基于粒子滤波的锂离子电池SOC预测方法研究解析.doc

基于研究 摘要 (SOC)是锂离子电池管理系统中最重要的参数之一，通过锂离子电池SOC可以判断锂离子电池组中单体锂离子电池之间的性能差异，避免锂离子电池的过度充电过度放电，因此及时和准确地对锂离子电池SOC进行估计具有非常重要的现实意义。但是由于锂离子电池SOC估算受锂离子电池工作状态、电流大小、温度高低等诸多因素的影响，想要计算出准确的SOC值是很困难的，距离应用于真正的锂离子电池设备还有一定的距离。 由于锂离子电池是一个复杂的非线性系统，而粒子滤波算法在求解非线性问题方面具有一定的优越性，因此本文研究了利用粒子滤波算法动态估计锂离子电池的SOC值，将粒子滤波算法和锂离子电池模型相结合，利用实测数据进行了锂离子电池SOC的估计实验。结果表明，粒子滤波算法能够对锂离子电池的SOC做出较准确的估计，该算法具有很好的适用性。 本文给出了用粒子滤波方法预测锂离子电池剩余有用寿命的详细实现方式，用集总参数模型来计算锂离子电池所有的动态特性，包括：非线性开环电压、电流，温度，循环次数和与时间不相关的储能容量。锂离子电池内部的反应是此模型的基础，系统中静态噪声经处理后可以对剩余有用寿命做估计。仿真的结果表明发展的预测系统能很好地预测锂离子电池的剩余有用寿命。 关键词；SOC Research of Management Information System of Power Plant Based on UML Abstract Lithium ion battery state of charge is one of the most important management systems in lithium ion battery parameters. The lithium ion battery state of charge can determine the performance differences between the lithium ion battery monomer lithium ion batteries. In order to avoid over charging and excessive discharge of lithium ion battery, Therefore, timely and accurately to the lithium ion battery state-of-charge estimation has a very important practical significance。But because of the influence of lithium ion battery state-of-charge estimation of many factors such as working state, lithium ion battery current and temperature etc. we want to calculate the exact state-of-charge values is very difficult. Application of distance from real lithium ion battery equipment there is a certain distance. Due to power battery is a complex nonlinear system, and particle filter (PF) has advantages in solving nonlinear problems, so this paper proposes the usage of PF in solving the battery SOC estimation problem. Making use of the measured battery data, we do the experiment of battery SOC estimation based on the lithium-ion battery model. The experiment result shows that particle filter algorithm can do an accurate prediction on battery SOC, and the prediction algorithm has better applicability Particle filter method are presented to predict the remaining useful life o