锂电池自放电检测技术的研究与应用

发布时间：2018-07-05 05:11

本文选题：锂离子电池 + 自放电　；参考：《哈尔滨理工大学》2014年硕士论文

【摘要】：随着全球能源紧缺的加剧，新能源产业悄然兴起，具有较好应用前景的二次电池技术被争相研究。锂离子电池因其本身无污染、比能量高、循环寿命长等特性被广泛应用在各种仪表和电动汽车上作为能源系统。而锂电池自放电现象的存在不仅造成电池本身能量的损失，还会因各电池间自放电的不一致性导致锂电池组寿命减少，容量迅速衰减，引起电池管理系统(BMS)对电池荷电状态(SOC)的预测出现较大误差，电动车控制策略失效，致使电动车电池系统出现过放电的情况。因此，对电池自放电的快速测量具有重要意义。由于自放电发生在电池内部，现有测量手段不能对其直接检测，这就使电池的自放电检测难度变大。依据自放电定义，通过长时间的开路搁置可以实现对电池自放电的检测，不过时间周期过长，且不能体现快速性和实时性，同时常规方法也不能实现对自放电的精确测量。基于上述问题，本文以间接测量作为指导思想，设计自放电检测系统，以实现对电池自放电的快速、精确测量。系统采用数字控制技术，，以MSP430F149单片机为核心控制器，控制DAC1220D/A转换器输出高精度的电压，作为检测电路的基准电压，最终得到该电压时电池的自放电电流。并设计了开路搁置实验和自放电检测系统测量实验，通过实验结果对比，验证本系统具有对于自放电测量的可靠性、精确性和快速性。在此基础上本文进一步研究了自放电对电池SOC预测的影响，通过建立自放电流Map模型，实现了自放电电流对基于扩展卡尔曼滤波(EKF)算法的电池SOC估算初值的修正。设计模拟工况实验，验证了该修正方法可以提高EKF估算电池SOC的准确度，进而能够改善BMS预测电池SOC的精度。
[Abstract]:With the aggravation of the global energy shortage, the new energy industry rises quietly, and the secondary battery technology, which has good application prospect, has been studied. Li-ion batteries are widely used as energy systems in various instruments and electric vehicles because of their characteristics of non-pollution, high specific energy, long cycle life and so on. The existence of self-discharge phenomenon of lithium battery not only causes the energy loss of the battery itself, but also reduces the Lithium battery life and decreases the capacity because of the inconsistency of self-discharge between the batteries. The battery management system (BMS) has caused a large error in the prediction of the state of charge (SOC) of the battery and the failure of the control strategy of the electric vehicle (EV), which leads to the over-discharge of the battery system of the electric vehicle (EV). Therefore, the rapid measurement of self-discharge is of great significance. Since the self-discharge occurs inside the battery, the existing measurement methods can not directly detect it, which makes it more difficult to detect the self-discharge of the battery. According to the definition of self-discharge, the detection of self-discharge of battery can be realized by using open circuit for a long time, but the time period is too long, and it can not reflect the rapidity and real-time performance. At the same time, the conventional method can not realize the accurate measurement of self-discharge. Based on the above problems, this paper designs a self-discharge detection system based on indirect measurement, so as to realize the rapid and accurate measurement of battery self-discharge. The system adopts digital control technology and MSP430F149 single chip microcomputer as the core controller to control the DAC1220D / A converter output high-precision voltage, as the reference voltage of the detection circuit, and finally get the self-discharge current of the battery. The open circuit shelving experiment and the measurement experiment of self-discharge detection system are designed. By comparing the experimental results, it is verified that the system has the reliability, accuracy and rapidity for the self-discharge measurement. On this basis, the effect of self-discharge on SOC prediction is further studied in this paper. By establishing a map model of self-discharge current, the correction of self-discharge current to the initial value of SOC estimation based on extended Kalman filter (EKF) algorithm is realized. Simulation experiments were designed to verify that the modified method can improve the accuracy of EKF estimation of battery SOC and then improve the accuracy of BMS prediction of battery SOC.
【学位授予单位】：哈尔滨理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM912

【引证文献】