亭 鲁植雄 周华栋 周润东 LUO Guangju;DENG Xiaoting;LU Zhixiong;ZHOU Huadong;ZHOU Rundong(College of Engineering, Nanjing Agricultural University, Nanjing 210031, China)
出处 《内燃机工程》 EI CAS CSCD 北大核心 2020年第4期79-86,共8页 Chinese Internal Combustion Engine Engineering
基金 中央高校基本科研业务费自主创新重点研究项目(KYJX201702)。
关键词 发动机管理策略 模糊控制 T-S模糊系统 Mamdani模糊系统 联合仿真 燃油经济性 engine management strategy fuzzy control T-S fuzzy system Mamdani fuzzy system co-simulation fuel economy
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