电动汽车的蓄电池管理系统设计

发布时间：2018-05-29 19:00

本文选题：新能源 + 电动汽车　；参考：《西安工业大学》2014年硕士论文

【摘要】：蓄电池作为电动汽车的主要动力源,其储能能力的不足一直是制约电动汽车发展的重要因素,解决这个问题,除了不断研制出存储容量大的蓄电池以外,如何最大限度的发挥现有蓄电池的使用效率也是解决这个问题关键技术之一。为了最大限度地发挥蓄电池的使用效率,现阶段的研究一般都是从以下几个方面开展的：一、加强保护,防止各种意外对蓄电池造成伤害；二、为蓄电池创造一种合适的工作环境；三、对串联的蓄电池组,加强充电过程中的均衡度控制,以及及早发现群体中存在的受损个体并将其更换;四、对蓄电池的剩余电量(SOC)的准确估测,不仅为蓄电池进行合理的充放电,而且也为电动汽车整车的能量管理提供了必要的依据。将这些功能集中在一起,就组成了现阶段的蓄电池管理系统(BMS),这些功能都是通过VB软件控制实现的,它能最终实现发挥蓄电池的使用效率和寿命、增加电动汽车续驶里程等功效。为了实现上述功能,本项目设计了一个蓄电池管理系统,该系统由信号处理模块、输出驱动模块、工业A/D采集卡、I/0接口卡和一台工业计算机组成。信号处理模块将采集到的蓄电池电压、电流、温度等数据滤波放大后输入到A/D卡,经计算机采集以后,通过计算,决定是否需要实现相应的控制以及完成SOC的计算,控制信号的输出经输出驱动模块作用到对应的设备,实现各种保护功能；对于SOC的计算,为了提高其估算精度,尝试利用Matlab-Simulink工具,建立蓄电池放电整体模型,分别将卡尔曼滤波法和安时积分法与电池模型有效结合,实现对蓄电池SOC的估测的仿真。同时,利用蓄电池端电压与SOC的关系,得到实际结果。实验结果表明,本设计中的蓄电池管理系统基本实现了蓄电池最大化利用,保证了蓄电池安全可靠的运行。通过对比研究,我们发现利用卡尔曼滤波Matlab仿真得到的曲线比安时法计算得到的曲线更接近实际测量结果。从而验证了卡尔曼滤波法相比于其他传统方法更能有效准确的对蓄电池sOC进行估测。
[Abstract]:As the main power source of the electric vehicle, the lack of energy storage capacity has been an important factor restricting the development of electric vehicles. In addition to developing the storage battery with large storage capacity, how to maximize the efficiency of the existing storage battery is also one of the key technologies to solve this problem.
In order to maximize the use efficiency of the battery, the research at the present stage is generally carried out from the following aspects: first, to strengthen the protection, prevent all kinds of accidents from causing damage to the battery; two, to create a suitable working environment for the battery; three, to the battery group in series, to strengthen the balance control during the charging process. Four, the accurate estimation of the residual quantity of the battery (SOC) not only provides a reasonable charge and discharge for the battery, but also provides the necessary basis for the energy management of the electric vehicle. The current storage battery management is formed by concentrating these functions together. System (BMS), these functions are achieved through the control of the VB software. It can eventually realize the efficiency and life of the battery, and increase the driving range of the electric vehicle.
In order to realize the above function, this project has designed a battery management system, which consists of signal processing module, output driver module, industrial A/D acquisition card, I/0 interface card and an industrial computer. The signal processing module amplifies the data filter of accumulator battery voltage, current, temperature and so on into the A/D card and passes through the computer. After collecting, through calculation, it decides whether to realize the corresponding control and to complete the calculation of SOC. The output of the control signal is acted by the output drive module to the corresponding equipment to realize various protection functions. For the calculation of SOC, in order to improve its estimation accuracy, the Matlab-Simulink tool is used to establish the whole model of the battery discharge. The Calman filter method and the time integration method and the battery model are effectively combined to achieve the simulation of the battery SOC estimation. At the same time, the actual results are obtained by using the relationship between the terminal voltage of the battery and the SOC.
The experimental results show that the battery management system in this design basically realizes the maximum utilization of the battery and ensures the safe and reliable operation of the battery. Through the comparative study, we find that the curve obtained by the Calman filter Matlab simulation is more close to the actual measurement result than the curve calculated by the time method. Thus, Calman has been verified. Compared with other traditional methods, the filtering method is more effective and accurate for battery sOC estimation.
【学位授予单位】：西安工业大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：U469.72;TM912

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