电动汽车电池管理系统SOC估算及均衡控制研究

发布时间：2018-05-15 01:03

本文选题：电动汽车 + 电池管理系统　；参考：《昆明理工大学》2017年硕士论文

【摘要】：近年来,全球环境污染日益加重,国内外雾霾天气频发,严重影响了人们的生活质量、威胁着人们的生命健康安全。因此,科研机构、政府部门都开始将目光投向环境友好的新型交通工具——新能源电动汽车。新能源电动汽车在运行过程中,其动力电池组是呈现非线性特点,动力电池组中的每一个单电池之间具有不一致性。所以,对电池组的剩余电量(State of Charge,SOC)估算及其均衡控制研究具有实际应用价值。本文采用松下18650特斯拉电动汽车同型号电池开展研究工作。本文首先阐述了电池管理系统(Battery Management System,BMS)对电池组的剩余电量估算及均衡控制的重要意义。为了能够满足新能源电动汽车的动力需求,需要将许多单电池串联或并联构成一个电池组,因为单电池具有不一致性,在使用过程中,对电动汽车动力电池的整体性能和安全性带来严重影响,针对这一问题,本文对锂电池不一致性进行分析,对比充电均衡与放电均衡控制两种均衡控制管理方法,并用MATLAB软件对其进行仿真。结果表明:当剩余电量低于10%的时候,放电均衡好于充电均衡;其次,对于估算电池组荷电状态,本文采用BP神经网络算法(Back-Propagation),通过实验获样本数据,分析它们之间的数学关系,利用MATLAB软件训练实验所获得的数据,并用训练好的模型来估算电池的SOC,其最大误差小于4%;最后,对双电源管理系统的整体方案进行设计。
[Abstract]:In recent years, the global environmental pollution is becoming more and more serious, and the frequent weather of haze at home and abroad has seriously affected people's quality of life and threatened people's health and safety. As a result, research institutions and government departments are beginning to focus on new environmentally-friendly vehicles-new energy electric vehicles. In the operation of new energy electric vehicle, the power battery pack is nonlinear, and there is inconsistency between each single cell in the power battery pack. Therefore, the research on the estimation of the state of charge SOC and its equalization control of the battery pack has practical application value. In this paper, the same type battery of Panasonic 18650 Tesla electric vehicle is used to carry out research work. In this paper, the importance of Battery Management system to the estimation and equalization control of the battery pack is described. In order to meet the power needs of new energy electric vehicles, many single cells need to be connected in series or in parallel to form a battery pack, because the single cell is inconsistent and in use, This paper analyzes the inconsistency of lithium battery and compares the two equalization control methods: charging equalization and discharge equalization control. It is simulated by MATLAB software. The results show that the discharge equalization is better than the charging equalization when the remaining charge is less than 10%. Secondly, the BP neural network algorithm is used to estimate the charging state of the battery pack, and the sample data are obtained by experiments, and the mathematical relations between them are analyzed. The data obtained from the MATLAB software training experiment are used to estimate the SOC of the battery with the trained model, and the maximum error is less than 4. Finally, the overall scheme of the dual power management system is designed.
【学位授予单位】：昆明理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM912;U469.72

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