【作者】 陈勇军；

【导师】 孙金玮；

【作者基本信息】 哈尔滨工业大学 ， 仪器科学与技术， 2011， 硕士

【摘要】 近年来,随着能源危机、环境问题的日益突出,磷酸铁锂电池以其自身的安全性、无污染等众多性能优点逐渐受到广泛的关注,在汽车,后备电源等领域得到了广泛的应用。为深入对电池的研究,建立符合电池特性的准确模型对于电池管理系统及工程开发有着十分重要的意义,而作为电动汽车的动力电池而言,准确的荷电状态(SOC)的估计对提高电池使用寿命和整车性能都具有重要意义。为了建立准确的电池模型,本论文进行了大量的充放电试验来对磷酸铁锂电池的性能特性进行研究,在不同温度、不同放电倍率下,不同的荷电状态下进行HPPC复合脉冲充放电测试,获取充放电双方向上的参数。结合现有等效电路模型进行一阶和二阶模型的改进。通过在三种工况下与实际工况响应值进行对比验证,建立的两种模型都具有较高的精度,其中在复杂工况下一阶修正模型的平均误差为0.025V,二阶修正模型的平均误差为0.0103V,为后文扩展卡尔曼滤波算法估算SOC提供了准确的模型。作为电动汽车发展的核心技术之一的动力电池的荷电状态(SOC)估算,是电动汽车产业化、实用化的关键。本文在前文建立的模型基础上,采用扩展卡尔曼滤波算法对电池的剩余电量进行估算。通过在四种不同工况下对卡尔曼滤波算法进行对比,一阶模型的卡尔曼滤波算法的精度在恒工况下要略高于二阶模型算法的精度,二阶模型算法在变工况下有更高的精度。通过在复杂工况下对二阶模型的收敛性验证发现,在初始误差为20%时,电池经过9分钟的运行后,算法估算的精度收敛到5%左右。初始误差为50%时,算法经过40分钟的调整后,算法的估计精度收敛到10%以内。文中建立的修正模型能较好的反映电池动态性能,基本能满足实际的仿真需求和精度;电池的荷电状态估算获得了较高的精度,有良好的动态适应性,并且具有较快的收敛速度。更多还原

【Abstract】 In recent years, the energy crisis and the environmental problems have become more prominent, LiFePO4 batteries to its own security, no pollution and many other performance characteristics are gradually to be concerned, and have a wide range of applications in the car and backup power supply etc .to be the further study of the battery, setting up accurate model of characteristic for batteries battery management system in engineering development has a very important significance, as the electric car in the power battery, accurate state-of-charge (SOC) estimates to improve battery life and vehicle performance is very important.In order to establish the accurate battery model, this paper made a lot of experiment of charging and discharging LiFePO4 batteries to test the battery performance characteristics, based on the existing first and second order model, the model was improved through the HPPC composite pulse condition acquire the parameters of different temperature、different discharge rate and different state of charge. compared the two models in the three conditions with dynamic response value of the voltage and gained a specific test precision, in the complex conditions, the first-order correction in the model has the average error of 0.025V, the average second-order error correction model is 0.0103V, and later provides accurate models for extended kalman filter algorithm to estimate SOC.The state of charge (SOC) estimation as power battery is the one of core technology of the electric car development, it’s the key of utility and industrialization. In this paper, basis for setting up the model, battery remaining power are calculated by extended kalman filter. Through compared in varying conditions based on the setting model, First-order model kalman filter algorithm accuracy is better than the second-order in the constant conditions. But in the changing conditions, the second-order models have higher accuracy. Complex conditions through the convergence of second-order model validation found that the initial error of 20%, the battery after a nine-minute run, the algorithm converges to the estimated accuracy of 5% around; the initial error of 50%, After 40 minutes of adjustment algorithm, the algorithm converges to the estimated accuracy of 10% range.The improved model of this paper can better reflect the dynamic performance of battery, and can meet the practical needs of the simulation, the state of charge estimation of battery also have the high accuracy, good dynamic adaptability, and has a faster convergence speed.更多还原