摘要
基于驾驶意图云模型识别的自适应等效燃油消耗最小策略(A-ECMS),构建了一种混合动力汽车能量管理策略,并对该策略进行了仿真研究。采用云模型算法,识别驾驶意图,获取了典型行驶工况下的意图识别结果;以加权平均的方法,求得各意图下对应的等效因子;建立了等效因子查询表。结果表明:在新欧洲行驶循环(NEDC)工况下,相比于基于充电状态(SOC)反馈的A-ECMS策略,本策略的燃油经济性提高了1.3%;在自定义组合工况下也能取得近似ECMS策略的燃油经济性控制效果,且具有更好的SOC稳定性控制效果。因而,该A-ECMS策略提高了原来的等效燃油消耗最小策略(ECMS)的实用性,体现驾驶意图。
An energy management strategy for hybrid electric vehicles(HEV)was constructed and simulated according to the adaptive equivalent fuel consumption minimization strategy(A-ECMS)based on driving intention cloud model recognition.The cloud model algorithm was used to identify driving intentions to obtain the intention recognition results under some typical driving conditions.By using a weighted average method to establish the look-up table of equivalent factor to calculate the corresponding equivalent factors under each intention.The results show that the proposed A-ECMS strategy improves fuel economy by 1.3%compared with the A-ECMS based on SOC(state of charge)feedback under the New European Drive Cycle(NEDC).By using ECMS strategy obtains a similar fuel economy under combination condition with a better SOC stability.Therefore,the proposed A-ECMS strategy improves the practical application of the old ECMS strategy with a good reflecting for different driving intentions.
作者
邓涛
罗远平
DENG Tao;LUO Yuanping(School of Mechatronics&Automotive Engineering,Chongqing Jiaotong University,Chongqing 400074,China;School of Aeronautic,Chongqing Jiaotong University,Chongqing 400074,China)
出处
《汽车安全与节能学报》
CAS
CSCD
2020年第3期305-313,共9页
Journal of Automotive Safety and Energy
基金
国家自然科学基金资助项目(51305473)
重庆市技术预见与制度创新项目(cstc2019jsyj-yzysbAX0030)
第五批重庆市高校优秀人才支持计划项目(2017)。
