考虑温度影响的磷酸铁锂电池建模及SOC估算研究

发布时间：2018-04-03 00:26

本文选题：磷酸铁锂蓄电池　切入点：荷电状态　出处：《合肥工业大学》2017年硕士论文

【摘要】：磷酸铁锂(LiFePO_4)蓄电池具有体积小、使用寿命长、可进行大电流放电、免维护等优势,已广泛应用于电动车、通讯工具、储能系统等领域。目前LiFePO_4电池作为纯电动汽车的动力来源,最常被使用在该类场合,而作为电动汽车的核心部件,动力电池的性能对整车性能产生重要影响。动力电池的荷电状态(State of Charge,SOC)是电动汽车的重要参数之一,反映了其剩余电量的多少。由于在电动汽车在行驶过程中电池的环境温度对电池的影响很大,进而影响到了电池SOC估计的精确度。因此,高效率地管理这些蓄电池,准确预估实际运行中的电池SOC,能更有效地进行电池和整车管理,对预测电动车的剩余行驶里程以及电池组的使用和维护有着重要的意义。论文首先介绍了LiFePO_4电池在纯电动汽车中的应用背景,及其电化学原理和工作特性,分析了影响电池性能的多种因素。然后通过分析对比LiFePO_4电池的常见的传统电池模型,选择本文使用的电池模型。针对LiFePO_4电池的SOC估计受环境温度影响较大这一现象,通过分析比对,基于Nernst电化学方程提出了一种新型的电池建模方法,将实验数据应用于统计学方法试验设计(Design of Experiment,DOE),通过测量较少的数据得到较为精确的电池内阻模型,模型中的其他参数能够用连续变化的温度、电池不同时刻SOC进行拟合,从而实现整个电池模型的实时估计。最后,介绍了扩展卡尔曼滤波(Extended Kalman Filter,EKF)算法,分析了基于改进后的Nernst电池模型的LiFePO_4电池的状态空间方程,在实验室条件下进行不同温度、不同工况的充放电实验,并在MATLAB/Simulink模块里搭建了LiFePO_4电池SOC估计的EKF模型进行实验数据的仿真分析,实现对LiFePO_4电池SOC的动态估计,比较改进后的电池模型与传统电池模型在SOC估计时的误差大小,结果表明改进的Nernst电池模型可以获得较高的SOC估计精度。
[Abstract]:LiFePO4) battery has been widely used in electric vehicles, communication tools, energy storage systems and other fields because of its advantages such as small size, long service life, high current discharge, no maintenance and so on.At present, as the power source of pure electric vehicle, LiFePO_4 battery is most often used in this kind of situation. As the core component of electric vehicle, the performance of power battery has an important impact on the performance of the whole vehicle.The state of charge state of electric vehicle (SOC) is one of the important parameters of electric vehicle, which reflects the amount of its remaining power.Because the ambient temperature of the battery has a great influence on the battery during the driving process of the electric vehicle, the accuracy of the battery SOC estimation is affected.Therefore, it is of great significance to manage these batteries efficiently and accurately predict the actual operation of SOCs, which can effectively manage the battery and the whole vehicle. It is of great significance to predict the remaining mileage of electric vehicle and the use and maintenance of battery pack.This paper first introduces the application background of LiFePO_4 battery in pure electric vehicle, and its electrochemical principle and working characteristics, and analyzes many factors that affect the performance of the battery.Then the conventional battery model of LiFePO_4 battery is analyzed and compared, and the battery model used in this paper is selected.In view of the fact that the SOC estimation of LiFePO_4 cells is greatly affected by ambient temperature, a new modeling method for LiFePO_4 cells is proposed based on the Nernst electrochemical equation.The experimental data were applied to the design of experimental design of experimental materials in statistical method. A more accurate model of battery internal resistance was obtained by measuring less data. The other parameters in the model could be fitted by continuously varying temperature and SOC at different time points of the battery.In order to realize the real-time estimation of the whole battery model.Finally, the extended Kalman filter extended Kalman filter (EKF) algorithm is introduced, and the state space equation of the LiFePO_4 battery based on the improved Nernst model is analyzed. The charging and discharging experiments at different temperatures and different working conditions are carried out in the laboratory.The EKF model of SOC estimation of LiFePO_4 battery is built in MATLAB/Simulink module to simulate and analyze the experimental data, and the dynamic estimation of SOC of LiFePO_4 battery is realized. The error between the improved model and the traditional model in SOC estimation is compared.The results show that the improved Nernst cell model can obtain high accuracy of SOC estimation.
【学位授予单位】：合肥工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM912

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