【摘要】：近幾年,電動(dòng)汽車憑借其清潔、高效、無污染等優(yōu)點(diǎn)成為城市交通實(shí)現(xiàn)低排放甚至零排放的理想交通工具。電動(dòng)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(dòng)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?jī)?nèi)容主要包括以下幾個(gè)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(duì)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(shí)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(shí)驗(yàn)數(shù)據(jù)對(duì)模型參數(shù)進(jìn)行辨識(shí),并且通過搭建的電池模型對(duì)于參數(shù)辨識(shí)結(jié)果進(jìn)行了驗(yàn)證,采用了遺忘因子遞推最小二乘法對(duì)于模型參數(shù)進(jìn)行在線辨識(shí),結(jié)合離線辨識(shí)結(jié)果對(duì)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(duì)電池的荷電狀態(tài)(SoC)進(jìn)行估算,并且將仿真估算結(jié)果與實(shí)際的測(cè)量結(jié)果進(jìn)行比較分析,在恒流放電工況下驗(yàn)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(zhǎng)安大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：U469.72

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 黃兵鋒;楊正才;傅佳宏;;基于六參數(shù)RC等效電路模型的鋰離子電池SOC估計(jì)[J];重慶交通大學(xué)學(xué)報(bào)(自然科學(xué)版);2015年05期

2 李洪宇;張曉強(qiáng);張衛(wèi)平;;大容量鋰離子電池SOC估算原理及應(yīng)用[J];電源技術(shù);2015年05期

3 么居標(biāo);呂江毅;任小龍;;基于無跡卡爾曼濾波的動(dòng)力電池荷電狀態(tài)估計(jì)[J];電源技術(shù);2014年09期

4 侯俊;朱長(zhǎng)青;閻海峰;高社生;;Sage-Husa隨機(jī)加權(quán)無跡卡爾曼濾波及其在導(dǎo)航中的應(yīng)用[J];導(dǎo)航定位學(xué)報(bào);2014年01期

5 魏克新;陳峭巖;;基于自適應(yīng)無跡卡爾曼濾波算法的鋰離子動(dòng)力電池狀態(tài)估計(jì)[J];中國(guó)電機(jī)工程學(xué)報(bào);2014年03期

6 孫楓;唐李軍;;Cubature卡爾曼濾波與Unscented卡爾曼濾波估計(jì)精度比較[J];控制與決策;2013年02期

7 熊瑞;何洪文;許永莉;何銀;;電動(dòng)汽車用動(dòng)力電池組建模和參數(shù)辨識(shí)方法[J];吉林大學(xué)學(xué)報(bào)(工學(xué)版);2012年04期

8 熊瑞;孫逢春;何洪文;;自適應(yīng)卡爾曼濾波器在車用鋰離子動(dòng)力電池SOC估計(jì)上的應(yīng)用[J];高技術(shù)通訊;2012年02期

9 熊瑞;何洪文;丁銀;;HEV用鋰離子電池動(dòng)態(tài)模型參數(shù)辨識(shí)方法研究[J];電力電子技術(shù);2011年04期

10 時(shí)瑋;姜久春;李索宇;賈容達(dá);;磷酸鐵鋰電池SOC估算方法研究[J];電子測(cè)量與儀器學(xué)報(bào);2010年08期

，

本文編號(hào)：2319778