动力电池SOC测量不确定度评定方法的研究

发布时间：2018-03-10 11:28

本文选题：不确定度　切入点：高斯过程　出处：《哈尔滨工业大学》2017年硕士论文　论文类型：学位论文

【摘要】：电池管理系统作为电动汽车的核心部件之一,它的性能直接关系到电动汽车的使用安全与使用寿命。随着电动汽车的普及,电池管理系统的检测成为当前电动汽车行业发展需要解决的问题。动力电池组荷电状态SOC(state of charge)的估算精度成为电池管理系统检测的核心内容,本文主要针对电池管理系统SOC的检测,提出了评定动力电池SOC测量不确定度的方法。本文通过分析锂电池工作原理及充放电数据特点,提出了基于高斯过程的锂电池充放电数据处理方法,与最小二乘拟合和高斯拟合相比,充放电曲线的预测精度更高。针对标准QC/T897-2011《电动汽车用电池管理系统技术条件》中SOC检测项目规程,设计了电池电量测量不确定度评定的方案,根据电量测量的特点,建立基于高斯过程的电池充放电模型,分析影响电量测量模型的不确定因素,确定电量测量模型中输入量的概率密度函数,利用蒙特卡洛法评定出不同充放电模式下电量测量的不确定度。分析电池老化对放电电量产生的影响,提出了利用高斯过程回归算法估算电池健康状态(SOH)的方法,采用不同的核函数进行SOH估算,并根据SOH的估算方法提出容量估算的不确定性,进行可用容量的测试,评定出可用容量不确定度。设计了基于虚拟仪器测量技术的SOC测试系统,搭建了硬件测试平台,同时选用NI公司的Labwindows/CVI设计其软件平台。用该测试系统来完成对电池管理系统SOC估算精度的检测实验,SOC测试系统搭建完成后,进行电池充放电数据的采样,以及不同工况下的SOC测试,最后根据实验数据评定出SOC测量不确定度。
[Abstract]:As one of the core components of electric vehicles, battery management system has a direct bearing on the safety and service life of electric vehicles. The detection of battery management system has become a problem that needs to be solved in the development of electric vehicle industry. The estimation accuracy of SOC(state of charge status of power battery pack becomes the core content of battery management system detection. This paper presents a method to evaluate the uncertainty of power battery SOC measurement based on the detection of battery management system (SOC). The principle of lithium battery and the characteristics of charging and discharging data are analyzed in this paper. A charging and discharging data processing method for lithium battery based on Gao Si process is proposed, which is compared with least square fitting and Gao Si fitting. The prediction accuracy of charge-discharge curve is higher. According to the SOC test item specification in the standard QC/T897-2011 "Technical condition of Battery Management system for Electric vehicles", a scheme to evaluate the uncertainty of battery quantity measurement is designed, and according to the characteristics of electric quantity measurement, a method is designed to evaluate the uncertainty of battery quantity measurement. The battery charge and discharge model based on Gao Si process is established. The uncertain factors affecting the electric quantity measurement model are analyzed, and the probability density function of the input quantity in the electric quantity measurement model is determined. The uncertainty of electric quantity measurement under different charging and discharging modes was evaluated by Monte Carlo method. The effect of battery aging on discharge quantity was analyzed, and a method of estimating battery healthy state by Gao Si process regression algorithm was put forward. Different kernel functions are used to estimate SOH, and uncertainty of capacity estimation is proposed according to the estimation method of SOH. The uncertainty of available capacity is evaluated by testing the available capacity. A SOC test system based on virtual instrument measurement technology is designed. The hardware test platform is built, and the software platform is designed by Labwindows/CVI of NI Company. The battery charge and discharge data are sampled after the test system is built to test the accuracy of SOC estimation of battery management system. Finally, the uncertainty of SOC measurement is evaluated according to the experimental data.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM912

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