基于ADVISOR运行模型的锂电池SOC算法研究

发布时间：2018-10-15 15:20

【摘要】：从进入20世纪以来,人们越来越关注能源的消耗和环境污染的治理等问题,在此形式下,世界各国的主要汽车生产商都高度重视新型能源汽车,尤其是纯电动汽车的发展。电动汽车的主要能量是有动力电池提供的,那么合理使用动力电池的剩余电量关系到整车的性能好坏,可见准确的估算电池的荷电状态(State of Charge,SOC)是电动汽车发展领域的重点和难点之一。在论文开始时,首先介绍了锂电池的发展以及一些基本理论,同时介绍了现在发展比较成熟的电池SOC估算方法,并且确定使用中心差分卡尔曼滤波法(CDKF,Central Difference Kalman Filter)来估算电池SOC。研究现有的锂电池特性,总结使用卡尔曼滤波一类方法时等效电路模型的结构和优缺点,建立了Thevenin等效电路模型并进行改进。随后进行开路电压特性实验以及特性电压脉冲实验等来为Matlab电池模型提供数据支持,同时采用数据拟合的方法,确定各个模拟数据的准确性。随后介绍了扩展卡尔曼滤波算法(EKF,Extended Kalman Filter)的估算过程,分析中心差分卡尔曼滤波算法的原理和算法实现过程,针对CDKF在算法以及实际运用中误差影响的问题进行了简单的优化,得到扩展卡尔曼滤波算法和中心差分卡尔曼滤波算法中的系统状态、观测方程和推导的各矩阵式。结合前面实验得到的电池仿真模型,通过Matlab/Simulink搭建扩展卡尔曼滤波器和中心差分卡尔曼滤波器来对估算电池的SOC。在论文的最终阶段,通过仿真软件ADVISOR2002建立纯电动汽车整车模型,获取不同实际工况下的锂电池的电流等信息。验证基于Matlab/Simulink设计的中心差分卡尔曼滤波器和扩展卡尔曼滤波器对动力锂电池电荷状态的估计的有效性。从实验成果中我们可以看出CDKF在实际电池电荷状态估算中的优势。
[Abstract]:Since entering the 20th century, people pay more and more attention to the problems of energy consumption and environmental pollution control. In this form, the major automobile manufacturers all over the world attach great importance to the development of new energy vehicles, especially pure electric vehicles. The main energy of the electric vehicle is provided by the power battery, so the reasonable use of the remaining power of the power battery is related to the performance of the whole vehicle. Therefore, it is one of the key and difficult points in the field of electric vehicle to accurately estimate the charged state of battery (State of Charge,SOC. At the beginning of the thesis, the development of lithium battery and some basic theories are introduced, and the current developed SOC estimation method of battery is introduced, and the central differential Kalman filter (CDKF,Central Difference Kalman Filter) is used to estimate SOC. of battery. This paper studies the characteristics of lithium battery, summarizes the structure, advantages and disadvantages of equivalent circuit model using Kalman filter, and establishes and improves the equivalent circuit model of Thevenin. Then the open circuit voltage characteristic experiment and the characteristic voltage pulse experiment are carried out to provide the data support for the Matlab battery model. At the same time, the accuracy of each simulation data is determined by the method of data fitting. Then it introduces the estimation process of extended Kalman filter (EKF,Extended Kalman Filter), analyzes the principle and realization process of the central differential Kalman filter algorithm, and optimizes the error effect of CDKF in algorithm and practice. The system states, observation equations and derived matrices of the extended Kalman filtering algorithm and the central differential Kalman filter algorithm are obtained. Combined with the battery simulation model obtained from the previous experiments, the extended Kalman filter and the central differential Kalman filter are constructed by Matlab/Simulink to estimate the SOC. of the battery. In the final stage of the thesis, the simulation software ADVISOR2002 is used to establish the vehicle model of pure electric vehicle, and to obtain the current information of lithium battery under different operating conditions. The effectiveness of central differential Kalman filter and extended Kalman filter based on Matlab/Simulink for charge state estimation of power lithium battery is verified. From the experimental results, we can see the advantages of CDKF in the estimation of actual cell charge state.
【学位授予单位】：山东大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：U469.72;TN713

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