發(fā)布時間：2018-11-09 07:39

【摘要】：近幾年,電動汽車憑借其清潔、高效、無污染等優(yōu)點成為城市交通實現(xiàn)低排放甚至零排放的理想交通工具。電動汽車所搭載的電池組性能的好壞是影響整車的續(xù)航里程、加速性能和制動能量回收的效率等性能的直接因素。鋰電池荷電狀態(tài)(State of Charge,SoC)估算是電池管理系統(tǒng)(Battery Management System,BMS)的關(guān)鍵功能,是電池的使用可靠性以及安全性的關(guān)鍵。本文研究內(nèi)容主要包括以下幾個方面:首先,本文介紹了鋰離子電池的結(jié)構(gòu)和工作原理,闡述了荷電狀態(tài)(SoC)的定義,比較分析了幾種常用的估算方法,然后給出了本文采用的估算方法——卡爾曼濾波法。然后,為了提高SoC的估算精度,并且準(zhǔn)確的對電池的特性以及工作過程中的狀態(tài)及行為做出仿真和模擬。在便于硬件實現(xiàn)的前提下,建立了Thevenin模型和二階RC電路相結(jié)合的電池模型,并且采用電池HPPC實驗數(shù)據(jù)對模型參數(shù)進行辨識,并且通過搭建的電池模型對于參數(shù)辨識結(jié)果進行了驗證,采用了遺忘因子遞推最小二乘法對于模型參數(shù)進行在線辨識,結(jié)合離線辨識結(jié)果對照分析可知,此方法可有效地提高模型參數(shù)的準(zhǔn)確性。最后,采用無跡卡爾曼濾波算法以及自適應(yīng)匹配的噪聲對電池的荷電狀態(tài)(SoC)進行估算,并且將仿真估算結(jié)果與實際的測量結(jié)果進行比較分析,在恒流放電工況下驗證了本文估算方法的有效性。
[Abstract]:In recent years, electric vehicles (EVs) have become the ideal vehicle to realize low emission and zero emission due to their advantages of cleanliness, high efficiency, no pollution and so on. The performance of battery pack on electric vehicle is a direct factor affecting the performance of the whole vehicle, such as the range of the vehicle, the acceleration performance and the efficiency of braking energy recovery. The estimation of charge state (State of Charge,SoC is the key function of the battery management system (Battery Management System,BMS) and the key to the reliability and safety of the battery. The main contents of this paper are as follows: firstly, the structure and working principle of lithium ion battery are introduced, the definition of charged state (SoC) is expounded, and several commonly used estimation methods are compared and analyzed. Then the Kalman filter method, which is used in this paper, is given. Then, in order to improve the accuracy of SoC estimation, and to accurately simulate and simulate the characteristics of the battery and the state and behavior in the working process. On the premise of easy hardware implementation, the battery model combining Thevenin model with second-order RC circuit is established, and the parameters of the model are identified by using the battery HPPC experimental data. The battery model is used to verify the parameter identification results, and the forgetting factor recursive least square method is used to identify the model parameters online. The results of off-line identification are compared and analyzed. This method can effectively improve the accuracy of model parameters. Finally, the unscented Kalman filter algorithm and adaptive matching noise are used to estimate the charge state (SoC) of the battery, and the simulation results are compared with the actual measurement results. The effectiveness of the proposed method is verified under constant current discharge conditions.
【學(xué)位授予單位】：長安大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：U469.72

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