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题名	电动汽车磷酸铁锂电池管理系统的研究; 电动汽车磷酸铁锂电池管理系统的研究
作者	1李慧军,电工研究所
学位类别	硕士
答辩日期	2009-06-02
授予单位	中国科学院电工研究所
导师	1廖承林,电工研究所
关键词	电动汽车 磷酸铁锂电池 电池模型 SOC估算 电池管理系统 Electric Vehicle Lithium iron phosphate battery battery model SOC estimation battery management system
其他题名	电动汽车磷酸铁锂电池管理系统的研究
中文摘要	随着能源危机和环境问题的日益严重，世界各国政府及汽车企业越来越重视电动汽车的研发。蓄电池技术是制约电动汽车发展的重要因素，磷酸铁锂电池是一种极具发展前景的动力电池，研究磷酸铁锂电池及其管理系统（BMS）的实现是对于电动汽车的发展具有重要的意义。 本文以磷酸铁锂电池为研究对象，在相关试验基础上，从磷酸铁锂电池的充放电特性、温度特性和循环特性三个方面总结了磷酸铁锂电池的主要特点。在分析现有多种等效电路模型特点的基础上，根据磷酸铁锂电池的电压特性，建立了磷酸铁锂电池的等效电路模型。根据电池模型推导辨识参数的计算公式，采用改进的HPPC循环试验，分不同的SOC点和充放电方向，用最小二乘方法拟合得到电池模型的参数。利用获得的电池模型参数，在Matlab中建立相应的仿真模型。仿真结果表明，该等效电路模型具有较高的精度，能够准确模拟磷酸铁锂电池的动态特性。 本文基于扩展的Kalman滤波器原理研究磷酸铁锂电池SOC估算算法。基于建立的磷酸铁锂电池等效电路模型，考虑到影响电池容量的因素，最终确定基于EKF的SOC估算算法的计算流程。为了提高SOC估算的精度，对上述推导的基于EKF的SOC估算算法进行了三个方面的改进：增加电池内阻作为新的状态变量，对观测误差方差进行动态修正，引入控制收敛速度的增益因子。仿真结果表明，基于EKF的SOC估算算法方法可以很快收敛到SOC真值附近，可以解决安时积分法初值难以确定的问题；与改进前的SOC估算算法相比，采用改进的SOC估算方法可以有效的提高SOC估算的精度。 磷酸铁锂电池组电池管理系统采用分布式结构，基于LTC6802-1芯片设计了单体电池电压的集成测量方案。利用Matlab软件提供的RTW工具将SOC估算算法模型进行自动代码生成，集成到电池管理系统当中，对SOC估算算法模型进行硬件在环测试和台架试验。台架试验结果表明，磷酸铁锂电池管理系统能够较为准确的测量单体电池电压；与改进前的SOC估算算法相比，改进后的SOC估算算法模型的SOC估算精度得到明显提高；改进后的SOC估算算法模型在台架试验中同样能较为准确的估计磷酸铁锂电池的SOC。本文研究针对磷酸铁锂电池的SOC估算算法在电池管理系统中具有一定的实用价值。 As the energy crisis and environmental problems are increasingly serious, governments and auto enterprises around the world pay more attention to the research and development of Electric Vehicles. Battery technology has important influence for the development of Electric Vehicles. Lithium iron phosphate battery (LiFeO4 battery) is a very promising power battery. The research of LiFeO4 battery and the realization of its battery management system (BMS) are very significant for the development of Electric Vehicle. As LiFeO4 battery being for research object, based on the related tests on LiFeO4 battery, the characteristics of LiFeO4 battery are summarized from three aspects: charge and discharge feature, temperature feature and circulation feature. On the analysis of the characteristics of present equivalent circuit model, and based on the voltage characteristic of LiFeO4 battery, the equivalent circuit model of LiFeO4 battery is established. According to the battery model, the parameter identification formulas are derived. Through the modified HPPC circulation experiment, considering the different SOC and charge and discharge direction, the parameters of the battery model are obtained using least-square method. The corresponding simulation model is established in Matlab using the obtained battery model parameters. Simulation results show that the equivalent circuit model has high precision and can simulate the dynamic characteristics of LiFeO4 battery accurately. In this paper, the SOC estimation algorithm of LiFeO4 battery based on the Extended Kalman Filter (EKF) is studied. Based on the established equivalent circuit model of LiFeO4 battery, considering the factors influencing battery capacity, the calculation process of SOC estimation algorithm is determined based on the EKF iteration. In order to improve the accuracy of SOC estimation, three aspects of improvements have been applied in the SOC estimation method based on the EKF: adding the battery internal resistance to the state equation as a new state variable, correcting the observation error covariance dynamically and introducing a gain factor for controlling convergence speed. Simulation results show that the SOC estimation algorithm based on EKF can converge to the true value of SOC quickly and it can solve the difficulty in determining the initial value of the AH integration method; Compared to the original SOC estimation method, the improved SOC estimation method can improve the accuracy of SOC estimation effectively. Using distributed structure for BMS of LiFeO4 battery, an integrated measurement solution for cell voltage is designed based on LTC6802-1 chip. By RTW tools in Matlab, codes for the SOC estimation algorithm model are generated automatically and will be integrated into the BMS. Hardware-in-loop test and bench experiment are done for the SOC estimation algorithm model. Bench experiment results show that the LiFeO4 battery BMS can measure cell voltage accurately. Compared to the SOC estimation algorithm before improvements, the improved SOC estimation algorithm increases the accuracy of SOC estimation obviously and it can also give accurate SOC estimation value of LiFeO4 battery in bench experiments. The study of SOC estimation algorithm for LiFeO4 battery has practical value in BMS.
语种	中文
公开日期	2010-10-18
页码	101
分类号	TM1;TM921
内容类型	学位论文
源URL	[http://ir.iee.ac.cn/handle/311042/7143]
专题	电工研究所_其他部门_其他部门_硕士学位论文
推荐引用方式 GB/T 7714	1李慧军,电工研究所. 电动汽车磷酸铁锂电池管理系统的研究, 电动汽车磷酸铁锂电池管理系统的研究[D]. 中国科学院电工研究所. 2009.

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