【作者】 王婷；

【导师】 张欣；

【作者基本信息】 北京交通大学 ， 动力机械及工程， 2009， 硕士

【摘要】 控制策略是混合动力电动汽车的关键技术之一,本文结合国家“863”计划电动汽车重大专项的研究课题“电动汽车控制算法与基础技术研究”的研究工作,针对串并联混合动力汽车分别建立了串联和并联混合动力汽车的仿真模型及其相应的能量管理控制策略。并在此基础上,应用模糊逻辑技术,制定了模糊逻辑控制策略,构建了模糊推理器,用以确定发动机和电机的最佳转矩分配。以并联混合动力汽车为研究对象,UDDS、CHINA、NEDC三种循环工况的仿真结果显示,在燃油经济性方面,模糊控制与电辅助控制相比,模糊控制下的整车燃油经济性分别提高9.3%、8.4%和7.6%。为实现模糊逻辑控制在实际HEV上的应用打下了良好的基础。本文将蓄电池高比能量和超级电容高比功率的优点结合起来,建立了复合电源模型,并且制定了相应的控制策略,以避免电池大电流充放电和提高制动能量的回收率。在UDDS和NEDC循环工况下,分别对复合电源和单一的电池电源进行仿真,结果表明复合电源制动能量回收率分别为71.4%、81.3%,而单一的电池电源制动能量回收率分别为43.2%、68.5%,复合电源的引入使得制动能量回收率分别提高28.1%和12.8%。综合考虑动力系统匹配参数和控制策略参数对整车性能的影响,提出将遗传算法和模拟退火算法相结合的组合优化算法,分别对以油耗为单目标和以油耗和排放为多目标进行了优化分析。优化结果表明,单目标优化前后进行对比,优化后并联混合动力汽车油耗降低9.6%左右,排放也有所下降;多目标优化前后相比,优化后并联混合动力汽车油耗下降约7.1%,而CO、HC和NO_x排放降低得比较多,优化后三者分别下降23.4%、5.6%和17.4%。更多还原

【Abstract】 Control strategy for hybrid electric vehicle is one of the key technologies. Supported by the major project "Research on Control Algorithm and Basis Technology for Electric Vehicles" under "High-Tech R&D Program of China", the energy management control strategies for series and parallel hybrid electric vehicles have been research. On this basis, a fuzzy logic control strategy has been build in order to distribute the torque between engine and motor reasonably. The simulation results of three cycles UDDS、CHINA and NEDC show that the fuel economy in fuzzy control strategy is better. It improves 9.3%、8.4%and 7.6% respectively compared with the electric assist control strategy. This has laid a good foundation for the application of a fuzzy logic control strategy in the actual hybrid electric vehicles.In this paper, combined the benefits of batteries and ultra capacitors, a hybrid power system has been model and the corresponding control strategy in order to avoid the high current and improve the recovery of braking energy has been research. The simulations of the hybrid power system and the battery power system have been done in the cycles of UDDS and NEDC respectively. The results show that the braking energy recovery rates of the hybrid power system are 71.4% and 81.3%, and that of the battery power system are 43.2% and 68.5%.In view of optimizing for design parameters, considered of performances on hybrid electrical vehicle of power-train parameters and control parameters, the optimizations on both parameters are concurrently performed. A new optimization scheme in which the genetic algorithm is used combined with simulated annealing algorithm is proposed. The optimizations for single-objective function and multi-objective function have been done respectively. Optimization results show that the fuel consumption of the single-objective optimization reduces 9.6% and emissions also decline compared with pre-optimization, and that of the multi-objective optimization reduces 7.1% and emissions of CO, HC and NO_x fell 23.4%, 5.6% and 17.4% compared with pre-optimization.更多还原