【作者】 王闯；

【导师】 冯健；

【作者基本信息】 东北大学 ， 电力电子与电力传动， 2014， 硕士

【摘要】 近些年来电动车的发展受到人们的关注,然而,由于电动车电池自身的不稳定性,关于剩余电量估算及充电状态下串联电池组一致性问题还没有得到妥善的解决。因此,本论文将针对上述问题进行如下的研究。首先,针对锂电池组进行建模的问题,本文构建具有温度标称的动力电池模型,由根据电池电荷量变化和电流平衡关系的充放电模型KiBaM(Kinetic Battery Model)和带有易受温度影响电阻的二阶充放电模型组成。由电荷量控制电压受控源,将电流、电池端电压、剩余电量、温度有效的结合在一起。数学模型往往依据的是经验,各种现象之间没有直接的关系；电化学模型却因较强的计算和特定的电池输入而不具有一般性；等效电路模型能够在一定程度上体现电池的特性,但是只能依据电流、电压。其次,将锂电池的工作状态划分为静置和充放电两种情况,根据不同的电池状态采用与之对应的SOC估算方法。利用改进的扩展卡尔曼滤波法(EKF)去估算锂电池的剩余电量,通过一阶泰勒公式展开,将电池的非线性问题转成线性化方法处理,将剩余电量的状态方程进行恰当的温度补偿。电流、温度作为输入量,电压作为输出量,预测的电池剩余电量作为状态变量来估算。仿真结果证明电池的电压、电流、温度主要影响电池的剩余电荷量。而且基于EKF估算SOC的误差会减小,精度会提高。最后,针对串联电池组的电量一致性问题,运用平均值和差值比较法的控制策略去度量电池的不均衡性并利用改进的二级双向非耗散型分流均衡系统,来均衡串联电池组的一致性,通过仿真较好地实现了串联电池组的均衡一致性。基于电池管理系统(BMS)硬件平台进行数据采集并且实现SOC估算算法的软件流程图,完成对串联电池组需求数据的采集及显示。更多还原

【Abstract】 For the recent years, progress has been made on the development of electric vehicles and this has drawn much attention. However, due to the instability of the battery, the estimation of the state of charge (SOC) and the equilibrium of series batteries in the charging state still require further research. Thus, to solve these problems, this thesis does the following research.Firstly, for the lithium-ion battery pack, a power battery model with rated temperature is constructed. It is composed of two models. One is the KiBaM (Kinetic Battery Model) which is based on relations of the change of the battery’s electric charge and the current balance. The other one is the second order battery model whose resistance can be easily influenced by temperature. In this model, controlled voltage source is the electric charge. Besides, current, voltage at the terminal of the battery, SOC, and temperature are effectively combined together. Mathematical model is generally based on experience and there is no direct relation among different phenomena; electrochemistry model is not universal for its strong computation and specified battery input; equivalent circuit model can show the properties of battery, but this is only possible on the basis of current and voltage.Secondly, the state of the lithium-ion battery is classified into two, namely the static state and the charging/discharging state. For the two states, different methods are adopted to estimate the SOC of the battery. The extended Karman Filtering (EKF) is improved for the estimation. Then, it is expanded through first order formula, and the non-linear questions are dealt with by changing them into linear ones. Besides, the equation of the SOC is revised properly with temperature compensation. Current and temperature are the inputs, and voltage is the output. The SOC regarded as state variable in its estimation. The simulation results show that the SOC is mainly influenced by the battery’s voltage, current, and temperature; the estimation of the SOC which is based on the EKF is more accurate.Lastly, the equilibrium of the series battery pack is explored. To begin with, the lack of uniformity of the batteries is measured with the control strategy in the approach of comparing the mean and difference value. Then, this thesis adopts the improved second order bidirectional non-dissipative diffluence equilibrium system in achieving equilibrium of series battery pack. In the simulation experiment, it is achieved successfully. Besides, on the basis of the battery management system (BMS), data collection is accomplished displayed, and software flow pattern of the algorithm for the SOC is presented.更多还原