混联式混合动力汽车模糊逻辑能量管理与传动比优化研究

发布时间：2018-05-19 08:12

本文选题：混联式混合动力汽车 + 能量管理　；参考：《河北工程大学》2016年硕士论文

【摘要】：随着世界经济的迅速发展,环境污染和能源短缺问题已受到各国重视,而由汽车等交通工具尾气排放引起的大气污染带来的问题日益突出,为降低汽车尾气带来的污染,各国汽车厂商正大力发展新能源汽车。混联式混合动力汽车兼有串并联结构的所有优点,能适应各种复杂的道路状况,但由于混联式混合动力汽车结构复杂,进行能量管理和参数匹配显得十分重要,欲实现这一目标,就需要对相应的多能量源进行协调控制,而多能源的控制是一种非线性程度较高的问题,具体因其混合动力汽车系统本身和部件之间的协调工作很复杂且很难建立精确的数学模型。因此必须借助较智能的控制策略来开发出具有通用性和的特性的控制策略来满足在不同驾驶风格进而道路工况。本文针对混联式混合动力汽车以改善燃油经济性为目的,对能量管理分配、再生制动及传动比优化等问题进行研究,具体工作如下:根据混联式特点,以其主要能源功率匹配及关键部件建模为基础,通过以解决非线性模糊量见长的模糊逻辑算法制定出提高燃油效率并可用于实际的工程的模糊控制策略,以获取发动机最佳工作曲线及兼顾电池效率和电机功率并重为核心,建立Matlab模糊逻辑管理原则及算法对两者之间的能量进行分配;然后在对其再生制动能量回馈也进行模糊算法研究,弥补汽车能量损失,延长续驶里程;最后在对其传动系比进行优化,更进一步提升整车燃油效率。具体分析如下:在混联式混合动力汽车模型的基础上,根据工况模式,设计以SOC、车速v,需求转矩T_req为输入,发动机功率P1与电动机功率P2为输出的模糊逻辑管理规则,然后根据输入输出,采用Stateflow建立模糊逻辑下的混联式混合动力汽车不同驱动下模式切换逻辑,选择在组合工况下,以嵌入到AVL-Cruise中模糊控制策略为功率分配为依托,对整车进行仿真,将结果与以电力辅助为核心的控制策略比较,显示出模糊控制策略在功率分配上更加合理,对燃油性和动力性有提高作用。由模糊逻辑分配得到的再生能量和摩擦能量也达到预期效果。最后将粒子群算法优化后的传动比导入到Cruise中,对整车性能较之前有了改善,换挡变得更加平稳顺滑,既提高电动汽车的动力性,又能降低其能量消耗。
[Abstract]:With the rapid development of the world economy, the problems of environmental pollution and energy shortage have been paid more and more attention by many countries. Car manufacturers all over the world are vigorously developing new energy vehicles. Hybrid HEV has all the advantages of series-parallel structure and can adapt to various complicated road conditions. However, because of the complex structure of hybrid HEV, it is very important to manage the energy and match the parameters. To achieve this goal, coordinated control of the corresponding multi-energy sources is required, and the control of multi-energy sources is a highly nonlinear problem. It is difficult to establish accurate mathematical model because of the complexity of the coordination between the system itself and the components of the hybrid electric vehicle (HEV). Therefore, it is necessary to develop a general and characteristic control strategy with the help of intelligent control strategies to meet different driving styles and road conditions. In this paper, the problems of energy management, regenerative braking and transmission ratio optimization are studied for the purpose of improving fuel economy of hybrid vehicle. The specific work is as follows: according to the characteristics of hybrid, Based on the main energy power matching and the modeling of key components, a fuzzy control strategy to improve fuel efficiency and be used in practical engineering is developed by using fuzzy logic algorithm to solve the problem of nonlinear fuzzy quantity growth. In order to obtain the best working curve of engine and take both battery efficiency and motor power as the core, the principle and algorithm of Matlab fuzzy logic management are established to distribute the energy between the two. Then the fuzzy algorithm of regenerative braking energy feedback is studied to make up for the energy loss of the vehicle and prolong the driving mileage. Finally the ratio of transmission system is optimized to further improve the fuel efficiency of the whole vehicle. The detailed analysis is as follows: on the basis of the hybrid electric vehicle model, according to the operating mode, the fuzzy logic management rule is designed, which takes SOC, speed, T_req as input, engine power P1 and motor power P2 as output. Then, according to the input and output, Stateflow is used to establish the mode switching logic of hybrid electric vehicle under different driving conditions under fuzzy logic, which is based on the fuzzy control strategy embedded in AVL-Cruise. The simulation results show that the fuzzy control strategy is more reasonable in power distribution and can improve the fuel and power performance. The regenerative energy and friction energy obtained from fuzzy logic distribution also reach the expected effect. Finally, the transmission ratio optimized by particle swarm optimization is introduced into Cruise, and the performance of the whole vehicle is improved, and the shift becomes more smooth and smooth, which not only improves the power performance of electric vehicle, but also reduces its energy consumption.
【学位授予单位】：河北工程大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：U469.7

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