【作者】 王静；

【导师】 田光宇；

【作者基本信息】 清华大学 ， 机械工程， 2015， 硕士

【摘要】 增程式电动汽车是新能源汽车技术发展的重要方向。对于具备两个动力源的增程式电动汽车而言,能量分配策略对其燃油消耗有很大的影响,而工况又是能量分配策略的基础。本文以增程式电动城市客车为研究对象,构建基于实时工况辨识的在线最优能量分配策略。本文首先采用K-均值聚类方法对增程式电动城市客车的实际工况数据进行了聚类分析,形成了四种工况类;然后构建了可用于离线优化和在线工况辨识的四种特征工况,为后文基于工况辨识的在线能量分配策略奠定了基础。针对某种增程式电动城市客车的动力系统构型及参数,建立其能量优化问题的数学模型,分别采用动态规划、庞德里亚金极小值原理和等效燃油消耗最小策略等方法进行了求解,并证明了等效燃油消耗最小策略与庞德里亚金极小值原理的等价性。考虑到能量分配策略的在线应用,本文选取等效燃油消耗最小策略作为最优能量分配策略的核心算法。对于等效燃油消耗最小策略,合适的当量因子是获得在线最优控制的基础。本文首先分析了最优能量分配策略中不同当量因子控制参数对车辆油耗的影响,然后对所重构的四种特征工况进行了离线的最优能量分配,寻求其最优的控制参数,形成了最优控制参数知识库,以用于在线最优能量分配的计算。在线最优能量分配策略根据车辆的实时运行数据,以特征参数的欧几里德距离作为度量标准,对当前运行工况所属的特征工况类型进行辨识。然后,核心算法根据工况辨识结果,从知识库中调取与当前工况相适应的最优控制参数,求解当前最优的发动机和动力电池的功率输出,从而实现最优的燃油经济性。更多还原

【Abstract】 The Extended-Range Electric Vehicle(E-REV) is an important technology development direction of new energy vehicles. For E-REV, which equipped with two power sources, energy distribution strategy has a great influence on consumption economy, and driving cycle is the key factor of developing energy distribution strategy. Thus, this dissertation based on driving pattern recognition of real world driving data, proposes an online optimal energy distribution strategy for an Extended-Range Electric City Bus.K-means cluster analysis is used to classify the collected real world driving data into four clusters. And then four feature cycles are reconstructed, which is necessary for offline optimization and online driving pattern recognition.Based on an Extended-Range Electric City Bus, the mathematical model of energy distribution strategy optimization is built, and the theoretic methods, such as Dynamic Program(DP), Pontryagin’s Minimum Principle(PMP) and Equivalent Consumption Minimization Strategy(ECMS) are shown. Then the equivalency between ECMS and PMP is proven. Considering online application performance, ECMS is selected as core optimal algorithm of the proposed energy distribution strategy.For ECMS, the suitable equivalent factor is the basis of optimal online control. Thus, several simulation experiments are implemented to analyze the effect of the control parameters on the optimization results, which are used to adjust equivalent factor. Then the offline optimization of four feature cycles by ECMS are done. And the best control parameters are selected to form the knowledge base for online optimization calculation.Based on real-time driving data, the online optimal energy distribution strategy recognizes current driving cycle as one of the four feature cycles with Euclidean distance of feature variables as measure criterion. Then, the corresponding control parameters of the recognized feature cycle are applied to calculate the optimal power of engine and battery by ECMS to realize the best consumption economy.更多还原