锂离子电池SOC估算研究与实现

发布时间：2018-06-02 02:27

本文选题：锂离子电池 + 荷电状态　；参考：《武汉纺织大学》2017年硕士论文

【摘要】：随着世界经济的高速发展,有限化石能源的快速消耗和日益严峻的污染成为当代亟待解决的两大世界难题。电动汽车因其环保性能和灵活的动力配置等优点逐渐形成一种新的发展潮流。对电动汽车而言,其动力主要来自电池。如何在保证使用安全的情况下最大功效的发挥电池的性能,是电动汽车电池管理系统(BMS)研究的主要目的。而电池的荷电状态(SOC)估计是BMS的众多功能属性中最重要的功能之一。SOC不仅能反应电池的剩余电量,还是电池均衡控制和能量管理的基础。精确的SOC估算对BMS具有非常重要的意义。本文以锂电池为研究对象,对电池的SOC估算算法方面进行了以下研究。1.研究基于扩展卡尔曼滤波和粒子滤波算法的SOC估算,综合分析了影响SOC估算的各种因素,建立了Thevenin一阶RC模型和复合电池模型,通过实验分析确定了模型参数获取的方法。在安时计量法的基础上,分别将扩展卡尔曼滤波(EKF)算法和粒子滤波(PF)算法结合电池模型建立了对应的空间状态方程,实现了这两种算法的SOC精确估算。2.搭建了SOC在线估算平台。用M7568215型电池为测试对象,AD7280、霍尔传感器和MS9S12XS128等外围器件搭建的硬件电路来采集电池信息(温度,电压,电流),用LabVIEW编写的上位机通过CAN总线与MCU进行数据通信,实现对采样数据进行实时监控和存储的功能。3.设计了一种基于EKF的SOC估算方法,并在所搭建的测试平台对电池进行模拟NEDC工步放电实验,用PF对数据进行离线处理。结果表明,相较于安时积分法,EKF和PF能有效提高SOC的估算精度。并结合实验结果,对产生误差的原因进行了较细致的分析。
[Abstract]:With the rapid development of the world economy, the fast consumption of limited fossil energy and the increasingly severe pollution have become two world problems to be solved. Electric vehicle (EV) has gradually formed a new trend of development because of its environmental protection performance and flexible power allocation. For electric vehicles, the power comes mainly from batteries. How to make the best use of the battery performance under the condition of safety is the main purpose of the battery management system of electric vehicle (BMS). The state of charge (BMS) estimation is one of the most important functions of BMS. SoC can not only reflect the residual power of the battery, but also the basis of battery balance control and energy management. Accurate SOC estimation is very important to BMS. In this paper, the lithium battery as the research object, the battery SOC estimation algorithm for the following research. 1. The SOC estimation based on extended Kalman filter and particle filter algorithm is studied. The factors influencing the estimation of SOC are analyzed synthetically. The first order RC model and composite battery model of Thevenin are established. The method of obtaining model parameters is determined by experimental analysis. Based on the amperometric method, the extended Kalman filter (EKF) algorithm and the particle filter (PF) algorithm are combined with the battery model to establish the corresponding spatial state equation, and the SOC estimation of the two algorithms is realized. SOC online estimation platform is built. Using M7568215 battery as test object, the hardware circuit of Hall sensor and MS9S12XS128 peripheral devices is used to collect battery information (temperature, voltage, current, etc.) the upper computer written by LabVIEW communicates with MCU through CAN bus. Realize the function of real-time monitoring and storage of sampled data. 3. A SOC estimation method based on EKF was designed, and the battery was simulated by NEDC step discharge experiment on the test platform, and the data were processed offline with PF. The results show that EKF and PF can effectively improve the accuracy of SOC. Combined with the experimental results, the causes of errors are analyzed in detail.
【学位授予单位】：武汉纺织大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM912

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