锂离子电池荷电状态研究

发布时间：2018-05-30 06:09

本文选题：锂离子电池 + 等效电路模型　；参考：《上海海洋大学》2017年硕士论文

【摘要】：近年来,随着经济发展,环境污染的日益加剧,传统的化石能源已不能满足人类生产生活的需要。为此,许多交通工具如火车,汽车等开始使用电力作为能源。电力能源可以储存在多种介质中,日产生活中应用最广泛的就是电池。电池的荷电状态(SOC)估算是对电池应用研究的一个基础,电池在使用中的深度充放电,会大大减少其使用年限,精确的SOC估计可以避免这种情况;剩余电量的准确显示,还能帮助汽车控制系统计算可行驶里程,驾驶者也可以更好规划行驶路线。以上优点说明SOC的研究具有重要的意义。本文从锂离子电池的化学结构入手,对锂电池的原理进行了分析,通过阶梯充放电实验研究了锂电池的充放电电压特性,分析了实验环境温度、充放电倍率对锂电池性能的改变。本文针对现有的四种经典锂离子电池电路模型,即内阻,Thevenin,PNGV和GNL模型所存在的不足,提出本文所采用的二阶RC等效电路模型,并推导出了此二阶模型的状态空间模型。接着,基于恒流放电实验数据用五次多项式极大逼近了锂电池的OCV-SOC关系。然后基于实验数据,采用参数拟合的方法和具有遗忘因子的RLS算法对锂离子电池二阶模型的参数进行了离线和在线辨识。MATLAB仿真实验研究表明,本文所建立的二阶等效电路模型能较好地体现锂电池的端电压特性,所建模型精度较高。从而为锂离子电池的SOC估计奠定基础。锂电池系统是一个复杂的系统,考虑到扩展卡尔曼滤波(EKF)算法在解决非线性系统滤波问题方面的优势,本文采用EKF对锂电池的SOC进行预测。首先基于锂电池的二阶等效电路模型,推导出了其线性离散化状态空间模型,确定了锂电池的SOC、极化电压、噪声方差阵等的初始值后,采用EKF逐次对恒流放电状态、周期性脉冲放电状态、FUDS状态和BJDST状态下锂电池的SOC进行了估计。并将EKF在各工况下的SOC估计结果与实际结果实施了比较,仿真实验得出结论,对于FUDS和BJDST工况,虽然在初期阶段SOC的评估误差较大,但最终EKF都可以顺利地预测锂电池的SOC改变。这说明本文采用EKF对锂电池的SOC进行预估是完全可行的,EKF在锂电池的SOC评估方面具有良好的鲁棒性。
[Abstract]:In recent years, with the development of economy and environmental pollution, the traditional fossil energy can not meet the needs of human production and life. To this end, many vehicles such as trains, cars and so on began to use electricity as an energy source. Electric energy can be stored in a variety of media, Nissan life the most widely used is the battery. The state of charge (SOC) estimation of the battery is a basis for the study of battery application. The depth charge and discharge of the battery in use will greatly reduce its service life, which can be avoided by accurate SOC estimation. It also helps car control systems calculate mileage and better route planning. The above advantages show that the study of SOC is of great significance. Based on the chemical structure of lithium ion battery, the principle of lithium battery is analyzed in this paper. The charge-discharge voltage characteristic of lithium battery is studied by step charge-discharge experiment, and the temperature of experimental environment is analyzed. The change of charge / discharge ratio on the performance of lithium battery. In this paper, the second order RC equivalent circuit model is proposed and the state space model of this second order model is derived, aiming at the shortcomings of the existing four classical lithium ion battery circuit models, namely, the internal resistance Theveninn PNGV model and the GNL model. Then, based on the constant current discharge experimental data, the OCV-SOC relation of lithium battery is approximated by the fifth order polynomial. Then, based on the experimental data, the parameter fitting method and the RLS algorithm with forgetting factor are used to study the off-line and on-line identification of the parameters of the second-order model of Li-ion battery. MATLAB simulation results show that, The second order equivalent circuit model established in this paper can well reflect the terminal voltage characteristics of lithium battery, and the precision of the model is high. Thus, it lays a foundation for the SOC estimation of lithium ion batteries. Lithium battery system is a complex system. Considering the advantage of extended Kalman filter (EKF) algorithm in solving nonlinear system filtering problem, EKF is used to predict the SOC of lithium battery. Firstly, based on the second order equivalent circuit model of lithium battery, the linear discrete state space model is derived, and the initial values of SOC, polarization voltage and noise variance matrix of lithium battery are determined, then the constant current discharge state of lithium battery is analyzed by EKF step by step. The SOC of lithium battery under periodic pulse discharge state and BJDST state are estimated. The SOC estimation results of EKF under various operating conditions are compared with the actual results. The simulation results show that for the FUDS and BJDST conditions, the evaluation error of SOC in the initial stage is large. But in the end, EKF can predict the change of SOC of lithium battery. This shows that it is feasible to predict the SOC of lithium battery by using EKF in this paper. EKF has good robustness in evaluating SOC of lithium battery.
【学位授予单位】：上海海洋大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM912

【参考文献】