锂离子电池组数据采集及管理系统研究与设计

发布时间：2018-05-15 10:02

本文选题：锂离子电池组 + 数据采集　；参考：《江西理工大学》2017年硕士论文

【摘要】：在新能源技术高速发展的今天,锂离子电池因其显著的优势,广泛应用于纯电动汽车。而电池作为纯电动汽车的重要核心部分,应对其进行数据采集与管理。优秀的管理可以延长电池使用寿命,同时保障人身安全。电池管理系统就是针对电池成组过程中的使用情况进行科学有效的管理,保护与控制电池,使其发挥最佳作用。本文首先研究电池等效模型,分析电池外特性对电池性能的影响。重点研究电池管理系统的关键技术,并就数据采集与电池管理进行相应的研究分析。根据采集到的数据,进行处理后进行系统的相应管理。同时从安全角度考虑电池组热管理以及绝缘检测技术的实现与研究设计。此外针对电池成组出现的差异性,采取以SOC作为变量的均衡方法进行去差异性处理。系统硬件电路采用模块化结构,将相应子功能隔离开来,同时与电池组隔离,使得子模块电路不相互间影响,在一定程度上提高了采集精度,同时方便电路的更换与故障检测。配套的软件部分同样采用模块化结构,系统只需调用各子程序就可实现对电池相关参数的采集与调用。其次在实现电池组管理系统的基本功能后,研究分析电池SOC和SOH的估算与预测。电池荷电状态(SOC)、健康状态(SOH)作为电池一致性和寿命的主要状态参数,必须准确的估算与预测。采用针对单体电池的灰色模型估算与预测电池SOC。在此基础上分析了传统电池组SOC估算方法中存在的不足,采用均衡过后的minSOC作为电池组SOC,降低估算误差,提高电池一致性。研究分析电池SOH的定义和估算方法后,以电池容量比作为SOH的识别参数,采用数据融合驱动PHM估算与预测SOH。该方法先根据电池的经验参数模型,找寻电池容量变化的关系式,然后通过tsPSO算法辨识模型中的参数,进而得出电池容量变化关系式,由此分析得出电池健康状态。最后通过实验和仿真测试验证系统中的功能,结果显示本文设计的电路和估算模型的可行性和有效性。
[Abstract]:With the rapid development of new energy technology, lithium ion batteries are widely used in pure electric vehicles because of their remarkable advantages. As an important part of pure electric vehicle, battery should be collected and managed. Excellent management can prolong battery life and ensure personal safety. Battery management system is to scientifically and effectively manage, protect and control the battery, and make it play the best role. In this paper, the equivalent model of the battery is studied, and the effect of the external characteristics of the battery on the performance of the battery is analyzed. The key technology of battery management system is studied, and the data acquisition and battery management are analyzed. According to the collected data, the system is managed after processing. At the same time, from the point of view of safety, the thermal management of battery pack and the realization and design of insulation detection technology are considered. In addition, the SOC equalization method is adopted to deal with the difference of battery group. The hardware circuit of the system adopts modularized structure to isolate the corresponding sub-functions and separate them from the battery pack at the same time, which makes the sub-module circuit not affect each other, which improves the acquisition accuracy to a certain extent, and facilitates the replacement of the circuit and the fault detection at the same time. The supporting software part also adopts the modularization structure, the system only need to call each subprogram to realize the collection and the call to the battery related parameters. Secondly, after realizing the basic function of battery management system, the estimation and prediction of cell SOC and SOH are studied and analyzed. As the main state parameter of battery consistency and lifetime, it must be estimated and predicted accurately. A grey model for single cell was used to estimate and predict the SOC of the battery. On this basis, the shortcomings of the traditional SOC estimation methods are analyzed. The equalized minSOC is used as the battery SOC to reduce the estimation error and improve the cell consistency. After analyzing the definition and estimation method of battery SOH, the battery capacity ratio is used as the identification parameter of SOH, and the data fusion is used to drive the PHM estimation and prediction. Based on the empirical parameter model of the battery, the relationship of the battery capacity change is found, and then the parameters in the model are identified by tsPSO algorithm, and then the relationship of battery capacity change is obtained, and the battery health state is obtained by analyzing the relationship between the parameters of the model and the battery capacity. Finally, the functions of the system are verified by experiments and simulation tests. The results show the feasibility and validity of the circuit and the estimation model designed in this paper.
【学位授予单位】：江西理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM912;TP311.52

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