燃料电池电动汽车动力系统的多模型切换控制研究

项目名称： 燃料电池电动汽车动力系统的多模型切换控制研究

项目编号： No.61203042

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 自动化学科

项目作者： 玄东吉

作者单位： 温州大学

项目金额： 25万元

中文摘要： 燃料电池电动汽车具有清洁无污染、效率高、供电时间长、噪音低等优点，已引起国内外专家的广泛关注。但燃料电池的动态响应具有一定的时滞，难以满足电动汽车实时变化的工况运行要求，需要配备小容量的辅助供电装置共同构成燃料电池电动汽车多能源动力系统。其动力系统是一个多输入多输出、强耦合的非线性系统，如何提高燃料电池的动态响应能力，并合理控制能量流，保证动力系统高效平稳运行是其中的难点与关键。为解决这个问题，本课题提出基于多模型的自适应预测分层控制结构。首先基于模糊聚类方法建立燃料电池、辅助供电装置等子系统的多神经网络模型，在此基础上分析系统的启动、加速、巡航行驶、减速制动等多个运行状态，在每个状态下针对各个典型的工作区分别建立多能源动力系统的能量流模型；然后为各个能量流模型设计底层模型预测控制策略，最后设计上层监督自适应控制器，通过对底层控制策略进行自适应切换优化能量流，以达到系统高效稳定运行的目的。

中文关键词： 质子交换膜燃料电池；燃料电池电动汽车；多能源动力系统；模糊控制；自适应控制

英文摘要： Fuel Cell Electric Vehicle has been highlighted by the experts in the world due to the merits such as no pollution, high efficiency, long power supply time, low noise and etc. But, the time-dependent real operating requirements of the electric vehicle can not be satisfied because the dynamic response of Fuel Cell has a certain time delay. So, a small additional power supply device should be included for constructing a multi-energy power system for Fuel Cell Electric Vehicle. The power system is a highly interacted non-linear system having multiple inputs and outputs. Improving the dynamic response ability of Fuel Cell, controlling energy flow reasonably, and stably operating the power system with high efficiency are the key technologies. For solving the problems, this project will recommend an adaptive prediction, separate layer control structure based on multi-model. At first, the multi neural network models of sub-system such as Fuel Cell, the additional power supply device and etc will be constructed based on fuzzy cluster method, and the operating status of the system such as starting, accelerating, cruising, decelerating, stopping and etc will be analyzed. For each typical operating region in each status, the energy flow model of multi-energy power system will be constructed; In the following step, a predic

英文关键词： Proton Exchange Membrane Fuel Cell；Fuel Cell Electric Vehicle；Multi-Energy Power System；Fuzzy Control；Adaptive Control