基于无迹卡尔曼滤波的磷酸铁锂电池SOC估计算法

发布时间：2018-05-12 05:44

本文选题：磷酸铁锂电池 + 荷电状态　；参考：《清华大学》2014年硕士论文

【摘要】：“安全、节能、环保”是汽车技术发展的永恒主题。在全球能源危机及环保问题日益严峻的今天,以电动汽车为代表的新能源汽车成为未来汽车的发展趋势。作为电动汽车的关键技术之一,电池的安全高效使用有赖于对电池状态的准确估计。电池的状态可分为两类,一类可直接测定,如电压、电流、温度等;另一类不能直接测定,只能通过一定的方法进行估计,如荷电状态SOC、健康状态SOH等。这些状态量在电池的使用过程中至关重要。SOC的估计方法有很多,目前大多数方法都基于安时积分的基本原理。安时积分法虽然简便易行,但存在两个重要的问题:(1)不能估计SOC初值;(2)电流测量的不准确会引起累积误差。为解决这些问题,可以采用卡尔曼滤波的方法。由于电池是高度非线性的系统,采用无迹卡尔曼滤波(UKF)能得到比扩展卡尔曼滤波(EKF)更好的效果。电池在长期使用的过程中会发生老化。老化的电池会出现容量衰减的现象。因此在利用安时积分原理进行SOC估计时,必须考虑老化的影响。本文以磷酸铁锂电池单体为研究对象,首先比较了各类等效电路模型,综合考虑模型的准确性和复杂性,选取Thevenin模型描述电池的动态行为。通过HPPC测试,对电池模型进行参数辨识,并利用北京公交车工况进行验证。结果表明,Thevenin模型能较好地描述电池的动态行为,并且其结构简单,参数辨识方便。在恒温条件下对电池进行循环老化,得到电池在老化过程中的容量衰减规律。然后在Matlab环境下,基于无迹卡尔曼滤波(UKF)算法,实现对电池SOC的估计。利用北京公交车工况验证算法在单次工作循环中的有效性,通过人为制造初值误差及输入噪声的方法验证算法的鲁棒性及抗噪能力。仿真结果表明,该算法在初值估计不准确及输入中包含噪声时,仍能逐渐收敛至SOC真值附近并进行跟随,在工况试验的中后期,估计误差不超过5%。之后结合容量衰减模型对不同老化阶段电池的SOC进行估计,实现电池整个老化过程中稳定误差不超过5%的精度结果。
[Abstract]:Safety, energy saving, environmental protection is the eternal theme of automobile technology development. Nowadays, with the global energy crisis and environmental protection problem becoming more and more serious, the new energy vehicle, represented by electric vehicle, has become the development trend of the future automobile. As one of the key technologies of electric vehicles, the safe and efficient use of batteries depends on the accurate estimation of battery state. The state of the battery can be divided into two categories, one can be directly measured, such as voltage, current, temperature, etc. The other can not be measured directly, but can only be estimated by certain methods, such as charged state SOC, healthy state SOH and so on. There are many methods to estimate the state variables in the battery. Most of the methods are based on the basic principle of the amperage integral. Although the Anshi integration method is simple and feasible, there are two important questions: 1) it is impossible to estimate the initial value of SOC / 2) the inaccuracy of current measurement will cause cumulative error. In order to solve these problems, Kalman filter can be used. Because the battery is a highly nonlinear system, unscented Kalman filter (UKF) is better than extended Kalman filter (EKF). Batteries will age during long-term use. Aging batteries will have capacity decay. Therefore, the effect of aging must be taken into account in the SOC estimation using the principle of Anchor integral. In this paper, the monomer of lithium iron phosphate battery is taken as the research object. Firstly, various equivalent circuit models are compared. Considering the accuracy and complexity of the model, the Thevenin model is selected to describe the dynamic behavior of the battery. The parameters of the battery model are identified by HPPC test, and verified by Beijing bus operating condition. The results show that the Thevenin model can well describe the dynamic behavior of the battery, and its structure is simple and the parameter identification is convenient. Under the condition of constant temperature, the capacity attenuation of the battery during the aging process is obtained by cyclic aging. Then, based on the unscented Kalman filter (KF) algorithm, the battery SOC estimation is realized in Matlab environment. The effectiveness of the algorithm in a single working cycle is verified by using the Beijing bus working condition. The robustness and anti-noise ability of the algorithm are verified by the method of artificial initial value error and input noise. The simulation results show that the algorithm can gradually converge to the true value of SOC and follow when the initial value is inaccurate and the noise is included in the input. The estimation error is less than 5% in the middle and late stage of the test. Then the SOC of the battery in different aging stages is estimated with the capacity attenuation model, and the accuracy of the stability error is less than 5% during the whole aging process of the battery.
【学位授予单位】：清华大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM912

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