题 目 电池管理系统状态估计算法开发摘要： 电动汽车不断增大的市场需求极大地促进了新能源技术的发展，电池管理系统是新能源车的一项非常重要技术研究。准确的实时的估计电池的荷电状态SOC（State of Charge）是非常重要的一个方面。本文的最终目标是实现 SOC 状态估计，主要的工作为：首先对状态估计的现状做分析，对现有的方法进行比较。接下来对电池的实验进行设计和介绍，并对实验数据进行简单的初步处理。然后对锂电池模型进行选型，对比各个模型的优劣，选定二阶 RC 等效电路模型，对该模型的数学表达式进行推导。紧接着对模型的参数进行识别，利用试验数据得出 SOC-OCV 关系，等效电路中电阻和电容随 SOC 变化的具体函数关系,利用 simulink 进 行 模 型 的 搭 建 并 验 证 。 最 后 采 用 以 安 时 积 分 为 基 础 的 EKF （Extended Kalman Filter）算法实现对 SOC 状态的估计，利用 matlab 程序对算法进行验证。关键词：锂离子动力电池，荷电状态，扩展卡尔曼滤波，电池模型， matlab/毕业设计（论文）外文摘要Title Development of state estimation algorithm for battery management system AbstractThe increasing market demand of electric vehicles has greatly promoted the development of new energy technology. Battery management system is a very important technology research of new energy vehicles. It is very important to estimate the SOC (state of charge) of battery in real time. The ultimate goal of this paper is to realize SOC state estimation. The main work is: first, analyze the status quo of state estimation and compare the existing methods. Next, the experiment of the battery is designed and introduced, and the experimental data is processed simply. Then, the model of lithium battery is selected, the second-order RC equivalent circuit model is selected, and the mathematical expression of the model is deduced. Then identify the parameters of the model, use the test data to get the soc-ocv relationship, the specific values of resistance an...
