城市地铁用LiB-EDLC混合储能系统功率/能量匹配特性及协同控制策略

项目名称： 城市地铁用LiB-EDLC混合储能系统功率/能量匹配特性及协同控制策略

项目编号： No.51477171

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 电工技术

项目作者： 冯自平

作者单位： 中国科学院广州能源研究所

项目金额： 80万元

中文摘要： 城市地铁制动时产生的回馈电能具有高功率、大能量、非线性等突出特点，由功率型锂电池（LiB）和超级电容器（EDLC）构成的混合储能系统可实现性能和经济性的完美平衡。本项目围绕混合储能系统如何优化匹配、如何优化控制这两个主要问题展开研究，主要研究内容包括：（1）通过系统仿真，全面研究地铁供电区间的能量流动模式，确定混合储能系统的功率/能量边界；（2）通过实验和仿真，研究电化学生热反应对锂电池功率/能量响应特性的影响规律，获得电池生热特性与可用容量及荷电状态（SOC）的相互关系；（3）建立多目标优化的LiB-EDLC优化匹配算法，在双DC/DC控制器的拓扑结构下，根据LiB和EDLC的不同功率/能量响应特性，建立协同控制的能量/功率分配策略。项目成果将为城市地铁混合储能系统的容量匹配和控制策略提供设计准则和方法指导。

中文关键词： 锂离子电池；超级电容器；混合储能系统；协调控制；多目标优化

英文摘要： The braking energy of city subway has the characteristics of high-power, high energy and nonlinear. The hybrid energy storage system(HESS), which is constituted by lithium ion Battery(LiB) and electric double layer capacitor(EDLC), could recycle this braking energy efficiently and economically. In connection with two main problems about HESS, which are how to match the capacities of LiB and EDLC Optimally and portion the energy opportunely, this project focus on the following mian contents: 1) By researching the energy flow mode of supply grid of metro through simulation, the range of power/energy is calculated to determine the boundary of HESS. 2) The law, that the electrochemical heat reaction of LiB impacts power/energy dynamic response characteristics is explored by experiments and simulations, and then the relationship between thermal characteristics and capacity as well as SOC is obtained. 3) The multi-objective function is established to solve the problem of the capacity optimization of LiB and EDLC. And then, according to the topology of double DC/DC converters and power/energy dynamic response characteristics of HESS, a control strategy is builded to apportion the power/energy to LiB and EDLC coordinately. The results of the project will provide guidance methods for city subway on the canpacity match and energy control strategy of HESS.

英文关键词： lithium battery;ultracapacitor;hybrid energy storage system;coordinated control;multi-objective optimization