锂电池SOC估算方法及串联电池组一致性研究

发布时间：2019-06-05 15:25

【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。
[Abstract]:In recent years, the development of electric vehicles has attracted people's attention. however, due to the instability of electric vehicle batteries themselves, the problem of residual power estimation and consistency of series batteries in charging state has not been properly solved. Therefore, this paper will focus on the above issues as follows. First of all, aiming at the problem of modeling lithium battery pack, this paper constructs a power battery model with temperature nominal. It is composed of a charge-discharge model KiBaM (Kinetic Battery Model) and a second-order charge-discharge model with resistance easily affected by temperature according to the relationship between battery charge and current balance. The voltage controlled source is controlled by the charge quantity, and the current, battery terminal voltage, residual power and temperature are effectively combined. The mathematical model is often based on experience, and there is no direct relationship between various phenomena, but the electrochemical model is not general because of strong calculation and specific battery input. The equivalent circuit model can reflect the characteristics of the battery to a certain extent, but can only be based on current and voltage. Secondly, the working state of lithium battery is divided into static and charge-discharge cases, and the corresponding SOC estimation method is adopted according to different battery states. The improved extended Kalman filter (EKF) is used to estimate the residual power of lithium battery. The nonlinear problem of lithium battery is transformed into a linear method through the expansion of the first order Taylor formula. The equation of state of the remaining electricity is compensated for the temperature properly. Current, temperature as input, voltage as output, predicted battery residual power as state variables to estimate. The simulation results show that the voltage, current and temperature of the battery mainly affect the residual charge of the battery. Moreover, the error of SOC estimation based on EKF will be reduced and the accuracy will be improved. Finally, in order to solve the problem of power consistency of series battery pack, the control strategy of average and difference comparison method is used to measure the imbalance of battery and the improved two-way non-dissipative shunt equilibrium system is used. To equalize the consistency of series battery pack, the equilibrium consistency of series battery pack is realized by simulation. Based on the (BMS) hardware platform of battery management system, the software flow chart of SOC estimation algorithm is realized, and the requirement data of series battery pack is collected and displayed.
【学位授予单位】：东北大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM912

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