基于高阶PNGV模型的动力电池SOC估计

发布时间：2018-01-09 04:23

本文关键词：基于高阶PNGV模型的动力电池SOC估计　出处：《南京理工大学》2014年硕士论文　论文类型：学位论文

【摘要】：动力电池的SOC (state of charge)表征的是电池的剩余容量,它不仅直观地反应了电池组的性能,如电池组的一致性、可用容量,更是电动汽车动力性能重要预测依据如汽车的续航里程、爬坡性能。为了优化电池组的能量利用和保障电动汽车行驶安全,精确的SOC估计成为电池管理系统的关键技术。本文以锂离子电池为研究对象,对电池的SOC估计进行了研究并重点围绕以下两个方面进行展开：首先是电池模型的研究,建立一个精确的电池模型是获得精确的SOC估计的必要前提。通过电池的充放电实验分析了电池的基本特性并在此基础上考虑到实际管理系统中SOC估计对电池模型的要求建立了高阶PNGV模型。接着根据最小二乘原理进行了参数辨识主要包括Uoc和RC参数,最后对电池进行了模型验证。其次是SOC估计算法的研究,针对非线性高阶PNGV模型,文中依次运用了扩展卡尔曼滤波、无迹卡尔曼滤波对进行了电池的SOC估计,由仿真实验结果可两种方法各有自己的优势,其中UKF以降底估计效率为代价在一定程度上提高了SOC的估计精度。为了在保证UKF估算精度的同时提高估算效率,文中提出了基于RB分解的无迹卡尔曼滤波(RBUKF),并实现了基于RBUKF的SOC估算。最后的仿真实验结果验证了RBUKF算法有效性。
[Abstract]:Battery SOC (state of charge) is the characterization of the remaining battery capacity, it not only directly reflects the performance of the batteries, such as consistency, battery available capacity, it is important to predict the dynamic performance of electric vehicles on the basis of such as car mileage, climbing performance. In order to optimize the battery energy utilization and security of electric vehicle driving safety, accurate SOC estimation is a key technology of the battery management system. In this paper, the lithium ion battery as the research object, the battery SOC estimation was studied and the focus revolves around the following two aspects:
The first is the research of battery model, establish a precise model of the battery is to obtain the necessary premise of accurate SOC estimation. Through the battery charge discharge experiment analysis of the basic characteristics of the battery and on the basis of considering the actual management system in SOC estimation of battery model requirements to establish a high order PNGV model and parameter identification. The main parameters include Uoc and RC according to the principle of least squares, at the end of the battery model is validated.
The second is the estimation of SOC, aiming at the nonlinear high order PNGV model, in this paper, followed by the use of extended Calman filter, unscented Calman filter to the battery SOC estimation, the simulation results of two methods have their own advantages, which base on UKF cost estimation efficiency is improved to a certain degree. The estimation accuracy of SOC. In order to ensure the UKF and enhance the estimation accuracy of the estimation efficiency, this paper proposes the RB decomposition based on Unscented filter Calman (RBUKF), and realizes the estimation of RBUKF based on SOC. The simulation results verify the validity of RBUKF algorithm.

【学位授予单位】：南京理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM912

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