摘要
针对固定循环工况下所制定的混合动力汽车能量管理策略存在一定局限性问题,从ADVISOR软件中选取覆盖车辆实际行驶工况的20个典型循环工况,以整车综合燃油消耗和动力电池寿命为综合优化目标,利用粒子群算法对各工况下能量管理策略中所涉及的关键参数进行了优化,并将得到的优化结果建立数据库,提出了基于行驶工况识别的混合动力汽车动态能量管理策略。最后,通过选择某个随机工况对所制定的能量管理策略进行仿真。结果表明:所制定的动态能量管理策略与未采用工况识别的能量管理策略相比,车辆综合燃油消耗下降10.70%,动力电池温升和平均有效工作电流分别下降2.46℃和1.63 A。
Energy management strategy of HEV which was built in invariable cycle condition existed some limitations. 20 typical cycle conditions which standed for vehicle real driving conditions were chosen from ADVISOR software and key control parameters of each driving cycle were optimized by using particle swarm algorithm as the comprehensive goal of vehicle total fuel consumption and power battery life, relevant optimized results were saved in database, an energy management strategy of HEV based on driving pattern recognition was proposed. Finally, simulation for the energy manage- ment strategy was carried out under a random driving condition, simulation results show that vehicle fuel consumption is cut down 10.70%, temperature rise and average operation current are cut down 2.46 ~C and 1.63 A respectively by using dynamic energy management strategy compared with energy management strategy without driving pattern recognition.
出处
《中国机械工程》
EI
CAS
CSCD
北大核心
2014年第11期1550-1555,共6页
China Mechanical Engineering
基金
国家自然科学基金资助项目(51305468)
中央高校基本科研业务费专项资金资助项目(CDJZR12110005)
机械传动国家重点实验室2012年度开放基金资助项目
关键词
混合动力汽车
工况识别
随机工况
动态能量管理策略
hybrid electric vehicle（HEV）
driving pattern recognition
random driving condition
dynamic energy management strategy
